Miracle-dz/UniFAR-FTUs huggingface dataset:
PM100: A Job Power Consumption Dataset of a Large-scale Production HPC System

The power requirements of modern High-Performance Computing (HPC) systems pose environmental and financial challenges, as they contribute to carbon emissions and strain power grids. Optimizing power consumption together with system performance has thus become a crucial goal for HPC centers. As jobs running on a system contribute to the whole system’s power usage, predicting their power requirements before execution on the system would allow to forecast the overall power consumption and perform techniques like power capping at the workload manager level. Such predictive studies need quality data, which is limited due to the inherent complexity of collecting structured data for job power characterization in a production system. This paper aims to fill the lack of resources for job power prediction and provide the HPC community with (i) a methodology to create a job power consumption dataset from workload manager data and node power metrics logs, and (ii) a novel and large dataset comprising around 230K jobs and their corresponding power consumption values. The dataset is derived from M100, a holistic dataset extracted from a production supercomputer hosted at the HPC centre CINECA in Italy.
Checkpoint/Restart for CUDA Kernels

In HPC clusters, it has become common to employ Checkpoint/Restart (C/R), that is, saving the execution state of applications in order to restore their computational progress at a later point in time. The benefits of this technique for clusters include more flexibility when reacting to changing workloads and an increased fault tolerance. While many clusters already benefit from C/R tools for traditional CPU applications, there is a lack of comparable tools enabling preemptive and transparent C/R for heterogeneous computing, where applications execute partly on accelerator devices, such as s. This is despite the increasing use of s as accelerators in High-Performance Computing (HPC) clusters. Therefore, we propose a novel C/R tool that enables saving the execution state of CUDA kernels, thus allowing preemptive C/R of applications without the need to wait for kernels to finish. We show that full-featured C/R for NVIDIA s is possible despite the proprietary nature of the hardware and software of these devices.
symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver

Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today’s large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs. We present symPACK, a GPU-capable parallel sparse Cholesky solver that uses one-sided communication primitives and remote procedure calls provided by the UPC++ library. We also utilize the UPC++ “memory kinds” feature to enable efficient communication of GPU-resident data. We show that on a number of large problems, symPACK outperforms comparable state-of-the-art GPU-capable Cholesky factorization codes by up to 14x on the NERSC Perlmutter supercomputer.
BaRRT: Buildtime and Runtime Reproducibility Tool for Software Development and Testing

During software development, many aspects of the system and user state can change. Significant time can be spent tracking down the causes of these differences, rather than focusing on the main task of software development. This paper describes a tool to record the state at build-time and at runtime of an application to more easily investigate the cause(s) of differences in behavior. The added logging enables better software quality assurance by tracking code changes and their effects on runtime behavior. At a minimum, this tool only requires prepending one command at build-time and another at runtime. Project-level configurations can be set to enable the collection of additional information.
Optimization of Ported CFD Kernels on Intel Data Center GPU Max 1550 using oneAPI ESIMD

We describe our experience porting FUN3D’s CUDA-optimized kernels to Intel oneAPI SYCL. We faced several challenges, including foremost the suboptimal performance of the oneAPI code on Intel’s new data center GPU. Suboptimal performance of the oneAPI code was due primarily to high register spills, memory latency, and poor vectorization. We addressed these issues by implementing the kernels using Intel oneAPI’s Explicit SIMD SYCL extension (ESIMD) API. The ESIMD API enables the writing of explicitly vectorized kernel code, gives more precise control over register usage and prefetching, and better handles thread divergence compared to SYCL. The ESIMD code outperforms the optimized SYCL code by up to a factor of 3.6, depending on the kernel. We also compared the performance of three ESIMD kernels on the Intel Data Center Max 1550 GPU with the CUDA-optimized versions on NVIDIA V100 and A100 GPUs. We found the performance of a single tile of the Intel GPU using ESIMD greater than NVIDIA V100 and similar to NVIDIA A100.
BERT-Based Logits Ensemble Model for Gender Bias and Hate Speech Detection

