<table><caption>Table 6: Maximum compression ratio achieved for different weather conditions.</caption><thead><tr><th>Weather condition</th><th>Baseline Pedestrian Detection Accuracy (no compression)</th><th>Dynamic EBLC Framework Pedestrian Detection Accuracy</th><th>Improvement in Pedestrian Detection using Dynamic EBLC Framework</th><th>Maximum Constant Rate Factor (CRF) (or Minimum PSNR)</th><th>Required Bandwidth (MBits/sec)</th><th>Bandwidth Reduction</th></tr></thead><tbody><tr><td>Normal</td><td>98%</td><td>97%</td><td>-1%</td><td>30 (41dB)</td><td>0.53</td><td>18x</td></tr><tr><td>Light Dark</td><td>93%</td><td>97%</td><td>4%</td><td>30 (41dB)</td><td>0.68</td><td>14x</td></tr><tr><td>Medium Dark</td><td>92%</td><td>97%</td><td>5%</td><td>20 (43dB)</td><td>1.01</td><td>9.5x</td></tr><tr><td>High Dark</td><td>90%</td><td>97%</td><td>7%</td><td>10 (56dB)</td><td>4.05</td><td>2.5x</td></tr><tr><td>Light Rain</td><td>89%</td><td>97%</td><td>8%</td><td>10 (56dB)</td><td>5.15</td><td>1.5x</td></tr><tr><td>Medium Rain</td><td>88%</td><td>97%</td><td>9%</td><td>0</td><td>9.82</td><td>0x</td></tr><tr><td>Heavy Rain</td><td>83%</td><td>97%</td><td>14%</td><td>0</td><td>9.82</td><td>0x</td></tr></tbody></table>

layers can be efficiently compressed without significant accuracy degradation.

In (3), we aim to implement compression techniques for both training data and checkpoint data. We observed that in numerous AI applications for scientific purposes, the input data often surpasses the scale of traditional AI image processing. As a result, a substantial portion of the model's training time is allocated to loading the extensive input data. Offering frameworks that efficiently reduce the size of input data without compromising convergence speed and accuracy can effectively reduce the expenses associated with transferring large input data.

# 7. Optimizing Compression on the GPU

## 7.1. Introduction

Recent advancements in scientific computing, notably marked by the transition to exascale (over 1e18 FLOPS) computing [1], impose data challenges in two ways: the sheer volume of data and the rapid rate of data generation. Data compression has increasingly been researched to meet these challenges. Notably, a lossy compressor designed for scientific data reduction should fulfill three primary design goals: high-fidelity preservation in accordance with specific domain science constraints, a high compression ratio, and robust processing capability. On the other hand, GPUs have demonstrated significantly higher data processing capabilities than CPUs have in modern heterogeneous computing systems, making them well suited to address data generation issues by conducting in situ reduction.

SZ is a state-of-the-art error-bounded lossy compression framework for scientific data, meeting the requirements of high-fidelity preservation and a high compression ratio. However, as shown in prior work [36, 139], SZ suffers from (1) low single-core compression and decompression speeds at hundreds of megabytes per second and (2) limited multicore performance. Therefore, adopting a data-parallel programming model and leveraging GPUs can be an attractive solution to keep pace with the high data production rate. Today, GPUs contribute significantly to compute capabilities thanks to a mature ecosystem; their high programmability, as opposed to other heterogeneous hardware platforms such as FPGAs [144], enables easier parallelization.

Parallelizing SZ requires addressing two main issues. First, a loop-carried data dependency exists among the loop iterations

of SZ's prediction and error quantization step—not until iteration *i* is finished can iteration (*i* + 1) start. This tight data dependency incurs extraordinarily costly synchronization to ensure correctness.<fnref n="5" /> Second, during Huffman coding, a variable-length encoding step in the SZ algorithm, the uneven data access pattern for grouped threads hurts performance by breaking the rigid SIMD parallel pattern (known as warp divergence).

The challenge centers on unleashing GPUs' potential by identifying parallelisms in the existing SZ algorithm and balancing the three primary design goals—achieving a high compression ratio, high throughput, and high data quality. Therefore, over the past five years, JLESC researchers have proposed a series of works to address the low-throughput issue and expand the usability of SZ compression. A summary of the proposed GPU-based compressors is given in Table 6.

<table><caption>Table 7: Comparison of cuSZ, FZ-GPU, cuSZp, and cuSZ-I.</caption><thead><tr><th>Work</th><th>Generality</th><th>Speed</th><th>Quality</th><th>Comp. Ratio</th></tr></thead><tbody><tr><td>cuSZ(+)</td><td>general-purpose</td><td>baseline</td><td>baseline</td><td>high</td></tr><tr><td>FZ-GPU</td><td>general-purpose</td><td>high</td><td>baseline</td><td>balanced medium</td></tr><tr><td>cuSZp</td><td>special (1D only)</td><td>high (end-to-end)</td><td>baseline</td><td>medium</td></tr><tr><td>cuSZ-I</td><td>special (3D only)</td><td>medium</td><td>highest</td><td>highest</td></tr></tbody></table>

## 7.2. The State of the Art

Given the surge in GPU-based HPC systems and their applications, there has been a proliferation of GPU-based compression methods for scientific data. Notable examples include cuZFP [32], MGARD [55], and nvCOMP's Bitcomp [115]. These GPU-based compressors offer significantly higher compression throughputs compared with CPU-based compressors.

cuZFP, employing a transformation-based approach, allows users to specify their desired bitrate, representing the average number of bits per value after compression. In contrast, Bitcomp (a quantization-based compressor) and MGARD (a multigrid-based compressor) enable users to set the maximum permissible error. Notably, cuZFP in fixed-rate mode consistently delivers

<footnote n="5">⁵Preliminary results show 30 to 40 GB/s for the major kernel on V100, which is approximately 1/20 of memory copy speed.</footnote>
ers superior compression throughput, while MGARD in error-
bounded mode tends to achieve a higher compression ratio. Al-
though the principles underlying nvCOMP's Bitcomp remain
undisclosed because of its closed-source nature, our evaluations
indicate that it employs prediction processes and consistently
attains a relatively high compression ratio while maintaining
commendable compression throughput.

However, these GPU-based lossy compressors face certain
challenges that warrant resolution. For instance, cuZFP exclu-
sively supports the fixed-rate mode, restricting its applicability
in scenarios necessitating strict control over the error bound in
reconstructed data compared with the original data. MGARD's
GPU version, on the other hand, necessitates CPU involvement
in the compression process, resulting in performance bottle-
necks attributed to frequent data transfers between the CPU and
GPU. MGARD's GPU version, on the other hand, integrates the
decomposition and Huffman encoding process into the pipeline,
which results in the relatively low throughput (single-digit GB/s
[54], which is impractical to fit the HPC real-time data pro-
cessing requirements. Additionally, nvCOMP's Bitcomp, being
proprietary, lacks transparency in its operation, with NVIDIA's
provided APIs being overly high-level, thereby limiting its adapt-
ability in practical applications. In light of these limitations, our
objective is to develop a new series of GPU-based compressors
to address these issues comprehensively.

7.3. Proposed Design

We now introduce our proposed GPU compressors.

cuSZ Framework. The cuSZ work The work cuSZ [145] and cuSZ+ [143] is the first error-bounded lossy compressor on GPUs, adapting the SZ framework for scientific data. The work features a practical framework on GPUs to achieve high throughput. The basic framework contains the fully parallelized Lorenzo-based prediction-quantization and coarse-grain multi-byte symbol-enumerating Huffman encoding, with a further lossless encoding. The lossless encoding can be dropped for the subsequent high throughput. The key novelty of cuSZ/cuSZ+, on top of the practice on GPUs, is the demonstration that the achieved capability originates from both algorithm design and the performance optimization in a hardware-software co-design manner, which are detailed in cuSZ and cuSZ+ work. The key novelty of cuSZ/cuSZ+ lies in demonstrating that the achieved capability originates from both algorithm design and the performance optimization, considering the practical utilization of GPU architecture, which is detailed in cuSZ[145] and cuSZ+ [143] work.

FZ-GPU. The FZ-GPU compressor provides an alternative com-
pression pipeline to broaden its usability based on its data pro-
cessing capability. Based on the existing prediction-quantization
that generates the error-quantization code from the original cuSZ
framework, a novel compression pipeline is proposed by incor-
porating GPU-based bitshuffle, a newly designed high-throughput-
oriented lossless coding technique. The prediction and bitshuf-
fle operations result in substantial data sparsity, so FZ-GPU in-
corporates a fast-scan GPU kernel. This kernel efficiently skips
data blocks containing all-zero values and selectively writes

non-all-zero blocks, aligning well with the compression pipeline’s
requirements. Compared with cuSZ/cuSZ+, FZ-GPU utilizes a
faster lossless encoder in the pipeline and maintains balanced
compression ratios achieves a gracefully degraded compression
ratio but higher throughput. Consequently, it manages to achieve
a notably higher host-device data transfer rate performance com-
pared with cuSZ/cuSZ+.

cuSZp. cuSZp represents a GPU-based error-bounded lossy com-
pression approach engineered to optimize end-to-end through-
put. This method is constructed on a series of lightweight bit-
level operations implemented within GPU registers, comple-
mented by finely tuned global synchronization mechanisms. This
concerted strategy culminates in the achievement of remark-
ably swift throughput. The methodology underlying cuSZp
can be briefly outlined through four key steps. (1) Quantiza-
tion and prediction: In cuSZp, the quantization and prediction
stages resemble those of other SZ-based compressors. Quan-
tization introduces the sole lossy element within the compres-
sion pipeline, introducing errors while concurrently enhancing
data redundancy to facilitate greater compressibility. The pre-
diction stage serves to decorrelate input data based on dimen-
sional patterns. (2) Fixed-length encoding: This step entails
preserving a fixed-length bit representation, further augmenting
compression ratios. (3) Global synchronization: In GPU-based
lossy compression methodologies global synchronization is es-
sential. In cuSZp, this process leverages a high-performance
prefix sum technique for local offset calculations and a global
lock mechanism for global offset computations. (4) Block bit-
shuffle: cuSZp harnesses the bitshuffle technique to introduce
additional redundancy at the bit level, thereby enhancing the
compressibility of processed data.

cuSZ-I. Based on the cuSZframework and CPU QoZ[106], cuSZ-I is using cubic spline interpolation in place of the generic Lorenzo prediction to achieve a higher compression ratio and improve the data reconstruction quality. Also, with more accurate prediction before the lossless encoding stage, the error-quantization codes are much more condensed so that higher data compressibility can be better utilized in the lossless encoding stage. With the original Huffman encoding, the proposed cubic spline interpolation shows its advantage in the compression ratio, even with the data-representation-restricted Huffman encoding.<fnref n="6" /> In addition, by appending another stage of lossless encoding, cuSZ-I can steadily achieve an even higher compression ratio, outperforming any other known GPU-based compressors. This work profiled the best-performing lossless encoding—in this case, the proprietary Bitcomp from NVIDIA's nvCOMP collection. Another dictionary-like lossless encoding can also leverage the found compressibility. We note that in the data-movement use scenario, cuSZ-I does not provide the best-in-class processing speed because of its significantly higher compression ratio; however, it shows a notable advantage in the equivalent data-transfer rate. Also, based on its improved data quality, rate-distortion or transfer-rate distortion profiling demonstrates that cuSZ-I could

<footnote n="6">⁶Huffman encoding needs at least 1 bit to represent each quantization code.</footnote>
ers superior compression throughput, while MGARD in error-
bounded mode tends to achieve a higher compression ratio. Al-
though the principles underlying nvCOMP's Bitcomp remain
undisclosed because of its closed-source nature, our evaluations
indicate that it employs prediction processes and consistently
attains a relatively high compression ratio while maintaining
commendable compression throughput.

However, these GPU-based lossy compressors face certain
challenges that warrant resolution. For instance, cuZFP exclu-
sively supports the fixed-rate mode, restricting its applicability
in scenarios necessitating strict control over the error bound in
reconstructed data compared with the original data. MGARD's
GPU version, on the other hand, necessitates CPU involvement
in the compression process, resulting in performance bottle-
necks attributed to frequent data transfers between the CPU and
GPU. MGARD's GPU version, on the other hand, integrates the
decomposition and Huffman encoding process into the pipeline,
which results in the relatively low throughput (single-digit GB/s
[54], which is impractical to fit the HPC real-time data pro-
cessing requirements. Additionally, nvCOMP's Bitcomp, being
proprietary, lacks transparency in its operation, with NVIDIA's
provided APIs being overly high-level, thereby limiting its adapt-
ability in practical applications. In light of these limitations, our
objective is to develop a new series of GPU-based compressors
to address these issues comprehensively.

7.3. Proposed Design

We now introduce our proposed GPU compressors.

cuSZ Framework. The cuSZ work The work cuSZ [145] and cuSZ+ [143] is the first error-bounded lossy compressor on GPUs, adapting the SZ framework for scientific data. The work features a practical framework on GPUs to achieve high throughput. The basic framework contains the fully parallelized Lorenzo-based prediction-quantization and coarse-grain multi-byte symbol-enumerating Huffman encoding, with a further lossless encoding. The lossless encoding can be dropped for the subsequent high throughput. The key novelty of cuSZ/cuSZ+, on top of the practice on GPUs, is the demonstration that the achieved capability originates from both algorithm design and the performance optimization in a hardware-software co-design manner, which are detailed in cuSZ and cuSZ+ work. The key novelty of cuSZ/cuSZ+ lies in demonstrating that the achieved capability originates from both algorithm design and the performance optimization, considering the practical utilization of GPU architecture, which is detailed in cuSZ[145] and cuSZ+ [143] work.

FZ-GPU. The FZ-GPU compressor provides an alternative com-
pression pipeline to broaden its usability based on its data pro-
cessing capability. Based on the existing prediction-quantization
that generates the error-quantization code from the original cuSZ
framework, a novel compression pipeline is proposed by incor-
porating GPU-based bitshuffle, a newly designed high-throughput-
oriented lossless coding technique. The prediction and bitshuf-
fle operations result in substantial data sparsity, so FZ-GPU in-
corporates a fast-scan GPU kernel. This kernel efficiently skips
data blocks containing all-zero values and selectively writes

non-all-zero blocks, aligning well with the compression pipeline’s
requirements. Compared with cuSZ/cuSZ+, FZ-GPU utilizes a
faster lossless encoder in the pipeline and maintains balanced
compression ratios achieves a gracefully degraded compression
ratio but higher throughput. Consequently, it manages to achieve
a notably higher host-device data transfer rate performance com-
pared with cuSZ/cuSZ+.

cuSZp. cuSZp represents a GPU-based error-bounded lossy com-
pression approach engineered to optimize end-to-end through-
put. This method is constructed on a series of lightweight bit-
level operations implemented within GPU registers, comple-
mented by finely tuned global synchronization mechanisms. This
concerted strategy culminates in the achievement of remark-
ably swift throughput. The methodology underlying cuSZp
can be briefly outlined through four key steps. (1) Quantiza-
tion and prediction: In cuSZp, the quantization and prediction
stages resemble those of other SZ-based compressors. Quan-
tization introduces the sole lossy element within the compres-
sion pipeline, introducing errors while concurrently enhancing
data redundancy to facilitate greater compressibility. The pre-
diction stage serves to decorrelate input data based on dimen-
sional patterns. (2) Fixed-length encoding: This step entails
preserving a fixed-length bit representation, further augmenting
compression ratios. (3) Global synchronization: In GPU-based
lossy compression methodologies global synchronization is es-
sential. In cuSZp, this process leverages a high-performance
prefix sum technique for local offset calculations and a global
lock mechanism for global offset computations. (4) Block bit-
shuffle: cuSZp harnesses the bitshuffle technique to introduce
additional redundancy at the bit level, thereby enhancing the
compressibility of processed data.

cuSZ-I. Based on the cuSZframework and CPU QoZ[106], cuSZ-I is using cubic spline interpolation in place of the generic Lorenzo prediction to achieve a higher compression ratio and improve the data reconstruction quality. Also, with more accurate prediction before the lossless encoding stage, the error-quantization codes are much more condensed so that higher data compressibility can be better utilized in the lossless encoding stage. With the original Huffman encoding, the proposed cubic spline interpolation shows its advantage in the compression ratio, even with the data-representation-restricted Huffman encoding.<fnref n="6" /> In addition, by appending another stage of lossless encoding, cuSZ-I can steadily achieve an even higher compression ratio, outperforming any other known GPU-based compressors. This work profiled the best-performing lossless encoding—in this case, the proprietary Bitcomp from NVIDIA's nvCOMP collection. Another dictionary-like lossless encoding can also leverage the found compressibility. We note that in the data-movement use scenario, cuSZ-I does not provide the best-in-class processing speed because of its significantly higher compression ratio; however, it shows a notable advantage in the equivalent data-transfer rate. Also, based on its improved data quality, rate-distortion or transfer-rate distortion profiling demonstrates that cuSZ-I could

<footnote n="6">⁶Huffman encoding needs at least 1 bit to represent each quantization code.</footnote>
be the preferable solution based on the knowledge of data in specific scientific applications.

## 7.4. Performance

In our evaluation, we use the representative scientific dataset to demonstrate the capability of the above-mentioned GPU-based compressors, namely JHTDB, Miranda, NYX, QMC-Pack, RTM, and S3D (detailed in Table 8). They have been widely used in prior works [11, 14, 19, 27] and are representative in the production-level simulations. Most of them are open data from the Scientific Data Reduction Benchmarks suite [34]. Also, we employ a set of metrics to assess the performance of the compression techniques. These metrics encompass the following aspects:

1. **Compression ratio:** This metric is a fundamental measure widely used in compression research. It quantifies the ratio between the size of the original data and the size of the compressed data. Higher compression ratios indicate a more efficient aggregation of information in comparison with the original data. We note that in our evaluations, we specifically calculate the bitrate, which represents the average number of bits required per value after compression. Given that our evaluation datasets consist of single-precision floating-point data, the bitrate is computed as 32 (bits) divided by the compression ratio. We utilize this metric to evaluate how each compressor performs in terms of data quality at a specific bitrate. Figure 22 effectively displays the compression ratio as bitrate.

2. **Distortion:** Assessing distortion is essential for evaluating the quality of data reconstruction in lossy compression. In our work, we primarily utilize the peak signal-to-noise ratio to quantify the quality of distortion. Similar to prior research, we construct rate-distortion curves to facilitate a fair comparison among different compressors and their various compression modes. These curves enable a comparison of distortion quality at equivalent bitrates. The PSNR is depicted in a rate-distortion map, as illustrated in Figure 22.

3. **Compression and decompression throughput:** This metric gauges the rate at which a compressor can process data within a unit of time. It represents a key advantage of utilizing GPU-based compression over CPU-based alternatives. Figure 21 presents the result of throughput for both compression and decompression processes.

These evaluation metrics collectively provide a comprehensive assessment of the compression techniques' performance, encompassing compression efficiency, data quality, and processing speed.

## 7.5. What's Next

**Workflow Optimization.** The recently updated cuSZ-I demonstrates that the prediction-quantization step creates compressibility for subsequent lossless encoding. However, the actual reduction in data size is achieved by our in-house Huffman encoding and Nvidia's proprietary Bitcomp. Therefore, the workflow needs to be optimized in the following ways: (1) replace the proprietary component (e.g., with a customized Finite State Entropy encoder) to benefit the compressor development in an

<table><caption>Table 8: Datasets used in GPU compressor evaluations.</caption><tbody><tr><td>JHTDB</td><td>10 files</td><td>dim: 512×512×512</td><td>total size: 5 GB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> turbulence; the Johns Hopkins Turbulence Database [97] with direct numerical simulation (DNS) turbulence data. We extracted 512³ data blocks of pressure field over time steps.</td></tr><tr><td>Miranda</td><td>7 files</td><td>dim: 256×384×384</td><td>total size: 1 GB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> Large-eddy simulation of multi-component turbulent flows via a radiation hydrodynamics code [114].</td></tr><tr><td>NYX</td><td>6 files</td><td>dim: 512×512×512</td><td>total size: 3.1 GB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> cosmology; cosmological hydrodynamics simulation based on adaptive mesh [10].</td></tr><tr><td>QMCPack</td><td>1 files</td><td>dim: 288×115×69×69</td><td>total size: 612 MB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> quantum; an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids [123].</td></tr><tr><td>RTM</td><td>37 files</td><td>dim: 449×449×235</td><td>total size: 6.5 GB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> seismic wave; reverse time migration for seismic imaging [86].</td></tr><tr><td>S3D</td><td>11 files</td><td>dim: 500×500×500</td><td>total size: 5.1 GB</td></tr><tr><td></td><td colspan="3"><b>Domain:</b> combustion; a simulation dataset of combustion process [?].</td></tr></tbody></table>

end-to-end manner and facilitate deployment; and (2) introduce a compressibility model to further optimize the efficiency of lossless encoding, utilizing the same intermediate data from the prediction-quantization step.

**Multiple Backends.** In-production and upcoming supercomputers are equipped with GPUs from multiple vendors, posing challenges in developing and maintaining GPU compressors for them. With the parallel algorithm in data processing developed, we can adapt compressors, originally based on NVIDIA's CUDA programming model, to other backends. This adaptation may include creating our portability layer or adopting Kokkos.<fnref n="7" /> Furthermore, the presence of multiple backends implies the need to study performance models to maximize the usability of the proposed GPU compressors.

# 8. Scheduling Compressed I/Os

The work [77] described in this section was conducted as part of a collaboration between ANL and Inria. In addition to the coauthors of the present survey this work involved Daoce Wang and Yves Robert.

## 8.1. Introduction

Scientific applications typically use parallel I/O libraries such as the Hierarchical Data Format 5 (HDF5) [142] for reading and storing their data. HDF5 offers dynamically loaded filters [67], including lossless and lossy compression [35], which enable the automatic storage and access of data in compressed formats.

Asynchronous I/O from parallel I/O libraries can help alleviate I/O bottlenecks by overlapping I/O operations with computations, enhancing the end-to-end performance of HPC applications. However, the current implementation of asynchronous

<footnote n="7">⁷Kokkos is a C++ performance portability programming ecosystem that addresses two major concerns regarding multiple backends: parallel execution description and memory abstraction across heterogeneous hardware.</footnote>
be the preferable solution based on the knowledge of data in specific scientific applications.

## 7.4. Performance

In our evaluation, we use the representative scientific dataset to demonstrate the capability of the above-mentioned GPU-based compressors, namely JHTDB, Miranda, NYX, QMC-Pack, RTM, and S3D (detailed in Table 8). They have been widely used in prior works [11, 14, 19, 27] and are representative in the production-level simulations. Most of them are open data from the Scientific Data Reduction Benchmarks suite [34]. Also, we employ a set of metrics to assess the performance of the compression techniques. These metrics encompass the following aspects:

1. **Compression ratio:** This metric is a fundamental measure widely used in compression research. It quantifies the ratio between the size of the original data and the size of the compressed data. Higher compression ratios indicate a more efficient aggregation of information in comparison with the original data. We note that in our evaluations, we specifically calculate the bitrate, which represents the average number of bits required per value after compression. Given that our evaluation datasets consist of single-precision floating-point data, the bitrate is computed as 32 (bits) divided by the compression ratio. We utilize this metric to evaluate how each compressor performs in terms of data quality at a specific bitrate. Figure 22 effectively displays the compression ratio as bitrate.

2. **Distortion:** Assessing distortion is essential for evaluating the quality of data reconstruction in lossy compression. In our work, we primarily utilize the peak signal-to-noise ratio to quantify the quality of distortion. Similar to prior research, we construct rate-distortion curves to facilitate a fair comparison among different compressors and their various compression modes. These curves enable a comparison of distortion quality at equivalent bitrates. The PSNR is depicted in a rate-distortion map, as illustrated in Figure 22.

3. **Compression and decompression throughput:** This metric gauges the rate at which a compressor can process data within a unit of time. It represents a key advantage of utilizing GPU-based compression over CPU-based alternatives. Figure 21 presents the result of throughput for both compression and decompression processes.

These evaluation metrics collectively provide a comprehensive assessment of the compression techniques' performance, encompassing compression efficiency, data quality, and processing speed.

## 7.5. What's Next

**Workflow Optimization.** The recently updated cuSZ-I demonstrates that the prediction-quantization step creates compressibility for subsequent lossless encoding. However, the actual reduction in data size is achieved by our in-house Huffman encoding and Nvidia's proprietary Bitcomp. Therefore, the workflow needs to be optimized in the following ways: (1) replace the proprietary component (e.g., with a customized Finite State Entropy encoder) to benefit the compressor development in an

<table><caption>Table 8: Datasets used in GPU compressor evaluations.</caption><tr><td>JHTDB</td><td>10 files</td><td>dim: 512×512×512</td><td>total size: 5 GB</td></tr><tr><td></td><td>Domain:</td><td>turbulence; the Johns Hopkins Turbulence Database [97] with direct numerical simulation (DNS) turbulence data. We extracted 512³ data blocks of pressure field over time steps.</td><td></td></tr><tr><td>Miranda</td><td>7 files</td><td>dim: 256×384×384</td><td>total size: 1 GB</td></tr><tr><td></td><td>Domain:</td><td>Large-eddy simulation of multi-component turbulent flows via a radiation hydrodynamics code [114].</td><td></td></tr><tr><td>NYX</td><td>6 files</td><td>dim: 512×512×512</td><td>total size: 3.1 GB</td></tr><tr><td></td><td>Domain:</td><td>cosmology; cosmological hydrodynamics simulation based on adaptive mesh [10].</td><td></td></tr><tr><td>QMCPack</td><td>1 files</td><td>dim: 288×115×69×69</td><td>total size: 612 MB</td></tr><tr><td></td><td>Domain:</td><td>quantum; an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids [123].</td><td></td></tr><tr><td>RTM</td><td>37 files</td><td>dim: 449×449×235</td><td>total size: 6.5 GB</td></tr><tr><td></td><td>Domain:</td><td>seismic wave; reverse time migration for seismic imaging [86].</td><td></td></tr><tr><td>S3D</td><td>11 files</td><td>dim: 500×500×500</td><td>total size: 5.1 GB</td></tr><tr><td></td><td>Domain:</td><td>combustion; a simulation dataset of combustion process [?].</td><td></td></tr></table>

end-to-end manner and facilitate deployment; and (2) introduce a compressibility model to further optimize the efficiency of lossless encoding, utilizing the same intermediate data from the prediction-quantization step.

**Multiple Backends.** In-production and upcoming supercomputers are equipped with GPUs from multiple vendors, posing challenges in developing and maintaining GPU compressors for them. With the parallel algorithm in data processing developed, we can adapt compressors, originally based on NVIDIA's CUDA programming model, to other backends. This adaptation may include creating our portability layer or adopting Kokkos.<fnref n="7" /> Furthermore, the presence of multiple backends implies the need to study performance models to maximize the usability of the proposed GPU compressors.

# 8. Scheduling Compressed I/Os

The work [77] described in this section was conducted as part of a collaboration between ANL and Inria. In addition to the coauthors of the present survey this work involved Daoce Wang and Yves Robert.

## 8.1. Introduction

Scientific applications typically use parallel I/O libraries such as the Hierarchical Data Format 5 (HDF5) [142] for reading and storing their data. HDF5 offers dynamically loaded filters [67], including lossless and lossy compression [35], which enable the automatic storage and access of data in compressed formats.

Asynchronous I/O from parallel I/O libraries can help alleviate I/O bottlenecks by overlapping I/O operations with computations, enhancing the end-to-end performance of HPC applications. However, the current implementation of asynchronous

<footnote n="7">⁷Kokkos is a C++ performance portability programming ecosystem that addresses two major concerns regarding multiple backends: parallel execution description and memory abstraction across heterogeneous hardware.</footnote>
<figure><img src="image_23.png" /><figcaption>Figure 21: Compression and decompression throughputs, with all-kernel combined, for GPU compressors in GB/s on NVIDIA A100.</figcaption></figure>

I/O has certain limitations. It supports either (1) asynchronous compression and I/O [81] or (2) asynchronous I/O and computation [165]. In the former scenario, the data writing and computation are still executed sequentially. In the latter scenario, one would miss the opportunity to utilize lossy compression. Moreover, asynchronous I/O typically occurs in a background thread to prevent interference with the main computational thread responsible for running the simulation. Nevertheless, it can still potentially result in performance degradation by introducing interference with other background tasks related to communication within the simulation, especially without proper scheduling [138, 146].

In this section we present a design that enables concurrent execution of compression, I/O, and computation. As scientific applications increasingly adopt GPUs [10, 45, 21], it is crucial to ensure that the GPUs are utilized continuously with minimal interruptions. However, the main and background threads experience periods of idle time while processing computational tasks. Our goal is to harness these idle periods to perform data compression and I/O tasks. To ensure that compression and I/O tasks do not impact overall execution, we carefully schedule compression tasks during the idle times of the corresponding computation thread. Avoiding any delays in existing processing tasks is essential, since such delays would hinder GPU work and potentially result in a slowdown of the entire execution. Our framework includes three key components: fine-grained compression, a compressed data buffer, and a shared tree for Huffman coding. The fine-grained compression algorithm allows for independent compression of data blocks, thereby enhancing task-scheduling efficiency. The compressed data buffer enables the overlapping of compression and I/O tasks. The shared Huffman tree minimizes the overhead associated with building the Huffman tree for compressing small data blocks.

## 8.2. Task Scheduling with Lossy Compression

**Problem formulation.** In most scientific simulations, each process is responsible for handling a segment of the entire data space. We focus on iterative HPC applications. We assume that the execution pattern is highly similar between consecutive

iterations. Therefore, for scheduling one iteration we use the recorded characteristics of the previous iteration (number, starting and ending times of computation tasks, etc.). We note that slight variations in the required task lengths and dates between neighboring iterations may result in some performance degradation, since the proposed task scheduling takes imperfect data as input. As demonstrated in the evaluation section, however, these variations do not significantly impact the effectiveness of the proposed solution.

During each iteration, the HPC application executes a series of tasks on both the CPU and GPU. Usually, each node operates multiple processes, with each process assigned to a dedicated GPU along with a subset of CPU cores. Within each process, there are multiple threads: one associated with the GPU (for computation) and others with the CPU cores (for computation, compression, I/O). Parallel I/O operations are collaboratively executed by the background thread in each process. Our design choice aims to prevent any interference with the GPU's simulation/computation performance. We ignore the GPU and concentrate on the computing, compression, and I/O tasks performed on the main thread and on the background thread by the CPU.

Periodically, either at each iteration or every *l* iterations for some fixed value *l*, the application generates a large amount of data that needs to be written to the storage system. We apply lossy compression to such data. A process is responsible for managing a certain number of data fields, and each of these fields undergoes our proposed fine-grained compression. Task scheduling must ensure that compression tasks do not interfere with computing tasks. In particular, this means that the execution of each computation task should start and end at the same times whether we perform lossy compression or not.

Each compression task is followed by a corresponding I/O task that writes the compressed data to the storage system. We call a *job* the pair formed by a compression task and the corresponding I/O task. We assume that the I/O operations and communications of the application all occur on the background thread. By construction, these tasks are separated from and cannot interfere with either the computing tasks or the compression
<figure><img src="image_23.png" /><figcaption>Figure 21: Compression and decompression throughputs, with all-kernel combined, for GPU compressors in GB/s on NVIDIA A100.</figcaption></figure>

I/O has certain limitations. It supports either (1) asynchronous compression and I/O [81] or (2) asynchronous I/O and computation [165]. In the former scenario, the data writing and computation are still executed sequentially. In the latter scenario, one would miss the opportunity to utilize lossy compression. Moreover, asynchronous I/O typically occurs in a background thread to prevent interference with the main computational thread responsible for running the simulation. Nevertheless, it can still potentially result in performance degradation by introducing interference with other background tasks related to communication within the simulation, especially without proper scheduling [138, 146].

In this section we present a design that enables concurrent execution of compression, I/O, and computation. As scientific applications increasingly adopt GPUs [10, 45, 21], it is crucial to ensure that the GPUs are utilized continuously with minimal interruptions. However, the main and background threads experience periods of idle time while processing computational tasks. Our goal is to harness these idle periods to perform data compression and I/O tasks. To ensure that compression and I/O tasks do not impact overall execution, we carefully schedule compression tasks during the idle times of the corresponding computation thread. Avoiding any delays in existing processing tasks is essential, since such delays would hinder GPU work and potentially result in a slowdown of the entire execution. Our framework includes three key components: fine-grained compression, a compressed data buffer, and a shared tree for Huffman coding. The fine-grained compression algorithm allows for independent compression of data blocks, thereby enhancing task-scheduling efficiency. The compressed data buffer enables the overlapping of compression and I/O tasks. The shared Huffman tree minimizes the overhead associated with building the Huffman tree for compressing small data blocks.

## 8.2. Task Scheduling with Lossy Compression

**Problem formulation.** In most scientific simulations, each process is responsible for handling a segment of the entire data space. We focus on iterative HPC applications. We assume that the execution pattern is highly similar between consecutive

iterations. Therefore, for scheduling one iteration we use the recorded characteristics of the previous iteration (number, starting and ending times of computation tasks, etc.). We note that slight variations in the required task lengths and dates between neighboring iterations may result in some performance degradation, since the proposed task scheduling takes imperfect data as input. As demonstrated in the evaluation section, however, these variations do not significantly impact the effectiveness of the proposed solution.

During each iteration, the HPC application executes a series of tasks on both the CPU and GPU. Usually, each node operates multiple processes, with each process assigned to a dedicated GPU along with a subset of CPU cores. Within each process, there are multiple threads: one associated with the GPU (for computation) and others with the CPU cores (for computation, compression, I/O). Parallel I/O operations are collaboratively executed by the background thread in each process. Our design choice aims to prevent any interference with the GPU's simulation/computation performance. We ignore the GPU and concentrate on the computing, compression, and I/O tasks performed on the main thread and on the background thread by the CPU.

Periodically, either at each iteration or every *l* iterations for some fixed value *l*, the application generates a large amount of data that needs to be written to the storage system. We apply lossy compression to such data. A process is responsible for managing a certain number of data fields, and each of these fields undergoes our proposed fine-grained compression. Task scheduling must ensure that compression tasks do not interfere with computing tasks. In particular, this means that the execution of each computation task should start and end at the same times whether we perform lossy compression or not.

Each compression task is followed by a corresponding I/O task that writes the compressed data to the storage system. We call a *job* the pair formed by a compression task and the corresponding I/O task. We assume that the I/O operations and communications of the application all occur on the background thread. By construction, these tasks are separated from and cannot interfere with either the computing tasks or the compression
<figure><img src="image_24.png" /><figcaption>Figure 22: Rate distortion of the proposal GPU compressors, with an extra lossless encoder enabled to show the best possible rate-distortion performance.</figcaption></figure>

tasks. However, there are core tasks associated with computing that are executed on the background thread. Like the computing tasks, these tasks have known execution intervals that must be respected when scheduling the I/O tasks.

All the scheduling constraints can be summarized as follows. On the compute thread, the compute tasks are immovable obstacles that the compression tasks must avoid. Similarly, on the background thread, the core tasks are immovable obstacles that the I/O tasks must avoid. The two threads are interdependent, since an I/O task cannot start until the corresponding compression task completes. This is the only constraint on the order of compression and I/O tasks, which can otherwise be scheduled in any order compatible with these dependence constraints. The goal of the task-scheduling algorithm is to find an ordering for compression tasks and for I/O tasks to minimize the overall execution time of the current iteration.

**Problem Analysis.** In scheduling theory nomenclature, the problem of scheduling the compression and I/O tasks is a flow-shop problem with two machines: The first one corresponds to the computation thread and the second to the background thread. Our problem is exactly a flow-shop problem with two machines with deterministic unavailability intervals on both machines and with non resumable jobs. The flow-shop problem with two machines, in the absence of unavailability intervals, is optimally solved by Johnson's algorithm [83]. The prob-

lem with non-resumable jobs becomes NP-complete as soon as there is at least one unavailability interval [92]; it becomes non-approximable by any constant factor (unless P=NP) as soon as there are at least two unavailability intervals on one machine [23].

**Adaptation of Johnson's Algorithm.** To schedule tasks, we adapt Johnson's algorithm, which builds an optimal solution in the absence of unavailability intervals. This algorithm works as follows. The jobs are partitioned in two sets $M_1$ and $M_2$ where jobs in $M_1$ are exactly the jobs whose compression task is not longer than its I/O task. Jobs in $M_1$ are sorted by non-decreasing duration of their compression task and jobs in $M_2$ by non-increasing duration of their I/O task. Then Johnson's algorithm executes first all jobs in $M_1$ followed by all jobs in $M_2$, starting each task as soon as possible. The first adaptation is straightforward: we execute tasks in the same order as in Johnson's algorithm, starting each one as soon as possible after already scheduled tasks and while respecting the unavailability intervals. The second adaptation is called backfilling [102], a technique that takes advantage of intervals of idleness. Then a new task, rather than being mandatorily scheduled after the completion of all already scheduled tasks, can be scheduled in an idleness interval if this does not delay the start of any already scheduled task.

## 8.3. Proposed Approach

We now present our framework that deeply integrates lossy compression with parallel I/O libraries, allowing for the overlap of compression and I/O with computation using our task-scheduling algorithms. Figure 23 shows the overview of our proposed framework. The goal is to dump the data generated by one iteration during the next iteration. The runtime design consists of three main components: fine-grained compression, compressed data buffer, and shared tree for Huffman coding.

**Fine-Grained Compression.** The data generated by HPC applications often consists of multiple data fields. For instance, data from a Nyx cosmological simulation may include density, temperature, and velocity information. While it may seem intuitive to compress each data field separately to achieve granularity in compression tasks for our task-scheduling algorithms, most scientific applications have only a few data fields (e.g., 6~12 data fields). This limits the number of compression tasks and I/O tasks, leading to low task-scheduling efficiency. To address this challenge, we propose slicing each data field into smaller data blocks and independently compressing each data block. Fine-grained compression may introduce two potential issues: (1) a degradation in compression ratio compared with compressing the data together and (2) a decrease in compression and I/O throughput. In practice, we use offline profiling to evaluate compression and I/O performance to identify the point at which compression and I/O throughput start to deteriorate with small data block sizes. To mitigate the impact on I/O throughput, we propose the use of a compressed data buffer. Moreover, to minimize overhead caused by degradation in compression throughput, we propose the use of a shared Huffman tree.
<figure><img src="image_24.png" /><figcaption>Figure 22: Rate distortion of the proposal GPU compressors, with an extra lossless encoder enabled to show the best possible rate-distortion performance.</figcaption></figure>

tasks. However, there are core tasks associated with computing that are executed on the background thread. Like the computing tasks, these tasks have known execution intervals that must be respected when scheduling the I/O tasks.

All the scheduling constraints can be summarized as follows. On the compute thread, the compute tasks are immovable obstacles that the compression tasks must avoid. Similarly, on the background thread, the core tasks are immovable obstacles that the I/O tasks must avoid. The two threads are interdependent, since an I/O task cannot start until the corresponding compression task completes. This is the only constraint on the order of compression and I/O tasks, which can otherwise be scheduled in any order compatible with these dependence constraints. The goal of the task-scheduling algorithm is to find an ordering for compression tasks and for I/O tasks to minimize the overall execution time of the current iteration.

**Problem Analysis.** In scheduling theory nomenclature, the problem of scheduling the compression and I/O tasks is a flow-shop problem with two machines: The first one corresponds to the computation thread and the second to the background thread. Our problem is exactly a flow-shop problem with two machines with deterministic unavailability intervals on both machines and with non resumable jobs. The flow-shop problem with two machines, in the absence of unavailability intervals, is optimally solved by Johnson's algorithm [83]. The prob-

lem with non-resumable jobs becomes NP-complete as soon as there is at least one unavailability interval [92]; it becomes non-approximable by any constant factor (unless P=NP) as soon as there are at least two unavailability intervals on one machine [23].

**Adaptation of Johnson's Algorithm.** To schedule tasks, we adapt Johnson's algorithm, which builds an optimal solution in the absence of unavailability intervals. This algorithm works as follows. The jobs are partitioned in two sets $M_1$ and $M_2$ where jobs in $M_1$ are exactly the jobs whose compression task is not longer than its I/O task. Jobs in $M_1$ are sorted by non-decreasing duration of their compression task and jobs in $M_2$ by non-increasing duration of their I/O task. Then Johnson's algorithm executes first all jobs in $M_1$ followed by all jobs in $M_2$, starting each task as soon as possible. The first adaptation is straightforward: we execute tasks in the same order as in Johnson's algorithm, starting each one as soon as possible after already scheduled tasks and while respecting the unavailability intervals. The second adaptation is called backfilling [102], a technique that takes advantage of intervals of idleness. Then a new task, rather than being mandatorily scheduled after the completion of all already scheduled tasks, can be scheduled in an idleness interval if this does not delay the start of any already scheduled task.

## 8.3. Proposed Approach

We now present our framework that deeply integrates lossy compression with parallel I/O libraries, allowing for the overlap of compression and I/O with computation using our task-scheduling algorithms. Figure 23 shows the overview of our proposed framework. The goal is to dump the data generated by one iteration during the next iteration. The runtime design consists of three main components: fine-grained compression, compressed data buffer, and shared tree for Huffman coding.

**Fine-Grained Compression.** The data generated by HPC applications often consists of multiple data fields. For instance, data from a Nyx cosmological simulation may include density, temperature, and velocity information. While it may seem intuitive to compress each data field separately to achieve granularity in compression tasks for our task-scheduling algorithms, most scientific applications have only a few data fields (e.g., 6~12 data fields). This limits the number of compression tasks and I/O tasks, leading to low task-scheduling efficiency. To address this challenge, we propose slicing each data field into smaller data blocks and independently compressing each data block. Fine-grained compression may introduce two potential issues: (1) a degradation in compression ratio compared with compressing the data together and (2) a decrease in compression and I/O throughput. In practice, we use offline profiling to evaluate compression and I/O performance to identify the point at which compression and I/O throughput start to deteriorate with small data block sizes. To mitigate the impact on I/O throughput, we propose the use of a compressed data buffer. Moreover, to minimize overhead caused by degradation in compression throughput, we propose the use of a shared Huffman tree.
<figure><img src="image_25.png" /><figcaption>Figure 23: Overview of our proposed framework. We propose three runtime designs to facilitate task scheduling to handle the data-dumping process from iteration *n* and execute asynchronous compression, I/O operations, and computation tasks during iteration *n* + 1.</figcaption></figure>

**Compressed Data Buffer.** Based on our observations, the size of compressed data blocks can be relatively small. However, writing small data blocks can significantly reduce I/O throughput. To optimize I/O efficiency, we introduce an additional compressed data buffer that allows us to initiate I/O tasks as soon as possible while maintaining high throughput. The policy of writing the compressed data to the buffer and subsequently writing the buffer to the storage system is determined after the task scheduling. During the execution phase, once the execution orders for compression and I/O tasks are determined, we start placing the compressed data into the buffer when the background thread is engaged with I/O tasks or core tasks.