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.
XI.—A Late-glacial Site at Loch Droma, Ross and Cromarty

Synopsis Excavations in 1958 for the construction of a dam at Loch Droma, Ross and Cromarty, revealed a deep and extensive section in peat and Late-glacial silts. The section was surveyed, its environs studied, and samples of materials analyzed by the Macaulay Institute, Aberdeen, and the Sub-department of Quaternary Research, University of Cambridge. The watershed location of the site, the early radio-carbon date obtained for the basal silts (12,810 ± 155 B.P.), and the nature of their organic contents, make this a critical site for the elucidation of the deglaciation and vegetational history of the Atlantic seaboard of North-west Scotland. In Part I of this report, W. Kirk describes the site and its setting, and indicates some of the problems it raises for existing glacial chronologies of Northern Scotland, and in Part II, H. Godwin summarizes and comments upon the analyses of organic remains.
Dask-Extended External Tasks for HPC/ML In transit Workflows

In situ workflows are inescapable to fully leverage exascale architectures. They can be complex to build, however, because simulation and data analytics come from two different software ecosystems with their own paradigms and programming models. This work extends the deisa bridging model between MPI+X simulations and distributed task-based analytics; it introduces the concept of external tasks to support the description of analytics graphs spanning multiple timesteps ahead of time while improving scalability. This new approach leads to a straightforward support for contracts between the simulation and analytics graph to limit the data transferred to that actually analyzed in a given execution. We implement this approach using Dask and MPI and evaluate it using an end-to-end in-transit workflow that uses an unsupervised ML model for dimensionality reduction. We compare our work with plain Dask postprocessing and with the previous version of deisa. Our work performs better, up to × 7 for the simulation and × 3 for the analytics compared with deisa, and is × 18 less costly compared with plain Dask—all of these with similar development efforts.
Towards A Massive-scale Distributed Neighborhood Graph Construction

Graph-based approximate nearest neighbor algorithms have shown high performance and quality. However, such approaches require a large amount of memory and still take a long time to construct high-quality nearest neighbor graphs (NNGs). Using distributed memory systems is important when data is large or a shorter indexing time is desired. We develop a distributed memory version of NN-Descent, a widely known graph-based ANN algorithm, closely following algorithmic advances by PyNN-Descent authors. Our distributed NN-Descent (DNND) is built on top of MPI and leverages two existing high-performance computing libraries: YGM (an asynchronous communication library) and Metall (a persistent memory allocator). We evaluate the performance of DNND on an HPC system using billion-scale datasets, demonstrating that our approach shows high performance and strong scaling and has great potential for developing massive-scale NNG frameworks.
Mixed-Precision S/DGEMM Using the TF32 and TF64 Frameworks on Low-Precision AI Tensor Cores

Using NVIDIA graphics processing units (GPUs) equipped with Tensor Cores has enabled the significant acceleration of general matrix multiplication (GEMM) for applications in machine learning (ML) and artificial intelligence (AI) and in high-performance computing (HPC) generally. The use of such power-efficient, specialized accelerators can provide a performance increase between 8 × and 20 ×, albeit with a loss in precision. However, a high level of precision is required in many large scientific and HPC applications, and computing in single or double precision is still necessary for many of these applications to maintain accuracy. Fortunately, mixed-precision methods can be employed to maintain a higher level of numerical precision while also taking advantage of the performance increases from computing with lower-precision AI cores. With this in mind, we extend the state of the art by using NVIDIA’s new TF32 framework. This new framework not only burdens some constraints of the previous frameworks, such as costly 32 16-bit castings but also provides an equivalent precision and performance by using a much simpler approach. We also propose a new framework called TF64 that attempts double-precision arithmetic with low-precision Tensor Cores. Although this framework does not exist yet, we validated the correctness of this idea and achieved an equivalent of 64-bit precision on 32-bit hardware.
CARAT KOP: Towards Protecting the Core HPC Kernel from Linux Kernel Modules