**Huffman Coding with Shared Tree.** During the prediction-based lossy compression process, one of the crucial steps is Huffman encoding of the quantization codes after the prediction and quantization steps. When compressing small data blocks with high compression ratios, building the Huffman tree can become a bottleneck for compression throughput. The reason is that building the Huffman tree takes nearly constant time regardless of the size of the input data. Additionally, data of similar types often results in similar Huffman trees: the Huffman tree built from the data of one iteration is highly similar to the tree built from the data of the next iteration.

To improve compression throughput for small data blocks,

we propose using shared Huffman trees across different iterations and data blocks on the same process. Prediction-based lossy compression accommodates outliers, which allow us to include values that defy coding by this shared Huffman tree. Building and using a shared Huffman tree for each process based on the data of the current iteration are impractical, because doing so would require synchronization of all compression tasks before any I/O task for compressed data could proceed. Instead, we build the shared Huffman tree based on the quantization code from the previous one or few iterations and utilize it for the data of the current iteration.

## 8.4. Performance Evaluation

**System configuration.** We implement our approach using HDF5 [25] and SZ3 [101]. Experiments are conducted on the Summit supercomputer [44] at Oak Ridge National Laboratory. We use 16 nodes, each including two IBM POWER9 processors with 42 physical cores and 512 GB DDR4 memory and 64 GPUs.

**Studied applications.** We consider two I/O-intensive scientific applications, Nyx [10] and WarpX [45], which have been used in numerous previous I/O studies. Nyx is an adaptive mesh hydrodynamics code designed to model astrophysical reacting flows on HPC systems [11, 10]. Nyx uses MPI for the long-range force calculation and architecture-specific programming language for the short-range force algorithms, such as OpenMP and CUDA. According to prior studies [10, 61, 131], it can run on tens of thousands of GPUs. WarpX is a highly parallel and highly optimized code that utilizes AMReX [160], runs on GPUs and multicore CPUs, and features load-balancing capabilities. WarpX can scale up to the world's largest supercomputer and was the recipient of the 2022 ACM Gordon Bell Prize [116].

**Compression Configuration.** We evaluate our approach using different scales of Nyx and WarpX applications. In all our evaluations, we utilize both GPUs and CPUs. While GPUs serve as the primary compute unit, CPUs handle compression and I/O tasks. Based on previous work [78, 80], we use absolute error bounds of (0.2, 0.4, $1e+3$, $2e+5$, $2e+5$, $2e+5$) to compress the six Nyx data fields (baryon density, dark matter density, temperature, velocity x, velocity y, velocity z), respectively, to achieve an average PSNR of 78.6 dB, resulting in a compression ratio of approximately 16×. The problem size of the Nyx application used in our evaluation is 4096 × 4096 × 4096 with three additional fields: particle_vx, particle_vy, and particle_vz. We compress these additional fields with a compression ratio of 16x to ensure the post hoc analysis quality. For the WarpX application, we compress the data fields with a compression ratio of 273.9×, as suggested by the application developers based on their post hoc analysis.

**Real-System-Based Evaluation.** We compare our solution to the baseline and to the previous solution that uses only asynchronous I/O without compression or task scheduling techniques. We report the overhead per iteration, that is, the total execution time of an iteration minus the time required to perform
<figure><img src="image_25.png" /><figcaption>Figure 23: Overview of our proposed framework. We propose three runtime designs to facilitate task scheduling to handle the data-dumping process from iteration *n* and execute asynchronous compression, I/O operations, and computation tasks during iteration *n* + 1.</figcaption></figure>

**Compressed Data Buffer.** Based on our observations, the size of compressed data blocks can be relatively small. However, writing small data blocks can significantly reduce I/O throughput. To optimize I/O efficiency, we introduce an additional compressed data buffer that allows us to initiate I/O tasks as soon as possible while maintaining high throughput. The policy of writing the compressed data to the buffer and subsequently writing the buffer to the storage system is determined after the task scheduling. During the execution phase, once the execution orders for compression and I/O tasks are determined, we start placing the compressed data into the buffer when the background thread is engaged with I/O tasks or core tasks.

**Huffman Coding with Shared Tree.** During the prediction-based lossy compression process, one of the crucial steps is Huffman encoding of the quantization codes after the prediction and quantization steps. When compressing small data blocks with high compression ratios, building the Huffman tree can become a bottleneck for compression throughput. The reason is that building the Huffman tree takes nearly constant time regardless of the size of the input data. Additionally, data of similar types often results in similar Huffman trees: the Huffman tree built from the data of one iteration is highly similar to the tree built from the data of the next iteration.

To improve compression throughput for small data blocks,

we propose using shared Huffman trees across different iterations and data blocks on the same process. Prediction-based lossy compression accommodates outliers, which allow us to include values that defy coding by this shared Huffman tree. Building and using a shared Huffman tree for each process based on the data of the current iteration are impractical, because doing so would require synchronization of all compression tasks before any I/O task for compressed data could proceed. Instead, we build the shared Huffman tree based on the quantization code from the previous one or few iterations and utilize it for the data of the current iteration.

## 8.4. Performance Evaluation

**System configuration.** We implement our approach using HDF5 [25] and SZ3 [101]. Experiments are conducted on the Summit supercomputer [44] at Oak Ridge National Laboratory. We use 16 nodes, each including two IBM POWER9 processors with 42 physical cores and 512 GB DDR4 memory and 64 GPUs.

**Studied applications.** We consider two I/O-intensive scientific applications, Nyx [10] and WarpX [45], which have been used in numerous previous I/O studies. Nyx is an adaptive mesh hydrodynamics code designed to model astrophysical reacting flows on HPC systems [11, 10]. Nyx uses MPI for the long-range force calculation and architecture-specific programming language for the short-range force algorithms, such as OpenMP and CUDA. According to prior studies [10, 61, 131], it can run on tens of thousands of GPUs. WarpX is a highly parallel and highly optimized code that utilizes AMReX [160], runs on GPUs and multicore CPUs, and features load-balancing capabilities. WarpX can scale up to the world's largest supercomputer and was the recipient of the 2022 ACM Gordon Bell Prize [116].

**Compression Configuration.** We evaluate our approach using different scales of Nyx and WarpX applications. In all our evaluations, we utilize both GPUs and CPUs. While GPUs serve as the primary compute unit, CPUs handle compression and I/O tasks. Based on previous work [78, 80], we use absolute error bounds of $(0.2, 0.4, 1e+3, 2e+5, 2e+5, 2e+5)$ to compress the six Nyx data fields (baryon density, dark matter density, temperature, velocity x, velocity y, velocity z), respectively, to achieve an average PSNR of 78.6 dB, resulting in a compression ratio of approximately $16 \times$. The problem size of the Nyx application used in our evaluation is $4096 \times 4096 \times 4096$ with three additional fields: particle_vx, particle_vy, and particle_vz. We compress these additional fields with a compression ratio of $16 \times$ to ensure the post hoc analysis quality. For the WarpX application, we compress the data fields with a compression ratio of $273.9 \times$, as suggested by the application developers based on their post hoc analysis.

**Real-System-Based Evaluation.** We compare our solution to the baseline and to the previous solution that uses only asynchronous I/O without compression or task scheduling techniques. We report the overhead per iteration, that is, the total execution time of an iteration minus the time required to perform
<figure><img src="image_26.png" /><figcaption>Figure 24: Time overheads (compared with computation time) of the baseline, asynchronous I/O, and our solution (with simulation for reference) with Nyx using 16 nodes and 64 GPUs</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Figure 25: Time overheads (compared with computation time) between the baseline, asynchronous I/O, and our solution across different timesteps.</figcaption></figure>

**only computations.** Figure 24 shows the overall performance improvement of our solution in comparison with the baseline and previous solutions on the Nyx application. Additionally, we provide simulation results for reference. We note that the real implementation results in slightly larger overhead compared with the simulation results. The reason is that the real implementation encounters more unexpected task interference due to (1) uncertainty of computing application intervals and (2) inaccurately predicted compression ratio, compression time, and/or I/O time for compressed data. Nonetheless, our solution still achieves a significant performance improvement, with a 3.78× and 2.57× improvement over the baseline and previous solutions, respectively. Figure 25 presents the performance of our solution across different stages of the application: the beginning, middle, and end. Our solution consistently outperforms the previous solution across all stages of the application.

## 8.5. Conclusion and Future Work

Lossy compression and asynchronous I/O are two efficient solutions for reducing storage overhead and improving I/O performance in large-scale HPC/scientific applications. However, existing implementations have limitations that hinder the full utilization of lossy compression and can lead to task collisions, limiting the overall application performance. To address these challenges, we proposed an optimization approach for the task-scheduling problem involving application computation, compression, and I/O. Experimental results with up to 64 GPUs on

Summit demonstrate that our solution reduces I/O overhead by up to 3.8× and 2.6× compared with the non-compression and asynchronous I/O solutions, respectively. Note that although we focus on parallel write to a single file, our proposed solutions are versatile and equally applicable to writing data to multiple files, enhancing performance in each scenario. Future work will need to consider other parallel I/O libraries.

## 9. Open problems

While the interest in lossy compression of scientific data started 7 to 8 years ago, the topic is still continuously developing, with more application domains, technique diversity, use cases, and users. We are not observing any slowdown in the growth of scientific data production from instrument facilities and simulation. On the contrary, the emergence of AI and large transformer models as new tools to advance science demands the production of even more data. Training a trillion-parameter transformer requires tens if not hundreds of trillion tokens. Generating this volume of data will take time, but it will also require significant storage space. Unfortunately, the storage, communication, and computing capabilities are not improving at the same pace. Scientific data compression will be even more needed in the future than it is today.

Beyond the need for scientific data compression, users also demand better lossy compression schemes that are faster, preserving more complex features and reducing the data further. Improving lossy compression after 7–8 years of dramatic progress is not trivial, and we summarize below some of the research topics that we consider important for this domain to continue its impressive improvement.

**Lossy compressibility bound** A fundamental driver for the research of compression has been the definition of compressibility limits expressed by compression bounds. The definition of Shannon entropy led to the development of a large research effort that produced optimal coding algorithms (Huffman and arithmetic coding). Such bounds are necessary to understand the effectiveness of existing algorithms and to motivate new research. However, despite years of research in information theory and improvements in the Shannon rate-distortion theory, there is no defined theoretical bound of lossy compressibility for scientific data. Establishing methods and techniques to compute such bounds (considering dataset properties and user quality constraints) is critical because it gives compressor developers an idea about the capability of existing compressors and informs users about the feasibility of using lossy compression for their use cases. This would serve all use cases of lossy compression for scientific data.

**Compression of communications and memory accesses** For many applications, including numerical simulation and AI model training, current parallel systems cannot reach high computing efficiency because of communication and memory bandwidth bottlenecks, despite decades of efforts in developing algorithms reducing communications and memory accesses. Research in mixed-precision computation has shown that for some applications the reduction of the numerical representation (e.g.,
<figure><img src="image_26.png" /><figcaption>Figure 24: Time overheads (compared with computation time) of the baseline, asynchronous I/O, and our solution (with simulation for reference) with Nyx using 16 nodes and 64 GPUs</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Figure 25: Time overheads (compared with computation time) between the baseline, asynchronous I/O, and our solution across different timesteps.</figcaption></figure>

**only computations.** Figure 24 shows the overall performance improvement of our solution in comparison with the baseline and previous solutions on the Nyx application. Additionally, we provide simulation results for reference. We note that the real implementation results in slightly larger overhead compared with the simulation results. The reason is that the real implementation encounters more unexpected task interference due to (1) uncertainty of computing application intervals and (2) inaccurately predicted compression ratio, compression time, and/or I/O time for compressed data. Nonetheless, our solution still achieves a significant performance improvement, with a 3.78× and 2.57× improvement over the baseline and previous solutions, respectively. Figure 25 presents the performance of our solution across different stages of the application: the beginning, middle, and end. Our solution consistently outperforms the previous solution across all stages of the application.

## 8.5. Conclusion and Future Work

Lossy compression and asynchronous I/O are two efficient solutions for reducing storage overhead and improving I/O performance in large-scale HPC/scientific applications. However, existing implementations have limitations that hinder the full utilization of lossy compression and can lead to task collisions, limiting the overall application performance. To address these challenges, we proposed an optimization approach for the task-scheduling problem involving application computation, compression, and I/O. Experimental results with up to 64 GPUs on

Summit demonstrate that our solution reduces I/O overhead by up to 3.8× and 2.6× compared with the non-compression and asynchronous I/O solutions, respectively. Note that although we focus on parallel write to a single file, our proposed solutions are versatile and equally applicable to writing data to multiple files, enhancing performance in each scenario. Future work will need to consider other parallel I/O libraries.

## 9. Open problems

While the interest in lossy compression of scientific data started 7 to 8 years ago, the topic is still continuously developing, with more application domains, technique diversity, use cases, and users. We are not observing any slowdown in the growth of scientific data production from instrument facilities and simulation. On the contrary, the emergence of AI and large transformer models as new tools to advance science demands the production of even more data. Training a trillion-parameter transformer requires tens if not hundreds of trillion tokens. Generating this volume of data will take time, but it will also require significant storage space. Unfortunately, the storage, communication, and computing capabilities are not improving at the same pace. Scientific data compression will be even more needed in the future than it is today.

Beyond the need for scientific data compression, users also demand better lossy compression schemes that are faster, preserving more complex features and reducing the data further. Improving lossy compression after 7–8 years of dramatic progress is not trivial, and we summarize below some of the research topics that we consider important for this domain to continue its impressive improvement.

**Lossy compressibility bound** A fundamental driver for the research of compression has been the definition of compressibility limits expressed by compression bounds. The definition of Shannon entropy led to the development of a large research effort that produced optimal coding algorithms (Huffman and arithmetic coding). Such bounds are necessary to understand the effectiveness of existing algorithms and to motivate new research. However, despite years of research in information theory and improvements in the Shannon rate-distortion theory, there is no defined theoretical bound of lossy compressibility for scientific data. Establishing methods and techniques to compute such bounds (considering dataset properties and user quality constraints) is critical because it gives compressor developers an idea about the capability of existing compressors and informs users about the feasibility of using lossy compression for their use cases. This would serve all use cases of lossy compression for scientific data.

**Compression of communications and memory accesses** For many applications, including numerical simulation and AI model training, current parallel systems cannot reach high computing efficiency because of communication and memory bandwidth bottlenecks, despite decades of efforts in developing algorithms reducing communications and memory accesses. Research in mixed-precision computation has shown that for some applications the reduction of the numerical representation (e.g.,
from double precision to simple precision or from simple preci-
sion to half precision) still produces correct results while open-
ing opportunities to improve performance by reducing the re-
quired communication and memory bandwidth. This is the
main motivation for the current research on compressing MPI
communications [167, 70] and data between the processors (CPU,
GPU, accelerators) and memory. One of the main critical ap-
plications of this research is accelerating AI training of very
large models by compressing all reduce/all-to-all communica-
tions [166]. Tests have also been performed on a wide range
of numerical simulations: heat propagation, lattice Boltzmann
simulation of airflow and fluid flow, simulation of the two-particle
orbit problem, financial stock option price forecasting model,
and weather forecasting models [42].

**Homomorphic compression** In simple terms, a homomor-
phism is a function that preserves the properties of mathemati-
cal structures (group, ring, or vector space) while mapping ele-
ments from one structure to another. For example, a ring homo-
morphism preserves addition, multiplication, and distributivity.
This property is critical in the context of encryption where the
goal is to perform operations on encrypted data without de-
crypting it. Homomorphic compression aims at a similar goal:
being able to compute operations (e.g., algebraic) directly on
the compressed data version without decompressing it first. Ho-
momorphic compression could reduce dramatically the decom-
pression/compression overhead in the case where many opera-
tions must be performed on the compressed data, for example, if
a numerical simulation needs to compute on lossy compressed
data because the non-compressed version of the data would not
fit in memory. The community has started working on this non-
trivial problem [111]. To be useful, this approach requires the
co-design of compression scheme(s) with the operations to per-
form on compressed data because it must retain all the qualities
of lossy compression: achieve desired ratios, speed, and accu-
racy (feature preservation).

**Compression for AI in science** An important direction to continue and potentially accelerate scientific discovery is to include artificial intelligence (AI) models in the scientific workflow. AI can be used for many use cases including surrogate models approximating numerical simulations, automatic online experiment steering, protein folding, and self-driving laboratories, and more generally as a researcher assistant. Current results show that larger models, in particular in the large language model (LLM) case, are more accurate, leading to a dramatic inflation of the AI model size. The most advanced LLMs use on the order of a trillion parameters. The largest models are so large that their parallel training takes several weeks or months on extreme-scale parallel systems. The most advanced training techniques combine multiple forms of parallelism including data, model, and pipeline parallelisms leading to data replication (data parallelism) and extreme volumes of internode communications (model and pipeline parallelism). The training duration far exceeds the mean time between failure of the parallel systems. In addition, large model training often fails, showing spikes of inaccuracy and divergence after convergence. The current method to mitigate system and training failures is checkpointing. Checkpoints can be very large, aggregating

model weights [76], gradients, and potentially other elements
such as the activation [79] and learning rate. Lossy compres-
sion can play an important role in accelerating large AI model
training by reducing training set size, communication overhead
[166], and checkpointing time.

**Combining lossy compression and encryption** Lossy com-
pression and encryption are two forms of data transformations
that are used for different applications. New use cases need both
lossy data compression and encryption. These two techniques
transform the data in fundamentally different ways: lossy com-
pression aims at reducing the data as much as possible, keeping
only the information necessary to produce correct results (from
the user's perspective), while encryption aims at protecting the
data against attacks, making it extremely difficult to decipher.
Because lossy compression aims at reducing as much as pos-
sible redundancy in the compressed format of the data, it also
makes it more fragile to attacks. Encryption can protect the
data, but it can be slow and reduce the performance benefit of
lossy compression. This situation raises the research question
of how to best combine them for different use cases, consider-
ing the criteria of performance, protection against attack, and
compressed version size. New results in this domain will find
applications in federated learning, the secured storage of sci-
entific data, and data communications in Integrated Research
Infrastructures [113].

**10. Conclusion: General observations from eight years of lossy compression for science.**

Lossy compression for scientific data is in rapid expansion
as an increasing number of scientific domains and modi operandi
(observation, experimentation, simulation) require reducing data
while preserving the same opportunity for scientific discovery.
From the diversity of the research work in lossy compression
for scientific data done in the joint laboratory of extreme-scale
computing, and discussions with scientific users in Climate,
Fluid Dynamics, Molecular dynamics, Cosmology, Fusion, In-
strument, Numerical methods, and AI, we can highlight the fol-
lowing general observations:

* Scientific researchers are initially reluctant to reduce their data sets using lossy compressors while they may already use other methods such as sampling, decimation, resolution reduction, dimensionality reduction, and filtering. When resource limitations (storage footprint, memory occupation, network bandwidth) mandate the use of data reduction techniques, lossy compression, and in particular error-bounded lossy compression, becomes a competitive data reduction method often outperforming other methods regarding the trade-off between reduction speed, ratio, and preservation of quantities of interest.

* Scientific users trust lossy compression techniques only if the compression technique provides some guarantees of scientific integrity preservation. In other words, users require the same scientific outcomes from non-compressed and lossy compressed data. Often, formal guarantees of
from double precision to simple precision or from simple preci-
sion to half precision) still produces correct results while open-
ing opportunities to improve performance by reducing the re-
quired communication and memory bandwidth. This is the
main motivation for the current research on compressing MPI
communications [167, 70] and data between the processors (CPU,
GPU, accelerators) and memory. One of the main critical ap-
plications of this research is accelerating AI training of very
large models by compressing all reduce/all-to-all communica-
tions [166]. Tests have also been performed on a wide range
of numerical simulations: heat propagation, lattice Boltzmann
simulation of airflow and fluid flow, simulation of the two-particle
orbit problem, financial stock option price forecasting model,
and weather forecasting models [42].

**Homomorphic compression** In simple terms, a homomor-
phism is a function that preserves the properties of mathemati-
cal structures (group, ring, or vector space) while mapping ele-
ments from one structure to another. For example, a ring homo-
morphism preserves addition, multiplication, and distributivity.
This property is critical in the context of encryption where the
goal is to perform operations on encrypted data without de-
crypting it. Homomorphic compression aims at a similar goal:
being able to compute operations (e.g., algebraic) directly on
the compressed data version without decompressing it first. Ho-
momorphic compression could reduce dramatically the decom-
pression/compression overhead in the case where many opera-
tions must be performed on the compressed data, for example, if
a numerical simulation needs to compute on lossy compressed
data because the non-compressed version of the data would not
fit in memory. The community has started working on this non-
trivial problem [111]. To be useful, this approach requires the
co-design of compression scheme(s) with the operations to per-
form on compressed data because it must retain all the qualities
of lossy compression: achieve desired ratios, speed, and accu-
racy (feature preservation).

**Compression for AI in science** An important direction to continue and potentially accelerate scientific discovery is to include artificial intelligence (AI) models in the scientific workflow. AI can be used for many use cases including surrogate models approximating numerical simulations, automatic online experiment steering, protein folding, and self-driving laboratories, and more generally as a researcher assistant. Current results show that larger models, in particular in the large language model (LLM) case, are more accurate, leading to a dramatic inflation of the AI model size. The most advanced LLMs use on the order of a trillion parameters. The largest models are so large that their parallel training takes several weeks or months on extreme-scale parallel systems. The most advanced training techniques combine multiple forms of parallelism including data, model, and pipeline parallelisms leading to data replication (data parallelism) and extreme volumes of internode communications (model and pipeline parallelism). The training duration far exceeds the mean time between failure of the parallel systems. In addition, large model training often fails, showing spikes of inaccuracy and divergence after convergence. The current method to mitigate system and training failures is checkpointing. Checkpoints can be very large, aggregating

model weights [76], gradients, and potentially other elements
such as the activation [79] and learning rate. Lossy compres-
sion can play an important role in accelerating large AI model
training by reducing training set size, communication overhead
[166], and checkpointing time.

**Combining lossy compression and encryption** Lossy com-
pression and encryption are two forms of data transformations
that are used for different applications. New use cases need both
lossy data compression and encryption. These two techniques
transform the data in fundamentally different ways: lossy com-
pression aims at reducing the data as much as possible, keeping
only the information necessary to produce correct results (from
the user's perspective), while encryption aims at protecting the
data against attacks, making it extremely difficult to decipher.
Because lossy compression aims at reducing as much as pos-
sible redundancy in the compressed format of the data, it also
makes it more fragile to attacks. Encryption can protect the
data, but it can be slow and reduce the performance benefit of
lossy compression. This situation raises the research question
of how to best combine them for different use cases, consider-
ing the criteria of performance, protection against attack, and
compressed version size. New results in this domain will find
applications in federated learning, the secured storage of sci-
entific data, and data communications in Integrated Research
Infrastructures [113].

10. Conclusion: General observations from eight years of
lossy compression for science.

Lossy compression for scientific data is in rapid expansion
as an increasing number of scientific domains and modi operandi
(observation, experimentation, simulation) require reducing data
while preserving the same opportunity for scientific discovery.
From the diversity of the research work in lossy compression
for scientific data done in the joint laboratory of extreme-scale
computing, and discussions with scientific users in Climate,
Fluid Dynamics, Molecular dynamics, Cosmology, Fusion, In-
strument, Numerical methods, and AI, we can highlight the fol-
lowing general observations:

* Scientific researchers are initially reluctant to reduce their data sets using lossy compressors while they may already use other methods such as sampling, decimation, resolution reduction, dimensionality reduction, and filtering. When resource limitations (storage footprint, memory occupation, network bandwidth) mandate the use of data reduction techniques, lossy compression, and in particular error-bounded lossy compression, becomes a competitive data reduction method often outperforming other methods regarding the trade-off between reduction speed, ratio, and preservation of quantities of interest.

* Scientific users trust lossy compression techniques only if the compression technique provides some guarantees of scientific integrity preservation. In other words, users require the same scientific outcomes from non-compressed and lossy compressed data. Often, formal guarantees of
scientific information preservation cannot be obtained. In many cases, only empirical analysis that compares analysis results from decompressed and uncompressed data is possible.

* As more scientific users adopt lossy compression for their data, we are observing a sharp increase in the application of lossy compression to more scientific domains and more use cases.

* A very important part of the methodology in this domain is missing: there is currently no method to compute an upper bound of lossy compressibility for any given datasets and user-specified distortion (loss) specifications. This lack should be addressed to provide lossy compressor developers with solid algorithmic objectives and users with well-informed expectations.

* Most scientific users need to preserve their observation/simulation/experimental data to be able to perform analysis after data generation. This is important to rerun the analysis to confirm a result for example. In some cases, new analyses, not planned or even not known at the time of generation will be run on the generated data. Lossy compression may not enable such unplanned analysis as the loss introduced by the compressor at the time of data generation was defined from the planned analysis.

* The paper has shown a panel of successes accomplished over the last eight years. As discussed in Section 4, the use of compressors within the core of numerical methods has also shown tremendous potential. That latter approach remains to be developed and assessed on additional algorithms.

## 11. Acknowledgments

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR), under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant OAC-2003709/2303064, OAC-2104023/2247080, OAC-2311875/2311876/2311877, OAC-2312673, and OAC-2034169. We acknowledge the computing resources provided on Bebop (operated by the Laboratory Computing Resource Center at Argonne). Some of the experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régional d'Aquitaine

(see https://www.plafrim.fr). TEZip - This work has been supported by the COE research grant in computational science from Hyogo Prefecture and Kobe City through the Foundation for Computational Science. XIOS-SZ - Mario Acosta and Xavier Yepes-Arbós have received co-funding from the State Research Agency through OEMES (PID2020-116324RA-I00).

## References

[1] Exascale computing project. https://www.exascaleproject.org/about/. (Accessed on 11/24/2023).

[2] SIGGRAPH Comput. Graph., 21(6), 1987.

[3] A year of weather. 2019.

[4] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.

[5] A. Abdelfattah, H. Anzt, E. G. Boman, E. Carson, T. Cojean, J. Dongarra, A. Fox, M. Gates, N. J. Higham, X. S. Li, J. Loe, P. Luszczek, S. Pranesh, S. Rajamanickam, T. Ribizel, B. F. Smith, K. Swirydowicz, S. Thomas, S. Tomov, Y. M. Tsai, and U. M. Yang. A survey of numerical linear algebra methods utilizing mixed-precision arithmetic. The International Journal of High Performance Computing Applications, 35(4):344–369, 2021.

[6] E. Agullo, F. Cappello, S. Di, L. Giraud, X. Liang, and N. Schenkels. Exploring variable accuracy storage through lossy compression techniques in numerical linear algebra: a first application to flexible GMRES. Research Report RR-9342, Inria Bordeaux Sud-Ouest, May 2020.

[7] E. Agullo, O. Coulaud, L. Giraud, M. Inannacito, G. Marait, and N. Schenkels. The backward stable variants of GMRES in variable accuracy. Research Report RR-9483, Inria, Sept. 2022.

[8] M. M. Alam, T. D. Nguyen, M. T. Hagan, and D. M. Chandler. A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images. In Applications of Digital Image Processing XXXVIII, volume 9599, page 959918. International Society for Optics and Photonics, 2015.

[9] J. I. Aliaga, H. Anzt, T. Grützmacher, E. S. Quintana-Ortí, and A. E. Tomás. Compressed basis GMRES on high-performance graphics processing units. The International Journal of High Performance Computing Applications, pages 1–18, Aug. 2022.

[10] A. Almgren, V. Beckner, C. Daley, B. Friesen, Z. Lukic, A. Myers, J. Sexton, and W. Zhang. Nyx, 2023. https://github.com/AMReX-Astro/Nyx.

[11] A. S. Almgren, J. B. Bell, M. J. Lijewski, Z. Lukić, and E. Van Andel. Nyx: A massively parallel AMR code for computational cosmology. The Astrophysical Journal, 765(1):39, 2013.

[12] J. Bachrach, H. Vo, B. Richards, and Y. L. D. a. d. Design. Chisel: constructing hardware in a Scala embedded language. DAC Design Automation Conference, pages 1212–1221, 2012.

[13] A. H. Baker. On preserving scientific integrity for climate model data in the HPC era. Computing in Science and Engineering, 23(6):16–24, 2021.

[14] A. H. Baker, D. Hammerling, and T. L. Turton. Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data. Eurographics Conference on Visualization (EuroVis), pages 517–528, 2019.

[15] A. H. Baker, D. M. Hammerling, S. A. Mickelson, H. Xu, M. B. Stolpe, P. Naveau, B. Sanderson, I. Ebert-Uphoff, S. Samarasinghe, F. De Simeone, F. Carbone, C. N. Gencarelli, J. M. Dennis, J. E. Kay, and P. Lindstrom. Evaluating lossy data compression on climate simulation data within a large ensemble. Geoscientific Model Development, 9(12):4381–4403.

[16] A. H. Baker, A. Pinard, and D. M. Hammerling. On a structural similarity index approach for floating-point data. IEEE Transactions on Visualization and Computer Graphics, pages 1–13, 2023.

[17] A. H. Baker, H. Xu, J. M. Dennis, M. N. Levy, D. Nychka, S. A. Mickelson, J. Edwards, M. Vertenstein, and A. Wegener. A methodology for evaluating the impact of data compression on climate simulation data. In Proceedings of the 23rd International Symposium on High-Performance
scientific information preservation cannot be obtained. In many cases, only empirical analysis that compares analysis results from decompressed and uncompressed data is possible.

* As more scientific users adopt lossy compression for their data, we are observing a sharp increase in the application of lossy compression to more scientific domains and more use cases.

* A very important part of the methodology in this domain is missing: there is currently no method to compute an upper bound of lossy compressibility for any given datasets and user-specified distortion (loss) specifications. This lack should be addressed to provide lossy compressor developers with solid algorithmic objectives and users with well-informed expectations.

* Most scientific users need to preserve their observation/simulation/experimental data to be able to perform analysis after data generation. This is important to rerun the analysis to confirm a result for example. In some cases, new analyses, not planned or even not known at the time of generation will be run on the generated data. Lossy compression may not enable such unplanned analysis as the loss introduced by the compressor at the time of data generation was defined from the planned analysis.

* The paper has shown a panel of successes accomplished over the last eight years. As discussed in Section 4, the use of compressors within the core of numerical methods has also shown tremendous potential. That latter approach remains to be developed and assessed on additional algorithms.

## 11. Acknowledgments

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR), under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant OAC-2003709/2303064, OAC-2104023/2247080, OAC-2311875/2311876/2311877, OAC-2312673, and OAC-2034169. We acknowledge the computing resources provided on Bebop (operated by the Laboratory Computing Resource Center at Argonne). Some of the experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régional d'Aquitaine

(see https://www.plafrim.fr). TEZip - This work has been supported by the COE research grant in computational science from Hyogo Prefecture and Kobe City through the Foundation for Computational Science. XIOS-SZ - Mario Acosta and Xavier Yepes-Arbós have received co-funding from the State Research Agency through OEMES (PID2020-116324RA-I00).

## References

[1] Exascale computing project. https://www.exascaleproject.org/about/. (Accessed on 11/24/2023).

[2] SIGGRAPH Comput. Graph., 21(6), 1987.

[3] A year of weather. 2019.

[4] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.

[5] A. Abdelfattah, H. Anzt, E. G. Boman, E. Carson, T. Cojean, J. Dongarra, A. Fox, M. Gates, N. J. Higham, X. S. Li, J. Loe, P. Luszczek, S. Pranesh, S. Rajamanickam, T. Ribizel, B. F. Smith, K. Swirydowicz, S. Thomas, S. Tomov, Y. M. Tsai, and U. M. Yang. A survey of numerical linear algebra methods utilizing mixed-precision arithmetic. The International Journal of High Performance Computing Applications, 35(4):344–369, 2021.

[6] E. Agullo, F. Cappello, S. Di, L. Giraud, X. Liang, and N. Schenkels. Exploring variable accuracy storage through lossy compression techniques in numerical linear algebra: a first application to flexible GMRES. Research Report RR-9342, Inria Bordeaux Sud-Ouest, May 2020.

[7] E. Agullo, O. Coulaud, L. Giraud, M. Inannacito, G. Marait, and N. Schenkels. The backward stable variants of GMRES in variable accuracy. Research Report RR-9483, Inria, Sept. 2022.

[8] M. M. Alam, T. D. Nguyen, M. T. Hagan, and D. M. Chandler. A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images. In Applications of Digital Image Processing XXXVIII, volume 9599, page 959918. International Society for Optics and Photonics, 2015.

[9] J. I. Aliaga, H. Anzt, T. Grützmacher, E. S. Quintana-Ortí, and A. E. Tomás. Compressed basis GMRES on high-performance graphics processing units. The International Journal of High Performance Computing Applications, pages 1–18, Aug. 2022.

[10] A. Almgren, V. Beckner, C. Daley, B. Friesen, Z. Lukic, A. Myers, J. Sexton, and W. Zhang. Nyx, 2023. https://github.com/AMReX-Astro/Nyx.

[11] A. S. Almgren, J. B. Bell, M. J. Lijewski, Z. Lukić, and E. Van Andel. Nyx: A massively parallel AMR code for computational cosmology. The Astrophysical Journal, 765(1):39, 2013.

[12] J. Bachrach, H. Vo, B. Richards, and Y. L. D. a. d. Design. Chisel: constructing hardware in a Scala embedded language. DAC Design Automation Conference, pages 1212–1221, 2012.

[13] A. H. Baker. On preserving scientific integrity for climate model data in the HPC era. Computing in Science and Engineering, 23(6):16–24, 2021.

[14] A. H. Baker, D. Hammerling, and T. L. Turton. Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data. Eurographics Conference on Visualization (EuroVis), pages 517–528, 2019.

[15] A. H. Baker, D. M. Hammerling, S. A. Mickelson, H. Xu, M. B. Stolpe, P. Naveau, B. Sanderson, I. Ebert-Uphoff, S. Samarasinghe, F. De Simeone, F. Carbone, C. N. Gencarelli, J. M. Dennis, J. E. Kay, and P. Lindstrom. Evaluating lossy data compression on climate simulation data within a large ensemble. Geoscientific Model Development, 9(12):4381–4403.

[16] A. H. Baker, A. Pinard, and D. M. Hammerling. On a structural similarity index approach for floating-point data. IEEE Transactions on Visualization and Computer Graphics, pages 1–13, 2023.

[17] A. H. Baker, H. Xu, J. M. Dennis, M. N. Levy, D. Nychka, S. A. Mickelson, J. Edwards, M. Vertenstein, and A. Wegener. A methodology for evaluating the impact of data compression on climate simulation data. In Proceedings of the 23rd International Symposium on High-Performance
*Parallel and Distributed Computing*, HPDC '14, page 203–214, New York, NY, USA, 2014. Association for Computing Machinery.

[18] R. Ballester-Ripoll, P. Lindstrom, and R. Pajarola. TTHRESH: Tensor compression for multidimensional visual data. *IEEE transactions on visualization and computer graphics*, 26(9):2891–2903, 2019.

[19] S. Barros, D. Dent, L. Isaksen, G. Robinson, G. Mozdzynski, and F. Wollenweber. The IFS model: A parallel production weather code. *Parallel Computing*, 21(10):1621–1638, 1995.

[20] J. F. Bell III, A. Godber, S. McNair, M. A. Caplinger, J. N. Maki, M. T. Lemmon, J. Van Beek, M. C. Malin, D. Wellington, K. M. Kinch, M. B. Madsen, C. Hardgrove, M. A. Ravine, E. Jensen, D. Harker, R. B. Anderson, K. E. Herkenhoff, R. V. Morris, E. Cisneros, and R. G. Deen. The Mars science laboratory Curiosity rover Mastcam instruments: Preflight and in-flight calibration, validation, and data archiving. *Earth and Space Science*, 4(7):396–452, 2017.

[21] R. Bird, N. Tan, S. V. Luedtke, S. L. Harrell, M. Taufer, and B. Albright. VPIC 2.0: Next generation particle-in-cell simulations. *IEEE Transactions on Parallel and Distributed Systems*, 33(4):952–963, 2021.

[22] J.-L. Blanco-Claraco, F.-A. Moreno-Dueñas, and J. González-Jiménez. The Malaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario. *The International Journal of Robotics Research*, 33(2):207–214, 2014.

[23] J. Breit, G. Schmidt, and V. A. Strusevich. Non-preemptive two-machine open shop scheduling with non-availability constraints. *Mathematical Methods of Operations Research*, 57:217—234, 2003.

[24] M. Burtscher and P. Ratanaworabhan. FPC: A high-speed compressor for double-precision floating-point data. *IEEE Transactions on Computers*, 58(1):18–31, Jan 2009.

[25] S. Byna, M. S. Breitenfeld, B. Dong, Q. Koziol, E. Pourmal, D. Robinson, J. Soumagne, H. Tang, V. Vishwanath, and R. Warren. ExaHDF5: delivering efficient parallel i/o on exascale computing systems. *Journal of Computer Science and Technology*, 35(1):145–160, 2020.

[26] F. Cappello, S. Di, S. Li, X. Liang, A. M. Gok, D. Tao, C. H. Yoon, X.-C. Wu, Y. Alexeev, and F. T. Chong. Use cases of lossy compression for floating-point data in scientific data sets. *The International Journal of High Performance Computing Applications*, 33(6):1201–1220, 2019.

[27] T. Chen, B. Xu, C. Zhang, and C. Guestrin. Training deep nets with sublinear memory cost. *arXiv preprint arXiv:1604.06174*, 2016.

[28] Z. Chen, T. He, X. Jin, and F. Wu. Learning for video compression. *IEEE Transactions on Circuits and Systems for Video Technology*, 2019.

[29] R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In *Proceedings of the 25th International Conference on Machine Learning*, pages 160–167, ACM, 2008.

[30] H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons, and E. P. Xing. Geeps: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server. In *Proceedings of the Eleventh European Conference on Computer Systems*, page 4. ACM, 2016.

[31] W. Cui, T. Zhang, S. Zhang, F. Jiang, W. Zuo, Z. Wan, and D. Zhao. Convolutional neural networks based intra prediction for HEVC. In *2017 Data Compression Conference (DCC)*, pages 436–436, 2017.

[32] cuZFP. https://github.com/LLNL/zfp/tree/develop/src/cuda_zfp, 2019. Online.

[33] Y. Dai, D. Liu, and F. Wu. A convolutional neural network approach for post-processing in HEVC intra coding. In *International Conference on Multimedia Modeling*, pages 28–39, 2017.

[34] X. Delaunay, A. Courtois, and F. Gouillon. Evaluation of lossless and lossy algorithms for the compression of scientific datasets in NetCDF-4 or HDF5 formatted files. preprint, Numerical Methods, Nov. 2018.

[35] S. Di. H5Z-SZ, 2023. https://github.com/disheng222/H5Z-SZ.

[36] S. Di and F. Cappello. Fast error-bounded lossy HPC data compression with SZ. In *2016 IEEE International Parallel and Distributed Processing Symposium*, pages 730–739. IEEE, 2016.

[37] S. Di, D. Tao, X. Liang, and F. Cappello. Efficient lossy compression for scientific data based on pointwise relative error bound. *IEEE Transactions on Parallel and Distributed Systems*, 30(2):331–345, 2019.

[38] R. Döscher, M. Acosta, A. Alessandri, P. Anthoni, T. Arsouze, T. Bergman, R. Bernardello, S. Boussetta, L.-P. Caron, G. Carver, M. Castrillo, F. Catalano, I. Cvijanovic, P. Davini, E. Dekker, F. J. Doblas-Reyes, D. Docquier, P. Echevarria, U. Fladrich, R. Fuentes-Franco, M. Gröger, J. v. Hardenberg, J. Hieronymus, M. P. Karami, J.-P. Keskinen, T. Koenig, R. Makkonen, F. Massonnet, M. Ménégoz, P. A. Miller, E. Moreno-Chamarro, L. Nieradzik, T. van Noije, P. Nolan, D. O'Donnell, P. Ollinaho, G. van den Oord, P. Ortega, O. T. Prims, A. Ramos, T. Reerink, C. Rousset, Y. Ruprich-Robert, P. Le Sager, T. Schmith, R. Schrödner, F. Serva, V. Šicardi, M. Sloth Madsen, B. Smith, T. Tian, E. Tourigny, P. Uotila, M. Vancoppenolle, S. Wang, D. Wärlind, U. Willén, K. Wyser, S. Yang, X. Yepeş-Arbós, and Q. Zhang. The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. *Geoscientific Model Development*, 15(7):2973–3020, 2022.

[39] J. Drkosova, A. Greenbaum, M. Rozloznik, and Z. Strakoš. Numerical stability of GMRES. *BIT Numerical Mathematics*, 35(February 1994):309–330, 1995.

[40] ECMWF. Modelling and prediction.

[41] A. Eldstål-Ahrens, A. Arelakis, and I. Sourdis. L2c: Combining lossy and lossless compression on memory and i/o. *ACM Trans. Embed. Comput. Syst.*, 21(1), jan 2022.

[42] A. Eldstål-Ahrens and I. Sourdis. Memsz: Squeezing memory traffic with lossy compression. *ACM Trans. Archit. Code Optim.*, 17(4), nov 2020.

[43] R. D. Evans, L. Liu, and T. M. Aamodt. JPEG-ACT: Accelerating deep learning via transform-based lossy compression. In *2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)*, pages 860–873. IEEE, 2020.

[44] O. R. L. C. Facility. Summit supercomputer, 2023. https://www.olcf.ornl.gov/summit/.

[45] L. Fedeli, A. Huebl, F. Boillod-Cerneux, T. Clark, K. Gott, C. Hillairet, S. Jaure, A. Leblanc, R. Lehe, A. Myers, C. Piechurski, M. Sato, N. Zaim, W. Zhang, J. Vay, and H. Vincenti. Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined particle-in-cell simulations on exascale-class supercomputers. In *SC22: International Conference for High Performance Computing, Networking, Storage and Analysis*, pages 1–12, Los Alamitos, CA, USA, Nov. 2022. IEEE Computer Society.

[46] L. Feng, X. Zhang, X. Zhang, S. Wang, R. Wang, and S. Ma. A dual-network based super-resolution for compressed high definition video. In *Pacific Rim Conference on Multimedia*, pages 600–610, 2018.

[47] J. L. Ferrer, M. Roth, and A. Antoniadis. Data compression for diffraction patterns. *Acta Crystallographica. Section D, Biological crystallography*, 54(Pt 2):184–199, March 1998.

[48] H. Filter. https://docs.hdfgroup.org/hdf5/develop/_f_i_l_t_e_r.html. Online.

[49] L. Floridi and M. Chiriatti. GPT-3: Its nature, scope, limits, and consequences. *Minds and Machines*, 30:681–694, 2020.

[50] M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robinson. An overview of the HDF5 technology suite and its applications. In *Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases - AD '11*, pages 36–47, New York, New York, USA, 2011. ACM Press.

[51] A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In *2012 IEEE Conference on Computer Vision and Pattern Recognition*, pages 3354–3361, 2012.