Extending Linux through the kernel module interface can offer immense benefits and capabilities in high performance computing (HPC). These extensions can also be more readily deployed because Linux is the common, typically only, supported OS choice among supercomputing vendors. However, because Linux is monolithic, Linux kernel modules are free to read and write any address with kernel-level permissions. A poorly written—or untrustworthy—module can wreak havoc on the whole system. This unfortunately means that many production HPC systems often do not permit custom kernel modules to be inserted into the system, no matter the benefit. By limiting what objects in the physical address space the module can have access to, it may be possible to guarantee memory safety for these modules. In this paper, we discuss the possibility of using the previously developed compiler- and runtime-based address translation (CARAT) model and toolchain to inject guards around a kernel module’s memory accesses. The memory accesses would then be allowed or disallowed according to a memory access policy specified by the user, in what amount to firewall rules. We share our results regarding the guard injection and address validation process. The CARAT-based Kernel Object Protection (CARAT KOP) prototype is able to transform a substantial production kernel module from the kernel tree (a network device driver composed of approximately 19,000 lines of code). The transformed module can then run with minimal effect on performance while restricted to a set of address regions.
Personalized Recommendation Algorithm of Interior Design Style Based on Local Social Network

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.
A Brief Survey into the Field of Automatic Image Dataset Generation through Web Scraping and Query Expansion

High-quality image datasets are in high demand for various applications. With many online sources providing manually collected datasets, a persisting challenge is to fully automate the dataset collection process. In this study, we surveyed an automatic image dataset generation field through analyzing a collection of existing studies. Moreover, we examined fields that are closely related to automated dataset generation, such as query expansion, web scraping, and dataset quality. We assess how both noise and regional search engine differences can be addressed using an automated search query expansion focused on hypernyms, allowing for user-specific manual query expansion. Combining these aspects provides an outline of how a modern web scraping application can produce large-scale image datasets.
A Network Intrusion Security Detection Method Using BiLSTM-CNN in Big Data Environment

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusion-detection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1-score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.
Channel Estimation for IRS-Aided Single-User MIMO Systems Based on Core Tensor

Intelligent reflective surface (IRS), comprised of numerous low-cost passive components, is a vital technology for the future sixth-generation wireless system. However, the inability of the IRS to handle signals independently poses challenges in estimating channels in fully passive IRS-aided systems. This paper investigates the channel estimation for an IRS-aided single-user multiple-input multiple-output (SU-MIMO) system and specifically focuses on the scenario where the large-dimensional received signals processed at the base station prove inconvenient for memory-based processing. To tackle this issue, this paper suggests a non-blind receiver that relies on a core tensor of the signals received, while also considering the quasi-static channel characteristic between the IRS and BS. Simulation results demonstrate the proposed receiver has superior performance in high-dimensional data processing, i.e., a lower normalized mean square error (NMSE) and computational complexity over the uncompressed received signal tensor-assisted receiver.
Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices

Embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images. Building on the foundational ResNet-18, this paper aims to maximize prediction accuracy, reduce memory size, and minimize inference latency. Various configurations were systematically probed by leveraging hardware-aware neural architecture search, accumulating 1,717 experimental results over six benchmarking variants. The experimental data analysis, enhanced by nn-Meter, provided a comprehensive understanding of inference latency across four different predictors. Significantly, a Pareto front analysis with three objectives of accuracy, latency, and memory resulted in five non-dominated solutions. These standout models showcased efficiency while retaining accuracy, offering a compelling alternative to the conventional ResNet-18 when deployed in resource-constrained environments. The paper concludes by highlighting insights drawn from the results and suggesting avenues for future exploration.
End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning

Current scientific workflow systems do not typically integrate simulation-centric and data-centric aspects due to their very different software/infrastructure requirements. A transparent integration of such components into a single end-to-end