[52] V. Gligorov. Real-time data analysis at the LHC: present and future. In G. Cowan, C. Germain, I. Guyon, B. Kégl, and D. Rousseau, editors, *Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning*, volume 42 of *Proceedings of Machine Learning Research*, pages 1–18, Montreal, Canada, 13 Dec 2015. PMLR.

[53] A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse. The reversible residual network: Backpropagation without storing activations. In *Advances in neural information processing systems*, pages 2214–2224, 2017.

[54] Q. Gong, J. Chen, B. Whitney, X. Liang, V. Reshniak, T. Banerjee, J. Lee, A. Rangarajan, L. Wan, N. Vidal, Q. Liu, A. Gainaru, N. Podhorszki, R. Archibald, S. Ranka, and S. Klasky. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. *SoftwareX*, 24:101590, 2023.

[55] Q. Gong, J. Chen, B. Whitney, X. Liang, V. Reshniak, T. Banerjee, J. Lee, A. Rangarajan, L. Wan, N. Vidal, Q. Liu, A. Gainaru, N. Podhorszki, R. Archibald, S. Ranka, and S. Klasky. Mgard: A multigrid framework for high-performance, error-controlled data compression and refactoring. *SoftwareX*, 24:101590, 2023.

[56] Q. Gong, C. Zhang, X. Liang, V. Reshniak, J. Chen, A. Rangarajan, J.-P. Keskinen, T. Koenig, R. Makkonen, F. Massonnet, M. Ménégoz, P. A. Miller, E. Moreno-Chamarro, L. Nieradzik, T. van Noije, P. Nolan, D. O'Donnell, P. Ollinaho, G. van den Oord, P. Ortega, O. T. Prims, A. Ramos, T. Reerink, C. Rousset, Y. Ruprich-Robert, P. Le Sager, T. Schmith, R. Schrödner, F. Serva, V. Šicardi, M. Sloth Madsen, B. Smith, T. Tian, E. Tourigny, P. Uotila, M. Vancoppenolle, S. Wang, D. Wärlind, U. Willén, K. Wyser, S. Yang, X. Yepeş-Arbós, and Q. Zhang. The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. *Geoscientific Model Development*, 15(7):2973–3020, 2022.
*Parallel and Distributed Computing*, HPDC '14, page 203–214, New York, NY, USA, 2014. Association for Computing Machinery.

[18] R. Ballester-Ripoll, P. Lindstrom, and R. Pajarola. TTHRESH: Tensor compression for multidimensional visual data. *IEEE transactions on visualization and computer graphics*, 26(9):2891–2903, 2019.

[19] S. Barros, D. Dent, L. Isaksen, G. Robinson, G. Mozdzynski, and F. Wollenweber. The IFS model: A parallel production weather code. *Parallel Computing*, 21(10):1621–1638, 1995.

[20] J. F. Bell III, A. Godber, S. McNair, M. A. Caplinger, J. N. Maki, M. T. Lemmon, J. Van Beek, M. C. Malin, D. Wellington, K. M. Kinch, M. B. Madsen, C. Hardgrove, M. A. Ravine, E. Jensen, D. Harker, R. B. Anderson, K. E. Herkenhoff, R. V. Morris, E. Cisneros, and R. G. Deen. The Mars science laboratory Curiosity rover Mastcam instruments: Preflight and in-flight calibration, validation, and data archiving. *Earth and Space Science*, 4(7):396–452, 2017.

[21] R. Bird, N. Tan, S. V. Luedtke, S. L. Harrell, M. Taufer, and B. Albright. VPIC 2.0: Next generation particle-in-cell simulations. *IEEE Transactions on Parallel and Distributed Systems*, 33(4):952–963, 2021.

[22] J.-L. Blanco-Claraco, F.-A. Moreno-Dueñas, and J. González-Jiménez. The Malaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario. *The International Journal of Robotics Research*, 33(2):207–214, 2014.

[23] J. Breit, G. Schmidt, and V. A. Strusevich. Non-preemptive two-machine open shop scheduling with non-availability constraints. *Mathematical Methods of Operations Research*, 57:217—234, 2003.

[24] M. Burtscher and P. Ratanaworabhan. FPC: A high-speed compressor for double-precision floating-point data. *IEEE Transactions on Computers*, 58(1):18–31, Jan 2009.

[25] S. Byna, M. S. Breitenfeld, B. Dong, Q. Koziol, E. Pourmal, D. Robinson, J. Soumagne, H. Tang, V. Vishwanath, and R. Warren. ExaHDF5: delivering efficient parallel i/o on exascale computing systems. *Journal of Computer Science and Technology*, 35(1):145–160, 2020.

[26] F. Cappello, S. Di, S. Li, X. Liang, A. M. Gok, D. Tao, C. H. Yoon, X.-C. Wu, Y. Alexeev, and F. T. Chong. Use cases of lossy compression for floating-point data in scientific data sets. *The International Journal of High Performance Computing Applications*, 33(6):1201–1220, 2019.

[27] T. Chen, B. Xu, C. Zhang, and C. Guestrin. Training deep nets with sublinear memory cost. *arXiv preprint arXiv:1604.06174*, 2016.

[28] Z. Chen, T. He, X. Jin, and F. Wu. Learning for video compression. *IEEE Transactions on Circuits and Systems for Video Technology*, 2019.

[29] R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In *Proceedings of the 25th International Conference on Machine Learning*, pages 160–167, ACM, 2008.

[30] H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons, and E. P. Xing. Geeps: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server. In *Proceedings of the Eleventh European Conference on Computer Systems*, page 4. ACM, 2016.

[31] W. Cui, T. Zhang, S. Zhang, F. Jiang, W. Zuo, Z. Wan, and D. Zhao. Convolutional neural networks based intra prediction for HEVC. In *2017 Data Compression Conference (DCC)*, pages 436–436, 2017.

[32] cuZFP. https://github.com/LLNL/zfp/tree/develop/src/cuda_zfp, 2019. Online.

[33] Y. Dai, D. Liu, and F. Wu. A convolutional neural network approach for post-processing in HEVC intra coding. In *International Conference on Multimedia Modeling*, pages 28–39, 2017.

[34] X. Delaunay, A. Courtois, and F. Gouillon. Evaluation of lossless and lossy algorithms for the compression of scientific datasets in NetCDF-4 or HDF5 formatted files. preprint, Numerical Methods, Nov. 2018.

[35] S. Di. H5Z-SZ, 2023. https://github.com/disheng222/H5Z-SZ.

[36] S. Di and F. Cappello. Fast error-bounded lossy HPC data compression with SZ. In *2016 IEEE International Parallel and Distributed Processing Symposium*, pages 730–739. IEEE, 2016.

[37] S. Di, D. Tao, X. Liang, and F. Cappello. Efficient lossy compression for scientific data based on pointwise relative error bound. *IEEE Transactions on Parallel and Distributed Systems*, 30(2):331–345, 2019.

[38] R. Döscher, M. Acosta, A. Alessandri, P. Anthoni, T. Arsouze, T. Bergman, R. Bernardello, S. Boussetta, L.-P. Caron, G. Carver, M. Castrillo, F. Catalano, I. Cvijanovic, P. Davini, E. Dekker, F. J. Doblas-Reyes, D. Docquier, P. Echevarria, U. Fladrich, R. Fuentes-Franco, M. Gröger, J. v. Hardenberg, J. Hieronymus, M. P. Karami, J.-P. Keskinen, T. Koenig, R. Makkonen, F. Massonnet, M. Ménégoz, P. A. Miller, E. Moreno-Chamarro, L. Nieradzik, T. van Noije, P. Nolan, D. O'Donnell, P. Ollinaho, G. van den Oord, P. Ortega, O. T. Prims, A. Ramos, T. Reerink, C. Rousset, Y. Ruprich-Robert, P. Le Sager, T. Schmith, R. Schrödner, F. Serva, V. Šicardi, M. Sloth Madsen, B. Smith, T. Tian, E. Tourigny, P. Uotila, M. Vancoppenolle, S. Wang, D. Wärlind, U. Willén, K. Wyser, S. Yang, X. Yepeş-Arbós, and Q. Zhang. The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. *Geoscientific Model Development*, 15(7):2973–3020, 2022.

[39] J. Drkosova, A. Greenbaum, M. Rozloznik, and Z. Strakoš. Numerical stability of GMRES. *BIT Numerical Mathematics*, 35(February 1994):309–330, 1995.

[40] ECMWF. Modelling and prediction.

[41] A. Eldstål-Ahrens, A. Arelakis, and I. Sourdis. L2c: Combining lossy and lossless compression on memory and i/o. *ACM Trans. Embed. Comput. Syst.*, 21(1), jan 2022.

[42] A. Eldstål-Ahrens and I. Sourdis. Memsz: Squeezing memory traffic with lossy compression. *ACM Trans. Archit. Code Optim.*, 17(4), nov 2020.

[43] R. D. Evans, L. Liu, and T. M. Aamodt. JPEG-ACT: Accelerating deep learning via transform-based lossy compression. In *2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)*, pages 860–873. IEEE, 2020.

[44] O. R. L. C. Facility. Summit supercomputer, 2023. https://www.olcf.ornl.gov/summit/.

[45] L. Fedeli, A. Huebl, F. Boillod-Cerneux, T. Clark, K. Gott, C. Hillairet, S. Jaure, A. Leblanc, R. Lehe, A. Myers, C. Piechurski, M. Sato, N. Zaim, W. Zhang, J. Vay, and H. Vincenti. Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined particle-in-cell simulations on exascale-class supercomputers. In *SC22: International Conference for High Performance Computing, Networking, Storage and Analysis*, pages 1–12, Los Alamitos, CA, USA, Nov. 2022. IEEE Computer Society.

[46] L. Feng, X. Zhang, X. Zhang, S. Wang, R. Wang, and S. Ma. A dual-network based super-resolution for compressed high definition video. In *Pacific Rim Conference on Multimedia*, pages 600–610, 2018.

[47] J. L. Ferrer, M. Roth, and A. Antoniadis. Data compression for diffraction patterns. *Acta Crystallographica. Section D, Biological crystallography*, 54(Pt 2):184–199, March 1998.

[48] H. Filter. https://docs.hdfgroup.org/hdf5/develop/_f_i_l_t_e_r.html. Online.

[49] L. Floridi and M. Chiriatti. GPT-3: Its nature, scope, limits, and consequences. *Minds and Machines*, 30:681–694, 2020.

[50] M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robinson. An overview of the HDF5 technology suite and its applications. In *Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases - AD '11*, pages 36–47, New York, New York, USA, 2011. ACM Press.

[51] A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In *2012 IEEE Conference on Computer Vision and Pattern Recognition*, pages 3354–3361, 2012.

[52] V. Gligorov. Real-time data analysis at the LHC: present and future. In G. Cowan, C. Germain, I. Guyon, B. Kégl, and D. Rousseau, editors, *Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning*, volume 42 of *Proceedings of Machine Learning Research*, pages 1–18, Montreal, Canada, 13 Dec 2015. PMLR.

[53] A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse. The reversible residual network: Backpropagation without storing activations. In *Advances in neural information processing systems*, pages 2214–2224, 2017.

[54] Q. Gong, J. Chen, B. Whitney, X. Liang, V. Reshniak, T. Banerjee, J. Lee, A. Rangarajan, L. Wan, N. Vidal, Q. Liu, A. Gainaru, N. Podhorszki, R. Archibald, S. Ranka, and S. Klasky. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. *SoftwareX*, 24:101590, 2023.

[55] Q. Gong, J. Chen, B. Whitney, X. Liang, V. Reshniak, T. Banerjee, J. Lee, A. Rangarajan, L. Wan, N. Vidal, Q. Liu, A. Gainaru, N. Podhorszki, R. Archibald, S. Ranka, and S. Klasky. Mgard: A multigrid framework for high-performance, error-controlled data compression and refactoring. *SoftwareX*, 24:101590, 2023.

[56] Q. Gong, C. Zhang, X. Liang, V. Reshniak, J. Chen, A. Rangarajan, J.-P. Keskinen, T. Koenig, R. Makkonen, F. Massonnet, M. Ménégoz, P. A. Miller, E. Moreno-Chamarro, L. Nieradzik, T. van Noije, P. Nolan, D. O'Donnell, P. Ollinaho, G. van den Oord, P. Ortega, O. T. Prims, A. Ramos, T. Reerink, C. Rousset, Y. Ruprich-Robert, P. Le Sager, T. Schmith, R. Schrödner, F. Serva, V. Šicardi, M. Sloth Madsen, B. Smith, T. Tian, E. Tourigny, P. Uotila, M. Vancoppenolle, S. Wang, D. Wärlind, U. Willén, K. Wyser, S. Yang, X. Yepeş-Arbós, and Q. Zhang. The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. *Geoscientific Model Development*, 15(7):2973–3020, 2022.
S. Ranka, N. Vidal, L. Wan, P. Ullrich, N. Podhorszki, R. Jacob, and S. Klasky. Spatiotemporally adaptive compression for scientific dataset with feature preservation — a case study on simulation data with extreme climate events analysis. In *2023 IEEE 19th International Conference on e-Science (e-Science)*, pages 1–10, 2023.

[57] Y. Gong, L. Liu, M. Yang, and L. Bourdev. Compressing deep convolutional networks using vector quantization. *arXiv preprint arXiv:1412.6115*, 2014.

[58] M. H. Gross, L. Lippert, and O. G. Staadt. Compression methods for visualization. *Future Generation Computer Systems*, 15(1):11–29, 1999.

[59] T. Grützmacher, H. Anzt, and E. S. Quintana-Ortí. Using Ginkgo's memory accessor for improving the accuracy of memory-bound low precision BLAS. *Software - Practice and Experience*, (September):1–18, 2021.

[60] J. Guivant, J. Nieto, and E. Nebot. Victoria park dataset, 2012.

[61] S. Habib, A. Pope, H. Finkel, N. Frontiere, K. Heitmann, D. Daniel, P. Fasel, V. Morozov, G. Zagaris, T. Peterka, V. Venkatram, L. Zarija, S. Saba, and W.-k. Liao. HACC: Simulating sky surveys on state-of-the-art supercomputing architectures. *New Astronomy*, 42:49–65, 2016.

[62] M. Hadian-Jazi, A. Sadri, A. Barty, O. Yefanov, M. Galchenkova, D. Oberthuer, D. Komadina, W. Brehm, H. Kirkwood, G. Mills, R. de Wijn, R. Letrun, M. Kloos, M. Vakili, L. Gelisio, C. Darmanin, A. P. Mancuso, H. N. Chapman, and B. Abbey. Data reduction for serial crystallography using a robust peak finder. *Journal of Applied Crystallography*, 54(5):1360–1378, Oct. 2021.

[63] M. Hammer, K. Yoshii, and A. Miceli. Strategies for on-chip digital data compression for x-ray pixel detectors. *Journal of Instrumentation*, 16(01):P01025, 2021.

[64] D. M. Hammerling, A. H. Baker, A. Pinard, and P. Lindstrom. A collaborative effort to improve lossy compression methods for climate data. In *2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)*, pages 16–22, 2019.

[65] S. Han, H. Mao, and W. J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. *arXiv preprint arXiv:1510.00149*, 2015.

[66] M. Hanke, J. Biercamp, C. O. Escamilla, T. Jahns, D. Kleberg, P. Selwood, and S. Mullerworth. Deliverable 7.3 – Reference implementations of parallel I/O and of I/O server. Technical report, DKRZ, 2013.

[67] HDF Group and others. Hierarchical data format version 5. filter, 2000.

[68] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In *Proceedings of the IEEE conference on Computer Vision and Pattern Recognition*, pages 770–778, 2016.

[69] N. J. Higham and T. Mary. Mixed precision algorithms in numerical linear algebra. *Acta Numerica*, 31:347–414, May 2022.

[70] J. Huang, S. Di, X. Yu, Y. Zhai, J. Liu, K. Raffenetti, H. Zhou, K. Zhao, Z. Chen, F. Cappello, Y. Guo, and R. Thakur. C-Coll: Introducing error-bounded lossy compression into MPI collectives, 2023.

[71] N. Hübbe, A. Wegener, J. M. Kunkel, Y. Ling, and T. Ludwig. Evaluating lossy compression on climate data. In J. M. Kunkel, T. Ludwig, and H. W. Meuer, editors, *Supercomputing*, pages 343–356, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg.

[72] J. W. Hurrell, M. M. Holland, P. R. Gent, S. Ghan, J. E. Kay, P. J. Kushner, J.-F. Lamarque, W. G. Large, D. Lawrence, K. Lindsay, et al. The Community Earth System Model: a framework for collaborative research. *Bulletin of the American Meteorological Society*, 94(9):1339–1360, 2013.

[73] T. Ishikawa et al. Spring-8-ii conceptual design report. Technical report, RIKEN SPRing-8 Center, Hyogo, Japan, 2014.

[74] C. Jia, S. Wang, X. Zhang, S. Wang, and S. Ma. Spatial-temporal residue network based in-loop filter for video coding. In *2017 IEEE Visual Communications and Image Processing (VCIP)*, pages 1–4, 2017.

[75] P. Jiao, S. Di, H. Guo, K. Zhao, J. Tian, D. Tao, X. Liang, and F. Cappello. Toward quantity-of-interest preserving lossy compression for scientific data. *Proc. VLDB Endow.*, 16(4):697–710, dec 2022.

[76] S. Jin, S. Di, X. Liang, J. Tian, D. Tao, and F. Cappello. DeepSZ: A novel framework to compress deep neural networks by using error-bounded lossy compression. In *Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing*, pages 159–170, 2019.

[77] S. Jin, S. Di, F. Vivien, D. Wang, Y. Robert, D. Tao, and F. Cappello. Concealing compression-accelerated I/O for HPC applications through in situ task scheduling. In *EuroSys 2024*, Athens, Greece, 2024.

[78] S. Jin, P. Grosset, C. M. Biwer, J. Pulido, J. Tian, D. Tao, and J. Ahrens. Understanding GPU-based lossy compression for extreme-scale cosmological simulations. In *2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 105–115. IEEE, 2020.

[79] S. Jin, G. Li, S. L. Song, and D. Tao. A novel memory-efficient deep learning training framework via error-bounded lossy compression, 2020.

[80] S. Jin, J. Pulido, P. Grosset, J. Tian, D. Tao, and J. Ahrens. Adaptive configuration of in situ lossy compression for cosmology simulations via fine-grained rate-quality modeling. In *Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing*, pages 45–56, 2020.

[81] S. Jin, D. Tao, H. Tang, S. Di, S. Byna, Z. Lukic, and F. Cappello. Accelerating parallel write via deeply integrating predictive lossy compression with hdf5. In *Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis*, pages 1–15, 2022.

[82] S. Jin, C. Zhang, X. Jiang, Y. Feng, H. Guan, G. Li, S. L. Song, and D. Tao. Comet: a novel memory-efficient deep learning training framework by using error-bounded lossy compression. *arXiv preprint arXiv:2111.09562*, 2021.

[83] S. M. Johnson. Optimal two- and three-stage production schedules with setup times included. *Naval Research Logistics Quarterly*, 1(1):61–68, 1954.

[84] S. Joussaume, A. Bellucci, J. Biercamp, R. Budich, A. Dawson, M. Fouchols, B. Lawrence, L. Linardikis, S. Masson, Y. Meurdesoif, G. Riley, K. Taylor, and P. Vidale. Modelling the Earth's climate system: data and computing challenges. In *2012 SC Companion: High Performance Computing, Networking Storage and Analysis*, pages 2325–2356. IEEE, nov 2012.

[85] J. E. Kay and et al. The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. *Bulletin of the American Meteorological Society*, 96(8):1333–1349, 2015.

[86] S. Kayum et al. GeoDRIVE – a high performance computing flexible platform for seismic applications. *First Break*, 38(2):97–100, 2020.

[87] A. Kiely and M. Klimesh. Preliminary image compression results from the mars exploration rovers. *Interplanetary Network Progress Report*, pages 1–8, 02 2004.

[88] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In *Advances in neural information processing systems*, pages 1097–1105, 2012.

[89] M. Kuhn, J. Kunkel, and T. Ludwig. Data compression for climate data. *Supercomputing Frontiers and Innovations*, 3(1):75–94, June 2016.

[90] S. Lakshminarasimhan, N. Shah, S. Ethier, S. Ku, C. Chang, S. Klasky, R. Latham, R. B. Ross, and N. F. Samatova. ISABELA for effective in situ compression of scientific data. *Concurrency and Computation: Practice and Experience*, 25(4):524–540, 2013.

[91] Large Scale Visual Recognition Challenge. http://www.image-net.org/challenges/LSVRC/. 2019. Online.

[92] C.-Y. Lee. Minimizing the makespan in the two-machine flowshop scheduling problem with an availability constraint. *Operations Research Letters*, 20(3):129–139, 1997.

[93] F. Leonarski, A. Mozzanica, M. Brückner, C. Lopez-Cuenca, S. Redford, L. Sala, A. Babic, H. Billich, O. Bunk, B. Schmitt, and M. Wang. JUNGFRAU detector for brighter X-ray sources: Solutions for IT and data science challenges in macromolecular crystallography. *Structural Dynamics*, 7(1):014305, 2020.

[94] J. Li, B. Li, J. Xu, R. Xiong, and W. Gao. Fully connected network-based intra prediction for image coding. *IEEE Transactions on Image Processing*, 27(7):3236–3247, 2018.

[95] S. Li, P. Lindstrom, and J. Clyne. Lossy scientific data compression with SPERR. In *2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 1007–1017, 2023.

[96] S. Li, N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. Data reduction techniques for simulation, visualization and data analysis. *Computer Graphics Forum*, 37(6):422–447, 2018.

[97] Y. Li, E. Perlman, M. Wan, Y. Yang, C. Meneveau, R. Burns, S. Chen, A. Szalay, and G. Eyink. A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence. *Journal of Turbulence*, (9):N31, 2008.

[98] X. Liang, S. Di, D. Tao, S. Li, S. Li, H. Guo, Z. Chen, and F. Cappello.
S. Ranka, N. Vidal, L. Wan, P. Ullrich, N. Podhorszki, R. Jacob, and S. Klasky. Spatiotemporally adaptive compression for scientific dataset with feature preservation — a case study on simulation data with extreme climate events analysis. In *2023 IEEE 19th International Conference on e-Science (e-Science)*, pages 1–10, 2023.

[57] Y. Gong, L. Liu, M. Yang, and L. Bourdev. Compressing deep convolutional networks using vector quantization. *arXiv preprint arXiv:1412.6115*, 2014.

[58] M. H. Gross, L. Lippert, and O. G. Staadt. Compression methods for visualization. *Future Generation Computer Systems*, 15(1):11–29, 1999.

[59] T. Grützmacher, H. Anzt, and E. S. Quintana-Ortí. Using Ginkgo's memory accessor for improving the accuracy of memory-bound low precision BLAS. *Software - Practice and Experience*, (September):1–18, 2021.

[60] J. Guivant, J. Nieto, and E. Nebot. Victoria park dataset, 2012.

[61] S. Habib, A. Pope, H. Finkel, N. Frontiere, K. Heitmann, D. Daniel, P. Fasel, V. Morozov, G. Zagaris, T. Peterka, V. Venkatram, L. Zarija, S. Saba, and W.-k. Liao. HACC: Simulating sky surveys on state-of-the-art supercomputing architectures. *New Astronomy*, 42:49–65, 2016.

[62] M. Hadian-Jazi, A. Sadri, A. Barty, O. Yefanov, M. Galchenkova, D. Oberthuer, D. Komadina, W. Brehm, H. Kirkwood, G. Mills, R. de Wijn, R. Letrun, M. Kloos, M. Vakili, L. Gelisio, C. Darmanin, A. P. Mancuso, H. N. Chapman, and B. Abbey. Data reduction for serial crystallography using a robust peak finder. *Journal of Applied Crystallography*, 54(5):1360–1378, Oct. 2021.

[63] M. Hammer, K. Yoshii, and A. Miceli. Strategies for on-chip digital data compression for x-ray pixel detectors. *Journal of Instrumentation*, 16(01):P01025, 2021.

[64] D. M. Hammerling, A. H. Baker, A. Pinard, and P. Lindstrom. A collaborative effort to improve lossy compression methods for climate data. In *2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)*, pages 16–22, 2019.

[65] S. Han, H. Mao, and W. J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. *arXiv preprint arXiv:1510.00149*, 2015.

[66] M. Hanke, J. Biercamp, C. O. Escamilla, T. Jahns, D. Kleberg, P. Selwood, and S. Mullerworth. Deliverable 7.3 – Reference implementations of parallel I/O and of I/O server. Technical report, DKRZ, 2013.

[67] HDF Group and others. Hierarchical data format version 5. filter, 2000.

[68] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In *Proceedings of the IEEE conference on Computer Vision and Pattern Recognition*, pages 770–778, 2016.

[69] N. J. Higham and T. Mary. Mixed precision algorithms in numerical linear algebra. *Acta Numerica*, 31:347–414, May 2022.

[70] J. Huang, S. Di, X. Yu, Y. Zhai, J. Liu, K. Raffenetti, H. Zhou, K. Zhao, Z. Chen, F. Cappello, Y. Guo, and R. Thakur. C-Coll: Introducing error-bounded lossy compression into MPI collectives, 2023.

[71] N. Hübbe, A. Wegener, J. M. Kunkel, Y. Ling, and T. Ludwig. Evaluating lossy compression on climate data. In J. M. Kunkel, T. Ludwig, and H. W. Meuer, editors, *Supercomputing*, pages 343–356, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg.

[72] J. W. Hurrell, M. M. Holland, P. R. Gent, S. Ghan, J. E. Kay, P. J. Kushner, J.-F. Lamarque, W. G. Large, D. Lawrence, K. Lindsay, et al. The Community Earth System Model: a framework for collaborative research. *Bulletin of the American Meteorological Society*, 94(9):1339–1360, 2013.

[73] T. Ishikawa et al. Spring-8-ii conceptual design report. Technical report, RIKEN SPRing-8 Center, Hyogo, Japan, 2014.

[74] C. Jia, S. Wang, X. Zhang, S. Wang, and S. Ma. Spatial-temporal residue network based in-loop filter for video coding. In *2017 IEEE Visual Communications and Image Processing (VCIP)*, pages 1–4, 2017.

[75] P. Jiao, S. Di, H. Guo, K. Zhao, J. Tian, D. Tao, X. Liang, and F. Cappello. Toward quantity-of-interest preserving lossy compression for scientific data. *Proc. VLDB Endow.*, 16(4):697–710, dec 2022.

[76] S. Jin, S. Di, X. Liang, J. Tian, D. Tao, and F. Cappello. DeepSZ: A novel framework to compress deep neural networks by using error-bounded lossy compression. In *Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing*, pages 159–170, 2019.

[77] S. Jin, S. Di, F. Vivien, D. Wang, Y. Robert, D. Tao, and F. Cappello. Concealing compression-accelerated I/O for HPC applications through in situ task scheduling. In *EuroSys 2024*, Athens, Greece, 2024.

[78] S. Jin, P. Grosset, C. M. Biwer, J. Pulido, J. Tian, D. Tao, and J. Ahrens. Understanding GPU-based lossy compression for extreme-scale cosmological simulations. In *2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 105–115. IEEE, 2020.

[79] S. Jin, G. Li, S. L. Song, and D. Tao. A novel memory-efficient deep learning training framework via error-bounded lossy compression, 2020.

[80] S. Jin, J. Pulido, P. Grosset, J. Tian, D. Tao, and J. Ahrens. Adaptive configuration of in situ lossy compression for cosmology simulations via fine-grained rate-quality modeling. In *Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing*, pages 45–56, 2020.

[81] S. Jin, D. Tao, H. Tang, S. Di, S. Byna, Z. Lukic, and F. Cappello. Accelerating parallel write via deeply integrating predictive lossy compression with hdf5. In *Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis*, pages 1–15, 2012.

[82] S. Jin, C. Zhang, X. Jiang, Y. Feng, H. Guan, G. Li, S. L. Song, and D. Tao. Comet: a novel memory-efficient deep learning training framework by using error-bounded lossy compression. *arXiv preprint arXiv:2111.09562*, 2021.

[83] S. M. Johnson. Optimal two- and three-stage production schedules with setup times included. *Naval Research Logistics Quarterly*, 1(1):61–68, 1954.

[84] S. Joussaume, A. Bellucci, J. Biercamp, R. Budich, A. Dawson, M. Fouchols, B. Lawrence, L. Linardikis, S. Masson, Y. Meurdesoif, G. Riley, K. Taylor, and P. Vidale. Modelling the Earth's climate system: data and computing challenges. In *2012 SC Companion: High Performance Computing, Networking Storage and Analysis*, pages 2325–2356. IEEE, nov 2012.

[85] J. E. Kay and et al. The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. *Bulletin of the American Meteorological Society*, 96(8):1333–1349, 2015.

[86] S. Kayum et al. GeoDRIVE – a high performance computing flexible platform for seismic applications. *First Break*, 38(2):97–100, 2020.

[87] A. Kiely and M. Klimesh. Preliminary image compression results from the mars exploration rovers. *Interplanetary Network Progress Report*, pages 1–8, 02 2004.

[88] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In *Advances in neural information processing systems*, pages 1097–1105, 2012.

[89] M. Kuhn, J. Kunkel, and T. Ludwig. Data compression for climate data. *Supercomputing Frontiers and Innovations*, 3(1):75–94, June 2016.

[90] S. Lakshminarasimhan, N. Shah, S. Ethier, S. Ku, C. Chang, S. Klasky, R. Latham, R. B. Ross, and N. F. Samatova. ISABELA for effective in situ compression of scientific data. *Concurrency and Computation: Practice and Experience*, 25(4):524–540, 2013.

[91] Large Scale Visual Recognition Challenge. http://www.image-net.org/challenges/LSVRC/. 2019. Online.

[92] C.-Y. Lee. Minimizing the makespan in the two-machine flowshop scheduling problem with an availability constraint. *Operations Research Letters*, 20(3):129–139, 1997.

[93] F. Leonarski, A. Mozzanica, M. Brückner, C. Lopez-Cuenca, S. Redford, L. Sala, A. Babic, H. Billich, O. Bunk, B. Schmitt, and M. Wang. JUNGFRAU detector for brighter X-ray sources: Solutions for IT and data science challenges in macromolecular crystallography. *Structural Dynamics*, 7(1):014305, 2020.

[94] J. Li, B. Li, J. Xu, R. Xiong, and W. Gao. Fully connected network-based intra prediction for image coding. *IEEE Transactions on Image Processing*, 27(7):3236–3247, 2018.

[95] S. Li, P. Lindstrom, and J. Clyne. Lossy scientific data compression with SPERR. In *2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 1007–1017, 2023.

[96] S. Li, N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. Data reduction techniques for simulation, visualization and data analysis. *Computer Graphics Forum*, 37(6):422–447, 2018.

[97] Y. Li, E. Perlman, M. Wan, Y. Yang, C. Meneveau, R. Burns, S. Chen, A. Szalay, and G. Eyink. A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence. *Journal of Turbulence*, (9):N31, 2008.

[98] X. Liang, S. Di, D. Tao, S. Li, S. Li, H. Guo, Z. Chen, and F. Cappello.
Error-controlled lossy compression optimized for high compression ratios of scientific datasets. In *2018 IEEE International Conference on Big Data*, IEEE, 2018.

[99] X. Liang, H. Guo, S. Di, F. Cappello, M. Raj, C. Liu, K. Ono, Z. Chen, and T. Peterka. Toward feature-preserving 2D and 3D vector field compression. In *2020 IEEE Pacific Visualization Symposium (PacificVis)*, pages 81–90, 2020.

[100] X. Liang, B. Whitney, J. Chen, L. Wan, Q. Liu, D. Tao, J. Kress, D. R. Pugmire, M. Wolf, N. Podhorszki, et al. MGARD+: optimizing multi-level methods for error-bounded scientific data reduction. *IEEE Transactions on Computers*, 2021.

[101] X. Liang, K. Zhao, S. Di, S. Li, R. Underwood, A. M. Gok, J. Tian, J. Deng, J. C. Calhoun, D. Tao, et al. SZ3: A modular framework for composing prediction-based error-bounded lossy compressors. *IEEE Transactions on Big Data*, 2022.

[102] D. A. Lifka. The ANL/IBM SP scheduling system. In D. G. Feitelson and L. Rudolph, editors, *Job Scheduling Strategies for Parallel Processing*, pages 295–303, Berlin, Heidelberg, 1995. Springer Berlin Heidelberg.

[103] P. Lindstrom. Fixed-rate compressed floating-point arrays. *IEEE Transactions on Visualization and Computer Graphics*, 20(12):2674–2683, 2014.

[104] P. Lindstrom and M. Isenburg. Fast and efficient compression of floating-point data. *IEEE Transactions on Visualization and Computer Graphics*, 12(5):1245–1250, 2006.

[105] P. Lindstrom and M. Isenburg. Fast and efficient compression of floating-point data. *IEEE Transactions on Visualization and Computer Graphics*, 12(5):1245–1250, 2006.

[106] J. Liu, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello. Dynamic quality metric oriented error bounded lossy compression for scientific datasets. In *2022 SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)*, pages 892–906. IEEE Computer Society, 2022.

[107] J. Liu, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello. FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data. In *Proceedings of the 37th International Conference on Supercomputing*, pages 1–13, 2023.

[108] W. Lotter, G. Kreiman, and D. Cox. Deep predictive coding networks for video prediction and unsupervised learning. *arXiv preprint arXiv:1605.08104*, 2016.

[109] E. Maisonnave, I. Fast, T. Jahns, J. Biercamp, S. Sénési, Y. Meurdesoif, and U. Fladrich. CDI-pio & XIOS I/O servers compatibility with HR climate models. Technical report, CERFACS, 2017.

[110] J. N. Maki, J. F. Bell III, K. E. Herkenhoff, S. W. Squyres, A. Kiely, M. Klimesh, M. Schwochert, T. Litwin, R. Willson, A. Johnson, M. Mai-mone, E. Baumgartner, A. Collins, M. Wadsworth, S. T. Elliot, A. Din-gizian, D. Brown, E. C. Hagerott, L. Scherr, R. Deen, D. Alexander, and J. Lorre. Mars exploration rover engineering cameras. *Journal of Geophysical Research: Planets*, 108(E12), 2003.

[111] M. Martel. Compressed matrix computations. In *2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)*, pages 68–76. IEEE, 2022.

[112] G. A. Meehl, R. Moss, K. E. Taylor, V. Eyring, R. J. Stouffer, S. Bony, and B. Stevens. Climate model intercomparisons: Preparing for the next phase. *Eos, Transactions American Geophysical Union*, 95(9):77–78, 2014.

[113] W. L. Miller, D. Bard, A. Boehnlein, K. Fagnan, C. Guok, E. Lançon, S. Ramprakash, M. Shankar, N. Schwarz, and B. L. Brown. Integrated research infrastructure architecture blueprint activity (final report 2023). 7 2023.

[114] Miranda Radiation Hydrodynamics Data. https://wci.lnl.gov/simulation/computer-codes/miranda, 2019. Online.

[115] NVIDIA. https://developer.nvidia.com/nvcomp. Online.

[116] Oak Ridge Leadership Computing Facility. WarpX, granted early access to the exascale supercomputer Frontier, receives the high-performance computing world's highest honor, 2023. https://www.olcf.ornl.gov/2022/11/17/plasma-simulation-code-wins-2022-acm-gordon-bell-prize/.

[117] A. Olmo, A. S. Zamzam, A. Glaws, and R. King. Physics-driven convolutional autoencoder approach for CFD data compressions: Preprint. Technical Report 1969095, U.S. Department of Energy Office of Scien-

tific and Technical Information, 4 2023.

[118] I. V. Oseledets. Tensor-train decomposition. *SIAM Journal on Scientific Computing*, 33(5):2295–2317, 2011.

[119] C. C. Paige and Z. Strakoš. Residual and backward error bounds in minimum residual Krylov subspace methods. *SIAM Journal on Scientific Computing*, 23(6):1898–1923, January 2002.

[120] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. In *Advances in neural information processing systems*, pages 8026–8037, 2019.

[121] A. Poppick, J. Nardi, N. Feldman, A. H. Baker, A. Pinard, and D. M. Hammerling. A statistical analysis of lossily compressed climate model data. *Computers and Geosciences*, 145:104599, 2020.

[122] S. Puri, S. Lasserre, and P. L. Callet. CNN-based transform index prediction in multiple transforms framework to assist entropy coding. In *2017 25th European Signal Processing Conference (EUSIPCO)*, pages 798–802, 2017.

[123] QMCPACK: many-body ab initio Quantum Monte Carlo code. http://vis.computer.org/vis2004contest/data.html, 2019. Online.

[124] M. Rahman, M. Islam, J. C. Calhoun, and M. Chowdhury. Dynamic error-bounded lossy compression (EBLC) to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications. *arXiv preprint arXiv:2002.03742*, 2020.

[125] B. Reagan, U. Gupta, B. Adolf, M. Mitzenmacher, A. Rush, G.-Y. Wei, and D. Brooks. Weightless: Lossy weight encoding for deep neural network compression. In *International Conference on Machine Learning*, pages 4321–4330, 2018.

[126] M. Rhu, N. Gimelshein, J. Clemons, A. Zulfiqar, and S. W. Keckler. vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. In *The 49th Annual IEEE/ACM International Symposium on Microarchitecture*, page 18. IEEE Press, 2016.

[127] R. Roy. Compression of time evolutionary image data through predictive deep neural networks. In *2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)*, pages 41–50, 2021.

[128] Y. Saad. A flexible inner-outer preconditioned GMRES algorithm. *SIAM Journal Scientific Computing*, 14:461–469, 1993.

[129] Y. Saad and M. H. Schultz. GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. *SIAM Journal on Scientific and Statistical Computing*, 7(3):856–869, 1986.

[130] E. R. Schendel, Y. Jin, N. Shah, J. Chen, C. Chang, S.-H. Ku, S. Ethier, S. Klasky, R. Latham, R. Ross, and N. F. Samatova. ISOBAR preconditioner for effective and high-throughput lossless data compression. In *2012 IEEE 28th International Conference on Data Engineering*, pages 138–149, 2012.

[131] A. Siegel, E. Draeger, J. Deslippe, T. Evans, M. Francois, T. C. Germann, D. F. Martin, and W. Hart. Application results on early exascale hardware. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2022.

[132] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. *arXiv preprint arXiv:1409.1556*, 2014.

[133] N. Srivastava, E. Mansimov, and R. Salakhudinov. Unsupervised learning of video representations using lstms. In *International Conference on Machine Learning*, pages 843–852, 2015.

[134] S. Strempfer, K. Yoshii, M. Hammer, D. Bycul, and A. Miceli. Designing a streaming data coalescing architecture for scientific detector ASICs with variable data velocity. In *2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)*, pages 8–14. IEEE, 2021.

[135] S. Strempfer, T. Zhou, K. Yoshii, M. Hammer, A. Babu, D. Bycul, J. Weizeorick, M. J. Cherukara, and A. Miceli. A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging. *Journal of Instrumentation*, 17(10):P10042, 2022.

[136] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In *Proceedings of the IEEE conference on Computer Vision and Pattern Recognition*, pages 1–9, 2015.

[137] I. Talukdar, A. Singh, R. Underwood, K. Sato, and W. Yu. Integrating tezip into libpressio: A case study of integrating a dynamic application into a static c environment. 2023.
Error-controlled lossy compression optimized for high compression ratios of scientific datasets. In *2018 IEEE International Conference on Big Data*, IEEE, 2018.

[99] X. Liang, H. Guo, S. Di, F. Cappello, M. Raj, C. Liu, K. Ono, Z. Chen, and T. Peterka. Toward feature-preserving 2D and 3D vector field compression. In *2020 IEEE Pacific Visualization Symposium (PacificVis)*, pages 81–90, 2020.

[100] X. Liang, B. Whitney, J. Chen, L. Wan, Q. Liu, D. Tao, J. Kress, D. R. Pugmire, M. Wolf, N. Podhorszki, et al. MGARD+: optimizing multi-level methods for error-bounded scientific data reduction. *IEEE Transactions on Computers*, 2021.

[101] X. Liang, K. Zhao, S. Di, S. Li, R. Underwood, A. M. Gok, J. Tian, J. Deng, J. C. Calhoun, D. Tao, et al. SZ3: A modular framework for composing prediction-based error-bounded lossy compressors. *IEEE Transactions on Big Data*, 2022.

[102] D. A. Lifka. The ANL/IBM SP scheduling system. In D. G. Feitelson and L. Rudolph, editors, *Job Scheduling Strategies for Parallel Processing*, pages 295–303, Berlin, Heidelberg, 1995. Springer Berlin Heidelberg.

[103] P. Lindstrom. Fixed-rate compressed floating-point arrays. *IEEE Transactions on Visualization and Computer Graphics*, 20(12):2674–2683, 2014.

[104] P. Lindstrom and M. Isenburg. Fast and efficient compression of floating-point data. *IEEE Transactions on Visualization and Computer Graphics*, 12(5):1245–1250, 2006.

[105] P. Lindstrom and M. Isenburg. Fast and efficient compression of floating-point data. *IEEE Transactions on Visualization and Computer Graphics*, 12(5):1245–1250, 2006.

[106] J. Liu, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello. Dynamic quality metric oriented error bounded lossy compression for scientific datasets. In *2022 SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)*, pages 892–906. IEEE Computer Society, 2022.

[107] J. Liu, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello. FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data. In *Proceedings of the 37th International Conference on Supercomputing*, pages 1–13, 2023.

[108] W. Lotter, G. Kreiman, and D. Cox. Deep predictive coding networks for video prediction and unsupervised learning. *arXiv preprint arXiv:1605.08104*, 2016.

[109] E. Maisonnave, I. Fast, T. Jahns, J. Biercamp, S. Sénési, Y. Meurdesoif, and U. Fladrich. CDI-pio & XIOS I/O servers compatibility with HR climate models. Technical report, CERFACS, 2017.

[110] J. N. Maki, J. F. Bell III, K. E. Herkenhoff, S. W. Suyres, A. Kiely, M. Klimesh, M. Schwochert, T. Litwin, R. Willson, A. Johnson, M. Mai-mone, E. Baumgartner, A. Collins, M. Wadsworth, S. T. Elliot, A. Din-gizian, D. Brown, E. C. Hagerott, L. Scherr, R. Deen, D. Alexander, and J. Lorre. Mars exploration rover engineering cameras. *Journal of Geophysical Research: Planets*, 108(E12), 2003.

[111] M. Martel. Compressed matrix computations. In *2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)*, pages 68–76. IEEE, 2022.

[112] G. A. Meehl, R. Moss, K. E. Taylor, V. Eyring, R. J. Stouffer, S. Bony, and B. Stevens. Climate model intercomparisons: Preparing for the next phase. *Eos, Transactions American Geophysical Union*, 95(9):77–78, 2014.

[113] W. L. Miller, D. Bard, A. Boehnlein, K. Fagnan, C. Guok, E. Lançon, S. Ramprakash, M. Shankar, N. Schwarz, and B. L. Brown. Integrated research infrastructure architecture blueprint activity (final report 2023). 7 2023.

[114] Miranda Radiation Hydrodynamics Data. https://wci.lnl.gov/simulation/computer-codes/miranda, 2019. Online.

[115] NVIDIA. https://developer.nvidia.com/nvcomp. Online.

[116] Oak Ridge Leadership Computing Facility. WarpX, granted early access to the exascale supercomputer Frontier, receives the high-performance computing world's highest honor, 2023. https://www.olcf.ornl.gov/2022/11/17/plasma-simulation-code-wins-2022-acm-gordon-bell-prize/.

[117] A. Olmo, A. S. Zamzam, A. Glaws, and R. King. Physics-driven convolutional autoencoder approach for CFD data compressions: Preprint. Technical Report 1969095, U.S. Department of Energy Office of Scien-

tific and Technical Information, 4 2023.

[118] I. V. Oseledets. Tensor-train decomposition. *SIAM Journal on Scientific Computing*, 33(5):2295–2317, 2011.

[119] C. C. Paige and Z. Strakoš. Residual and backward error bounds in minimum residual Krylov subspace methods. *SIAM Journal on Scientific Computing*, 23(6):1898–1923, January 2002.

[120] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. In *Advances in neural information processing systems*, pages 8026–8037, 2019.

[121] A. Poppick, J. Nardi, N. Feldman, A. H. Baker, A. Pinard, and D. M. Hammerling. A statistical analysis of lossily compressed climate model data. *Computers and Geosciences*, 145:104599, 2020.

[122] S. Puri, S. Lasserre, and P. L. Callet. CNN-based transform index prediction in multiple transforms framework to assist entropy coding. In *2017 25th European Signal Processing Conference (EUSIPCO)*, pages 798–802, 2017.

[123] QMCPACK: many-body ab initio Quantum Monte Carlo code. http://vis.computer.org/vis2004contest/data.html, 2019. Online.

[124] M. Rahman, M. Islam, J. C. Calhoun, and M. Chowdhury. Dynamic error-bounded lossy compression (EBLC) to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications. *arXiv preprint arXiv:2002.03742*, 2020.

[125] B. Reagan, U. Gupta, B. Adolf, M. Mitzenmacher, A. Rush, G.-Y. Wei, and D. Brooks. Weightless: Lossy weight encoding for deep neural network compression. In *International Conference on Machine Learning*, pages 4321–4330, 2018.

[126] M. Rhu, N. Gimelshein, J. Clemons, A. Zulfiqar, and S. W. Keckler. vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. In *The 49th Annual IEEE/ACM International Symposium on Microarchitecture*, page 18. IEEE Press, 2016.

[127] R. Roy. Compression of time evolutionary image data through predictive deep neural networks. In *2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)*, pages 41–50, 2021.

[128] Y. Saad. A flexible inner-outer preconditioned GMRES algorithm. *SIAM Journal Scientific Computing*, 14:461–469, 1993.

[129] Y. Saad and M. H. Schultz. GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. *SIAM Journal on Scientific and Statistical Computing*, 7(3):856–869, 1986.

[130] E. R. Schendel, Y. Jin, N. Shah, J. Chen, C. Chang, S.-H. Ku, S. Ethier, S. Klasky, R. Latham, R. Ross, and N. F. Samatova. ISOBAR preconditioner for effective and high-throughput lossless data compression. In *2012 IEEE 28th International Conference on Data Engineering*, pages 138–149, 2012.

[131] A. Siegel, E. Draeger, J. Deslippe, T. Evans, M. Francois, T. C. Germann, D. F. Martin, and W. Hart. Application results on early exascale hardware. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2022.

[132] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. *arXiv preprint arXiv:1409.1556*, 2014.

[133] N. Srivastava, E. Mansimov, and R. Salakhudinov. Unsupervised learning of video representations using lstms. In *International Conference on Machine Learning*, pages 843–852, 2015.

[134] S. Strempfer, K. Yoshii, M. Hammer, D. Bycul, and A. Miceli. Designing a streaming data coalescing architecture for scientific detector ASICs with variable data velocity. In *2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)*, pages 8–14. IEEE, 2021.

[135] S. Strempfer, T. Zhou, K. Yoshii, M. Hammer, A. Babu, D. Bycul, J. Weizeorick, M. J. Cherukara, and A. Miceli. A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging. *Journal of Instrumentation*, 17(10):P10042, 2022.

[136] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In *Proceedings of the IEEE conference on Computer Vision and Pattern Recognition*, pages 1–9, 2015.

[137] I. Talukdar, A. Singh, R. Underwood, K. Sato, and W. Yu. Integrating tezip into libpressio: A case study of integrating a dynamic application into a static c environment. 2023.
[138] H. Tang, Q. Koziol, J. Ravi, and S. Byna. Transparent asynchronous parallel I/O using background threads. *IEEE Transactions on Parallel and Distributed Systems*, 33(4):891–902, 2021.

[139] D. Tao, S. Di, Z. Chen, and F. Cappello. Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization. In *2017 IEEE International Parallel and Distributed Processing Symposium*, pages 1129–1139. IEEE, 2017.

[140] D. Tao, S. Di, H. Guo, Z. Chen, and F. Cappello. Z-checker: A framework for assessing lossy compression of scientific data. *Int. J. High Perform. Comput. Appl.*, 33(2):285–303, March 2019.

[141] D. S. Taubman, M. W. Marcellin, and M. Rabbani. JPEG2000: Image compression fundamentals, standards and practice. *Journal of Electronic Imaging*, 11(2):286–287, 2002.

[142] The HDF Group. Hierarchical data format version 5. http://www.hdfgroup.org/HDF.

[143] J. Tian, S. Di, X. Yu, C. Rivera, K. Zhao, S. Jin, Y. Feng, X. Liang, D. Tao, and F. Cappello. Optimizing error-bounded lossy compression for scientific data on GPUs. In *2021 IEEE International Conference on Cluster Computing (CLUSTER)*, pages 283–293, 2021.

[144] J. Tian, S. Di, C. Zhang, X. Liang, S. Jin, D. Cheng, D. Tao, and F. Cappello. WaveSZ: A hardware-algorithm co-design of efficient lossy compression for scientific data. In *Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP'20*, page 74–88, New York, NY, USA, 2020. Association for Computing Machinery.

[145] J. Tian, S. Di, K. Zhao, C. Rivera, M. H. Fulp, R. Underwood, S. Jin, X. Liang, J. Calhoun, D. Tao, and F. Cappello. cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data. In *Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques, PACT'20*, page 3–15, New York, NY, USA, 2020. Association for Computing Machinery.

[146] S.-M. Tseng, B. Nicolae, F. Cappello, and A. Chandramowlishwaran. Demystifying asynchronous I/O Interference in HPC applications. *The International Journal of High Performance Computing Applications*, 35(4):391–412, 2021.

[147] T. Ueno, K. Sano, and S. Yamamoto. Bandwidth compression of floating-point numerical data streams for FPGA-based high-performance computing. *ACM Transactions on Reconfigurable Technology and Systems*, 10(3):1–22, July 2017.

[148] R. Underwood, J. Bessac, S. Di, and F. Cappello. Understanding the effects of modern compressors on the Community Earth Science Model. In *2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD)*, pages 1–10, 2022.

[149] R. Underwood, V. Malvoso, J. C. Calhoun, S. Di, and F. Cappello. Productive and performant generic lossy data compression with libpresso. In *2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)*, pages 1–10. IEEE, 2021.

[150] R. Underwood, C. Yoon, A. Gok, S. Di, and F. Cappello. ROIBIN-SZ: Fast and science-preserving compression for serial crystallography, 2022.

[151] H. F. Walker. Implementation of the GMRES method using Householder transformations. *SIAM Journal on Scientific Computing*, 9(1):152–163, 1988.

[152] H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In *Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining*, pages 1235–1244. ACM, 2015.

[153] L. Wang, J. Ye, Y. Zhao, W. Wu, A. Li, S. L. Song, Z. Xu, and T. Kraska. Superneurons: dynamic GPU memory management for training deep neural networks. In *Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming*, pages 41–53. ACM, 2018.

[154] L. Yan, X. Liang, H. Guo, and B. Wang. TopoSZ: Preserving topology in error-bounded lossy compression. *IEEE Transactions on Visualization and Computer Graphics*, pages 1–11, 2023.

[155] X. Yepes-Arbós, G. van den Oord, M. C. Acosta, and G. D. Carver. Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study. *Geoscientific Model Development*, 15(2):379–394, 2022.

[156] K. Yoshii. Streampressor: Stream compressor hardware generator.

https://github.com/hwspec/StreamPressor, 2023.

[157] K. Yoshii, T. Ueno, K. Sano, A. Miceli, and F. Cappello. Streaming hardware compressor generator framework. In *Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis*, pages 289–297, 2023.

[158] X. Yu, S. Di, A. M. Gok, D. Tao, and F. Cappello. cuZ-Checker: A GPU-based ultra-fast assessment system for lossy compressions. In *2021 IEEE International Conference on Cluster Computing (CLUSTER)*, pages 307–319, 2021.

[159] C. S. Zender. Bit grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF operators (NCO, v4.4.8+). *Geoscientific Model Development*, 9(9):3199–3211, 2016.

[160] W. Zhang, A. Almgren, V. Beckner, J. Bell, J. Blaschke, C. Chan, M. Day, B. Friesen, K. Gott, D. Graves, et al. AMReX: a framework for block-structured adaptive mesh refinement. *The Journal of Open Source Software*, 4(37):1370, 2019.

[161] K. Zhao, S. Di, M. Dmitriev, T.-L. D. Tonellot, Z. Chen, and F. Cappello. Optimizing error-bounded lossy compression for scientific data by dynamic spline interpolation. In *2021 IEEE 37th International Conference on Data Engineering (ICDE)*, pages 1643–1654, 2021.

[162] K. Zhao, S. Di, X. Lian, S. Li, D. Tao, J. Bessac, Z. Chen, and F. Cappello. SDRBench: Scientific data reduction benchmark for lossy compressors. In *2020 IEEE International Conference on Big Data (Big Data)*, pages 2716–2724, Los Alamitos, CA, USA, dec 2020. IEEE Computer Society.

[163] K. Zhao, S. Di, D. Perez, X. Liang, Z. Chen, and F. Cappello. MDZ: An efficient error-bounded lossy compressor for molecular dynamics. In *2022 IEEE 38th International Conference on Data Engineering (ICDE)*, pages 27–40, 2022.

[164] L. Zhao, S. Wang, X. Zhang, S. Wang, S. Ma, and W. Gao. Enhanced CTU-level inter prediction with deep frame rate up-conversion for high efficiency video coding. In *2018 25th IEEE International Conference on Image Processing (ICIP)*, pages 206–210, 2018.

[165] H. Zheng, V. Vishwanath, Q. Koziol, H. Tang, J. Ravi, J. Mainzer, and S. Byna. HDFS Cache VOL: Efficient and scalable parallel I/O through caching data on node-local storage. In *2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)*, pages 61–70. IEEE, 2022.

[166] Q. Zhou, Q. Anthony, L. Xu, A. Shafi, M. Abduljabbar, H. Subramoni, and D. K. D. Panda. Accelerating distributed deep learning training with compression assisted allgather and reduce-scatter communication. In *2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 134–144, 2023.

[167] Q. Zhou, P. Kousha, Q. Anthony, K. Shafie Khorassani, A. Shafi, H. Subramoni, and D. K. Panda. Accelerating MPI all-to-all communication with online compression on modern GPU clusters. In A.-L. Varbanescu, A. Bhatele, P. Luszczek, and B. Marc, editors, *High Performance Computing*, pages 3–25, Cham, 2022. Springer International Publishing.
[138] H. Tang, Q. Koziol, J. Ravi, and S. Byna. Transparent asynchronous parallel I/O using background threads. *IEEE Transactions on Parallel and Distributed Systems*, 33(4):891–902, 2021.

[139] D. Tao, S. Di, Z. Chen, and F. Cappello. Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization. In *2017 IEEE International Parallel and Distributed Processing Symposium*, pages 1129–1139. IEEE, 2017.

[140] D. Tao, S. Di, H. Guo, Z. Chen, and F. Cappello. Z-checker: A framework for assessing lossy compression of scientific data. *Int. J. High Perform. Comput. Appl.*, 33(2):285–303, March 2019.

[141] D. S. Taubman, M. W. Marcellin, and M. Rabbani. JPEG2000: Image compression fundamentals, standards and practice. *Journal of Electronic Imaging*, 11(2):286–287, 2002.

[142] The HDF Group. Hierarchical data format version 5. http://www.hdfgroup.org/HDF.

[143] J. Tian, S. Di, X. Yu, C. Rivera, K. Zhao, S. Jin, Y. Feng, X. Liang, D. Tao, and F. Cappello. Optimizing error-bounded lossy compression for scientific data on GPUs. In *2021 IEEE International Conference on Cluster Computing (CLUSTER)*, pages 283–293, 2021.

[144] J. Tian, S. Di, C. Zhang, X. Liang, S. Jin, D. Cheng, D. Tao, and F. Cappello. WaveSZ: A hardware-algorithm co-design of efficient lossy compression for scientific data. In *Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP'20*, page 74–88, New York, NY, USA, 2020. Association for Computing Machinery.

[145] J. Tian, S. Di, K. Zhao, C. Rivera, M. H. Fulp, R. Underwood, S. Jin, X. Liang, J. Calhoun, D. Tao, and F. Cappello. cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data. In *Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques, PACT'20*, page 3–15, New York, NY, USA, 2020. Association for Computing Machinery.

[146] S.-M. Tseng, B. Nicolae, F. Cappello, and A. Chandramowlishwaran. Demystifying asynchronous I/O Interference in HPC applications. *The International Journal of High Performance Computing Applications*, 35(4):391–412, 2021.

[147] T. Ueno, K. Sano, and S. Yamamoto. Bandwidth compression of floating-point numerical data streams for FPGA-based high-performance computing. *ACM Transactions on Reconfigurable Technology and Systems*, 10(3):1–22, July 2017.

[148] R. Underwood, J. Bessac, S. Di, and F. Cappello. Understanding the effects of modern compressors on the Community Earth Science Model. In *2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD)*, pages 1–10, 2022.

[149] R. Underwood, V. Malvoso, J. C. Calhoun, S. Di, and F. Cappello. Productive and performant generic lossy data compression with libpresso. In *2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)*, pages 1–10. IEEE, 2021.

[150] R. Underwood, C. Yoon, A. Gok, S. Di, and F. Cappello. ROIBIN-SZ: Fast and science-preserving compression for serial crystallography, 2022.

[151] H. F. Walker. Implementation of the GMRES method using Householder transformations. *SIAM Journal on Scientific Computing*, 9(1):152–163, 1988.

[152] H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In *Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining*, pages 1235–1244. ACM, 2015.

[153] L. Wang, J. Ye, Y. Zhao, W. Wu, A. Li, S. L. Song, Z. Xu, and T. Kraska. Superneurons: dynamic GPU memory management for training deep neural networks. In *Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming*, pages 41–53. ACM, 2018.

[154] L. Yan, X. Liang, H. Guo, and B. Wang. TopoSZ: Preserving topology in error-bounded lossy compression. *IEEE Transactions on Visualization and Computer Graphics*, pages 1–11, 2023.

[155] X. Yepes-Arbós, G. van den Oord, M. C. Acosta, and G. D. Carver. Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study. *Geoscientific Model Development*, 15(2):379–394, 2022.

[156] K. Yoshii. Streampressor: Stream compressor hardware generator.

https://github.com/hwspec/StreamPressor, 2023.

[157] K. Yoshii, T. Ueno, K. Sano, A. Miceli, and F. Cappello. Streaming hardware compressor generator framework. In *Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis*, pages 289–297, 2023.

[158] X. Yu, S. Di, A. M. Gok, D. Tao, and F. Cappello. cuZ-Checker: A GPU-based ultra-fast assessment system for lossy compressions. In *2021 IEEE International Conference on Cluster Computing (CLUSTER)*, pages 307–319, 2021.

[159] C. S. Zender. Bit grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF operators (NCO, v4.4.8+). *Geoscientific Model Development*, 9(9):3199–3211, 2016.

[160] W. Zhang, A. Almgren, V. Beckner, J. Bell, J. Blaschke, C. Chan, M. Day, B. Friesen, K. Gott, D. Graves, et al. AMReX: a framework for block-structured adaptive mesh refinement. *The Journal of Open Source Software*, 4(37):1370, 2019.

[161] K. Zhao, S. Di, M. Dmitriev, T.-L. D. Tonellot, Z. Chen, and F. Cappello. Optimizing error-bounded lossy compression for scientific data by dynamic spline interpolation. In *2021 IEEE 37th International Conference on Data Engineering (ICDE)*, pages 1643–1654, 2021.

[162] K. Zhao, S. Di, X. Lian, S. Li, D. Tao, J. Bessac, Z. Chen, and F. Cappello. SDRBench: Scientific data reduction benchmark for lossy compressors. In *2020 IEEE International Conference on Big Data (Big Data)*, pages 2716–2724, Los Alamitos, CA, USA, dec 2020. IEEE Computer Society.

[163] K. Zhao, S. Di, D. Perez, X. Liang, Z. Chen, and F. Cappello. MDZ: An efficient error-bounded lossy compressor for molecular dynamics. In *2022 IEEE 38th International Conference on Data Engineering (ICDE)*, pages 27–40, 2022.

[164] L. Zhao, S. Wang, X. Zhang, S. Wang, S. Ma, and W. Gao. Enhanced CTU-level inter prediction with deep frame rate up-conversion for high efficiency video coding. In *2018 25th IEEE International Conference on Image Processing (ICIP)*, pages 206–210, 2018.

[165] H. Zheng, V. Vishwanath, Q. Koziol, H. Tang, J. Ravi, J. Mainzer, and S. Byna. HDFS Cache VOL: Efficient and scalable parallel I/O through caching data on node-local storage. In *2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)*, pages 61–70. IEEE, 2022.

[166] Q. Zhou, Q. Anthony, L. Xu, A. Shafi, M. Abduljabbar, H. Subramoni, and D. K. D. Panda. Accelerating distributed deep learning training with compression assisted allgather and reduce-scatter communication. In *2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)*, pages 134–144, 2023.

[167] Q. Zhou, P. Kousha, Q. Anthony, K. Shafie Khorassani, A. Shafi, H. Subramoni, and D. K. Panda. Accelerating MPI all-to-all communication with online compression on modern GPU clusters. In A.-L. Varbanescu, A. Bhatele, P. Luszczek, and B. Marc, editors, *High Performance Computing*, pages 3–25, Cham, 2022. Springer International Publishing.
Author Biography

Franck Cappello is senior computer scientist and R&D lead at Argonne National laboratory. From 2016 and with the support of ECP (US Exascale Computing Project), he started exploring lossy compression for scientific computing. This research produced the SZ lossy compressor, the Libpressio unifying API, the Z-checker tool to assess the nature of lossy compression errors and the SDRbench repository of reference scientific datasets. Franck is IEEE fellow and recipient of the 2024 IEEE Charles Babbage Award, the 2024 Europar achievement award, the 2022 HPDC achievement award, the 2018 IEEE TCPP Outstanding Service Award, the 2021 IEEE Transactions in Computers Award for Editorial Service and Excellence and two prestigious R&D100 awards (2019 and 2021).

Frédéric Vivien graduated in Computer Sciences and received his PhD from École Normale Supérieure de Lyon, France, in 1997. From 1998 to 2002, he was an associate professor at the Louis Pasteur University in Strasbourg, France. He is currently an INRIA senior researcher at ENS Lyon, France. His main research interests include parallel computing, scheduling, and resilience techniques. He is the author of two books.

Kazutomo Yoshii is a principal experimental systems specialist at Argonne National Laboratory. He earned an M.S. in computer science from Toyohashi University of Technology, Japan, in 1994. His career began with developing medical imaging analysis software for functional MRI images at Hitachi's research facility in Japan. In 1998, he joined Turbolinux, contributing to the Linux operating system in Japan and Santa Fe, New Mexico. Later, in 2002, he focused on dynamic provisioning systems for cluster systems at Mountain View Data. Since 2004, he has been with Argonne National Laboratory, actively engaged in co-design activities for supercomputers and experimental systems. His recent work involves investigating on-chip processing digital logic for scientific detectors in collaboration with domain scientists at Argonne. His extensive research interests include power/thermal-aware computing, reconfigurable dataflow computing, edge computing, AI accelerators, and hardware specialization.

Xavier Yepes-Arbós is a research engineer of the Earth Sciences Department at the Barcelona Supercomputing Center (BSC) where he co-leads the High-Performance Computing for Earth Sciences Team. He holds a bachelor's degree in computer engineering with specialization in computer architecture and a master's degree in Innovation and Research in Informatics (MIRI) with specialization in HPC from the Universitat Politècnica de Catalunya (UPC). He joined BSC in 2015 to work on different research lines, including scalability and performance analysis of Earth system models (ESMs), optimization of the computational and I/O processes, data compression and explore novel hardware architectures.

Mario C. Acosta is a leader researcher the Computational Group of the Earth Sciences Department at the Barcelona Supercomputing Center. He received his PhD in Computer Science from University of Granada in 2015. He integrated the BSC eight years ago, assuming the leadership of the computational group one year ago. Dr Acosta's research lines are well embodied in those related to High Performance Computing (HPC) applied to Earth System Models He is the coordinator of the HorizonE ESiWACE3: Center of excellence for weather and climate phase 3 and WP leader in the development of the climate digital twin in Destination Earth.

Amarjit Singh is working as Postdoctoral researcher at High Performance Big Data Research Team in the Center for Computational Science at RIKEN (RIKEN R-CCS). He received his Ph.D. in Computer Science
Author Biography

Franck Cappello is senior computer scientist and R&D lead at Argonne National laboratory. From 2016 and with the support of ECP (US Exascale Computing Project), he started exploring lossy compression for scientific computing. This research produced the SZ lossy compressor, the Libpressio unifying API, the Z-checker tool to assess the nature of lossy compression errors and the SDRbench repository of reference scientific datasets. Franck is IEEE fellow and recipient of the 2024 IEEE Charles Babbage Award, the 2024 Europar achievement award, the 2022 HPDC achievement award, the 2018 IEEE TCPP Outstanding Service Award, the 2021 IEEE Transactions in Computers Award for Editorial Service and Excellence and two prestigious R&D100 awards (2019 and 2021).

Frédéric Vivien graduated in Computer Sciences and received his PhD from École Normale Supérieure de Lyon, France, in 1997. From 1998 to 2002, he was an associate professor at the Louis Pasteur University in Strasbourg, France. He is currently an INRIA senior researcher at ENS Lyon, France. His main research interests include parallel computing, scheduling, and resilience techniques. He is the author of two books.

Kazutomo Yoshii is a principal experimental systems specialist at Argonne National Laboratory. He earned an M.S. in computer science from Toyohashi University of Technology, Japan, in 1994. His career began with developing medical imaging analysis software for functional MRI images at Hitachi's research facility in Japan. In 1998, he joined Turbolinux, contributing to the Linux operating system in Japan and Santa Fe, New Mexico. Later, in 2002, he focused on dynamic provisioning systems for cluster systems at Mountain View Data. Since 2004, he has been with Argonne National Laboratory, actively engaged in co-design activities for supercomputers and experimental systems. His recent work involves investigating on-chip processing digital logic for scientific detectors in collaboration with domain scientists at Argonne. His extensive research interests include power/thermal-aware computing, reconfigurable dataflow computing, edge computing, AI accelerators, and hardware specialization.

Xavier Yepes-Arbós is a research engineer of the Earth Sciences Department at the Barcelona Supercomputing Center (BSC) where he co-leads the High-Performance Computing for Earth Sciences Team. He holds a bachelor's degree in computer engineering with specialization in computer architecture and a master's degree in Innovation and Research in Informatics (MIRI) with specialization in HPC from the Universitat Politècnica de Catalunya (UPC). He joined BSC in 2015 to work on different research lines, including scalability and performance analysis of Earth system models (ESMs), optimization of the computational and I/O processes, data compression and explore novel hardware architectures.

Mario C. Acosta is a leader researcher the Computational Group of the Earth Sciences Department at the Barcelona Supercomputing Center. He received his PhD in Computer Science from University of Granada in 2015. He integrated the BSC eight years ago, assuming the leadership of the computational group one year ago. Dr Acosta's research lines are well embodied in those related to High Performance Computing (HPC) applied to Earth System Models He is the coordinator of the HorizonE ESiWACE3: Center of excellence for weather and climate phase 3 and WP leader in the development of the climate digital twin in Destination Earth.

Amarjit Singh is working as Postdoctoral researcher at High Performance Big Data Research Team in the Center for Computational Science at RIKEN (RIKEN R-CCS). He received his Ph.D. in Computer Science
from IKG Punjab Technical University, INDIA in 2022 and M.Sc. from Guru Nanak Dev University, INDIA in 2008. His main research interests include high performance computing, data compression, deep learning.

Kento Sato is a team leader of High-Performance Big Data Research Team in the Center for Computational Science at RIKEN (RIKEN R-CCS). His research area is distributed systems and parallel computing, particularly in High Performance Computing (HPC). Major focuses of his research are artificial intelligence, machine learning and deep learning in HPC, application reproducibility (MPI reproducibility, and Validation), scalable fault tolerance (Scalable checkpoint/restart, Fault tolerant MPI, Resilient system design), and I/O optimization (NVRAM, Burst buffer, and Big data), co-designing and cloud computing. He received his Ph.D. in the Dept. of Mathematical & Computing Sciences at Tokyo Tech in 2014, his M.S. in the Dept. of Mathematical & Computing Sciences at Tokyo Tech in 2010, and his B.S. in the Dept. of Information Science at Tokyo Tech in 2008.

Dingwen Tao is an associate professor at Indiana University Bloomington, where he directs the High-Performance Data Analytics and Computing Lab. He received his Ph.D. in Computer Science from University of California, Riverside in 2018 and B.S. in Mathematics from University of Science and Technology of China in 2013. He is the recipient of various awards including NSF CAREER Award (2023), Amazon Research Award (2022), Meta Research Award (2022), R&D100 Awards Winner (2021), IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in HPC (2020), NSF CRII Award (2020), and IEEE CLUSTER Best Paper Award (2018). He is serving as an Associate Editor of IEEE Transactions on Parallel and Distributed Systems.

Jiannan Tian is a PhD student in Intelligent Systems Engineering at Indiana University Bloomington. His research interests include scientific data compression, error analysis, and GPU-centric computing. His ongoing projects include developing GPU-accelerated compression algorithms and system design optimization for scientific compression. He has been in a long-term internship at Argonne National Laboratory.

Boyuan Zhang is a PhD student in Intelligent Systems Engineering at Indiana University Bloomington. His research interests encompass GPU computing, data compression, and quantum computing. He has completed a summer internship at Pacific Northwest National Laboratory in 2023.

Sheng Di (ANL) is a Computer Scientist in the Mathematics and Computer Science (MCS) division of Argonne National Laboratory. His current research interest includes lossy compression for scientific datasets, high performance computing, scalable computing, and fault tolerance. He is a senior member of IEEE, institute fellow of NAISE, also the scientist at Large through the Consortium for Advanced Science and Engineering (CASE) at the University of Chicago. He is the DOE 2021 Early Career Research Program Award Winner, and the recipient of 2018 IEEE-Chicago Distinguished Mentoring Award and 2019 IEEE-Chicago Distinguished R\&D Award. See https://www.mcs.anl.gov/~shdi for further information.

Sian Jin is an assistant professor at Temple University. He received his Ph.D. in Computer Engineering from Indiana University in 2023 and B.S. in Physics from Beijing Normal University in 2018. His research interests include high performance computing, data compression, neural networks, and parallel
from IKG Punjab Technical University, INDIA in 2022 and M.Sc. from Guru Nanak Dev University, INDIA in
2008. His main research interests include high performance computing, data compression, deep
learning.

Kento Sato is a team leader of High-Performance Big Data Research Team in the Center for Computational Science at RIKEN (RIKEN R-CCS). His research area is distributed systems and parallel computing, particularly in High Performance Computing (HPC). Major focuses of his research are artificial intelligence, machine learning and deep learning in HPC, application reproducibility (MPI reproducibility, and Validation), scalable fault tolerance (Scalable checkpoint/restart, Fault tolerant MPI, Resilient system design), and I/O optimization (NVRAM, Burst buffer, and Big data), co-designing and cloud computing. He received his Ph.D. in the Dept. of Mathematical & Computing Sciences at Tokyo Tech in 2014, his M.S. in the Dept. of Mathematical & Computing Sciences at Tokyo Tech in 2010, and his B.S. in the Dept. of Information Science at Tokyo Tech in 2008.

Dingwen Tao is an associate professor at Indiana University Bloomington, where he directs the High-Performance Data Analytics and Computing Lab. He received his Ph.D. in Computer Science from University of California, Riverside in 2018 and B.S. in Mathematics from University of Science and Technology of China in 2013. He is the recipient of various awards including NSF CAREER Award (2023), Amazon Research Award (2022), Meta Research Award (2022), R&D100 Awards Winner (2021), IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in HPC (2020), NSF CRII Award (2020), and IEEE CLUSTER Best Paper Award (2018). He is serving as an Associate Editor of IEEE Transactions on Parallel and Distributed Systems.

Jiannan Tian is a PhD student in Intelligent Systems Engineering at Indiana University Bloomington. His research interests include scientific data compression, error analysis, and GPU-centric computing. His ongoing projects include developing GPU-accelerated compression algorithms and system design optimization for scientific compression. He has been in a long-term internship at Argonne National Laboratory.

Boyuan Zhang is a PhD student in Intelligent Systems Engineering at Indiana University Bloomington. His research interests encompass GPU computing, data compression, and quantum computing. He has completed a summer internship at Pacific Northwest National Laboratory in 2023.

Sheng Di (ANL) is a Computer Scientist in the Mathematics and Computer Science (MCS) division of Argonne National Laboratory. His current research interest includes lossy compression for scientific datasets, high performance computing, scalable computing, and fault tolerance. He is a senior member of IEEE, institute fellow of NAISE, also the scientist at Large through the Consortium for Advanced Science and Engineering (CASE) at the University of Chicago. He is the DOE 2021 Early Career Research Program Award Winner, and the recipient of 2018 IEEE-Chicago Distinguished Mentoring Award and 2019 IEEE-Chicago Distinguished R\&D Award. See https://www.mcs.anl.gov/~shdi for further information.

Sian Jin is an assistant professor at Temple University. He received his Ph.D. in Computer Engineering from Indiana University in 2023 and B.S. in Physics from Beijing Normal University in 2018. His research interests include high performance computing, data compression, neural networks, and parallel
computing. He has published several papers in major journals and international conferences including the SC, PPoPP, VLDB, EuroSys, HPDC, IPDPS, and ICDE. Email: sian.jin@temple.edu.

Robert Underwood is a Post-Doctoral Appointee in the Mathematics and Computer Science Division at Argonne National Laboratory focusing on using data compression to accelerate I/O for large-scale scientific applications including AI for Science. His library LibPressio, which allows users to experiment and adopt advanced compressors quickly, has over 200 average unique monthly downloads, is used in over 17 institutions worldwide, and is a contributor to the R&D100 winning SZ family of compressors and other compression libraries. He regularly mentors students and is the early career ambassador for Argonne to the Joint Laboratory for Extreme Scale Computing.

Tomohiro Ueno is currently a research scientist in the processor research team at the Riken Center for Computational Science. He received his master's and Ph.D. degrees from the graduate school of information sciences at Tohoku University in 2016. Since then, he has been a postdoctoral researcher at the Graduate School of Engineering, Tohoku University, as a member of the ImPACT project until 2017 and RIKEN Center for Computational Science until 2022. His research interests include hardware architectures, hardware algorithms, data compression, and communication networks for high-performance computing.

Hartwig Anzt is a Full Professor at the School of Computation, Information and Technology at the Technical University of Munich. He also holds a Research Professor Position at the University of Tennessee in Knoxville. He preciously served as the director of the University of Tennessee's Innovative Computing Lab after holding a Junior Professor at the Karlsruhe Institute of Technology. Hartwig Anzt's research focuses on iterative methods and preconditioning techniques for the next generation hardware architectures. He also has a long track record of high-quality development. He is author of the MAGMA-sparse open-source software package and managing lead of the Ginkgo math software library. He also is a PI in the EuroHPC Project MICROCARD.

Thomas Grützmacher is a Ph.D. researcher at the Karlsruhe Institute of Technology. He received a bachelor's degree from the KIT in 2015 with an emphasis on software development for mobile computing and IoT. In 2018 he completed his Master's studies with an emphasis on High-Performance Computing and unconventional precision formats. Thomas Grützmacher's research focus is on designing a modular precision ecosystem. He also is among the core developer team of the Ginkgo open-source library.

Emmanuel Agullo is a researcher at Inria in the Concace joint project with Airbus CR&T and Cerfacs. His main research interests are parallel computing, parallel task programming, parallel composability and numerical linear algebra.

Luc Giraud is a researcher at Inria in the Concace joint project with Airbus CR&T and Cerfacs. His main research interests are in parallel scientific computing and numerical linear algebra for large scale applications.
computing. He has published several papers in major journals and international conferences including the SC, PPoPP, VLDB, EuroSys, HPDC, IPDPS, and ICDE. Email: sian.jin@temple.edu.

Robert Underwood is a Post-Doctoral Appointee in the Mathematics and Computer Science Division at Argonne National Laboratory focusing on using data compression to accelerate I/O for large-scale scientific applications including AI for Science. His library LibPressio, which allows users to experiment and adopt advanced compressors quickly, has over 200 average unique monthly downloads, is used in over 17 institutions worldwide, and is a contributor to the R&D100 winning SZ family of compressors and other compression libraries. He regularly mentors students and is the early career ambassador for Argonne to the Joint Laboratory for Extreme Scale Computing.

Tomohiro Ueno is currently a research scientist in the processor research team at the Riken Center for Computational Science. He received his master's and Ph.D. degrees from the graduate school of information sciences at Tohoku University in 2016. Since then, he has been a postdoctoral researcher at the Graduate School of Engineering, Tohoku University, as a member of the ImPACT project until 2017 and RIKEN Center for Computational Science until 2022. His research interests include hardware architectures, hardware algorithms, data compression, and communication networks for high-performance computing.

Hartwig Anzt is a Full Professor at the School of Computation, Information and Technology at the Technical University of Munich. He also holds a Research Professor Position at the University of Tennessee in Knoxville. He preciously served as the director of the University of Tennessee's Innovative Computing Lab after holding a Junior Professor at the Karlsruhe Institute of Technology. Hartwig Anzt's research focuses on iterative methods and preconditioning techniques for the next generation hardware architectures. He also has a long track record of high-quality development. He is author of the MAGMA-sparse open-source software package and managing lead of the Ginkgo math software library. He also is a PI in the EuroHPC Project MICROCARD.

Thomas Grützmacher is a Ph.D. researcher at the Karlsruhe Institute of Technology. He received a bachelor's degree from the KIT in 2015 with an emphasis on software development for mobile computing and IoT. In 2018 he completed his Master's studies with an emphasis on High-Performance Computing and unconventional precision formats. Thomas Grützmacher's research focus is on designing a modular precision ecosystem. He also is among the core developer team of the Ginkgo open-source library.

Emmanuel Agullo is a researcher at Inria in the Concace joint project with Airbus CR&T and Cerfacs. His main research interests are parallel computing, parallel task programming, parallel composability and numerical linear algebra.

Luc Giraud is a researcher at Inria in the Concace joint project with Airbus CR&T and Cerfacs. His main research interests are in parallel scientific computing and numerical linear algebra for large scale applications.
Luc Giraud, Emmanuel Agullo,

<figure><img src="image_28.png" /><figcaption>Franck Cappello</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Sheng Di</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Robert Underwood</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>Frederic Vivien</figcaption></figure>

<figure><img src="image_32.png" /></figure>
Luc Giraud, Emmanuel Agullo,

<figure><img src="image_28.png" /><figcaption>Franck Cappello</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Sheng Di</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Robert Underwood</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>Frederic Vivien</figcaption></figure>

<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /><figcaption>Kazutomo Yoshii</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Xavier Yepes</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Mario Acosta</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>Amarjit Singh</figcaption></figure>

<figure><img src="image_37.png" /></figure>
<figure><img src="image_33.png" /><figcaption>Kazutomo Yoshii</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Xavier Yepes</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Mario Acosta</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>Amarjit Singh</figcaption></figure>

<figure><img src="image_37.png" /></figure>
<figure><img src="image_38.png" /><figcaption>Kento Sato</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>Dingwen Tao</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>Boyuan Zhang</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>Jiannan Tian</figcaption></figure>

<figure><img src="image_42.png" /></figure>
<figure><img src="image_38.png" /><figcaption>Kento Sato</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>Dingwen Tao</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>Boyuan Zhang</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>Jiannan Tian</figcaption></figure>
<figure><img src="image_43.png" /><figcaption>Sian Jin</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>Tomohiro Ueno</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>Hartwig Anzt</figcaption></figure>
<figure><img src="image_42.png" /><figcaption>Sian Jin</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>Tomohiro Ueno</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>Hartwig Anzt</figcaption></figure>

<figure><img src="image_45.png" /></figure>
<figure><img src="image_46.png" /><figcaption>Thomas Grützmache</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>Luc Giraud</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>Emmanuel Agullo</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>Thomas Grützmache</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>Luc Giraud</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>Emmanuel Agullo</figcaption></figure>
Franck Cappello
Argonne National Laboratory

Sheng Di
Argonne National Laboratory

Robert Underwood
Argonne National Laboratory

Dingwen Tao
Indiana University Bloomington Jon Calhoun Clemson University Yoshii

Kazutomo Argonne
National Laboratory

Kento Sato
RIKEN Center for Computational Science

Amarjit Singh
RIKEN Center for Computational Science

Luc Giraud
National Research Institute for Computing and Automation

Emmanuel Agullo
National Research Institute for Computing and Automation

Xavier Yepes
Barcelona Supercomputing Center Mario

Acosta Barcelona Supercomputing Center

Sian Jin
Indiana University Bloomington

Jiannan Tian
Indiana University Bloomington

Frédéric Vivien
National Research Institute for Computing and Automation

Boyuan Zhang
Indiana University Bloomington Kentaro Sano RIKEN Center for Computational Science

Tomohiro Ueno
RIKEN Center for Computational Science

Thomas Gruetzmacher
Karlsruhe Institute of Technology

Hartwig Anzt
Karlsruhe Institute of Technology
Franck Cappello
Argonne National Laboratory

Sheng Di
Argonne National Laboratory

Robert Underwood
Argonne National Laboratory

Dingwen Tao
Indiana University Bloomington Jon Calhoun Clemson University Yoshii

Kazutomo Argonne
National Laboratory

Kento Sato
RIKEN Center for Computational Science

Amarjit Singh
RIKEN Center for Computational Science

Luc Giraud
National Research Institute for Computing and Automation

Emmanuel Agullo
National Research Institute for Computing and Automation

Xavier Yepes
Barcelona Supercomputing Center Mario

Acosta Barcelona Supercomputing Center

Sian Jin
Indiana University Bloomington

Jiannan Tian
Indiana University Bloomington

Frédéric Vivien
National Research Institute for Computing and Automation

Boyuan Zhang
Indiana University Bloomington Kentaro Sano RIKEN Center for Computational Science

Tomohiro Ueno
RIKEN Center for Computational Science

Thomas Gruetzmacher
Karlsruhe Institute of Technology

Hartwig Anzt
Karlsruhe Institute of Technology
The Joint Laboratory on Extreme Scale Computing (JLESC) was initiated at the same time lossy compression for scientific data became an important topic for the scientific communities. The teams involved in the JLESC played and are still playing an important role in developing the research, techniques, methods, and technologies making lossy compression for scientific data a key tool for scientists and engineers. In this paper we present the evolution of lossy compression for scientific data from 2015, describing the situation before the JLESC started, the evolution of this discipline in the past 8 years (until 2023) through the prism of the JLESC collaborations on this topic, and some of the remaining open research questions.
The Joint Laboratory on Extreme Scale Computing (JLESC) was initiated at the same time lossy compression for scientific data became an important topic for the scientific communities. The teams involved in the JLESC played and are still playing an important role in developing the research, techniques, methods, and technologies making lossy compression for scientific data a key tool for scientists and engineers. In this paper we present the evolution of lossy compression for scientific data from 2015, describing the situation before the JLESC started, the evolution of this discipline in the past 8 years (until 2023) through the prism of the JLESC collaborations on this topic, and some of the remaining open research questions.
Franck Cappello, lead writer, contributed to intro, open questions.

Mario Acosta, contributed to the section on compression for climate simulation.

Emmanuel Agullo, contributed to the section on compression for linear algebra.

Hartwig Anzt, contributed to the section on compression for linear algebra.

Sheng Di, contributed to several sections.

Luc Giraud, contributed to the section on compression for linear algebra.

Thomas Grutzmacher, contributed to the section on compression for linear algebra.

Sian Jin, contributed to the section on compression for I/O.

Kento Sato, contributed to the section on compression for instruments.

Amarjit Singh, contributed to the section on compression for instruments.

Dingwen Tao, contributed to the section on compression for AI and GPU for compression.

Jiannan Tian, contributed to the section on compression for AI and GPU for compression.

Tomohiro Ueno, contributed to the section on compression for instruments.

Robert Underwood, contributed to the section on compression for instruments.

Frédéric Vivien, contributed to the section on compression for I/O.

Xavier Yepes-Arbós, contributed to the section on compression for climate simulation.

Kazutomo Yoshii, contributed to the section on compression for instruments.

Boyuan Zhang, contributed to the section on compression for AI and GPU for compression.
Franck Cappello, lead writer, contributed to intro, open questions.

Mario Acosta, contributed to the section on compression for climate simulation.

Emmanuel Agullo, contributed to the section on compression for linear algebra.

Hartwig Anzt, contributed to the section on compression for linear algebra.

Sheng Di, contributed to several sections.

Luc Giraud, contributed to the section on compression for linear algebra.

Thomas Grutzmacher, contributed to the section on compression for linear algebra.

Sian Jin, contributed to the section on compression for I/O.

Kento Sato, contributed to the section on compression for instruments.

Amarjit Singh, contributed to the section on compression for instruments.

Dingwen Tao, contributed to the section on compression for AI and GPU for compression.

Jiannan Tian, contributed to the section on compression for AI and GPU for compression.

Tomohiro Ueno, contributed to the section on compression for instruments.

Robert Underwood, contributed to the section on compression for instruments.

Frédéric Vivien, contributed to the section on compression for I/O.

Xavier Yepes-Arbós, contributed to the section on compression for climate simulation.

Kazutomo Yoshii, contributed to the section on compression for instruments.

Boyuan Zhang, contributed to the section on compression for AI and GPU for compression.
Declaration of interests

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Declaration of interests

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: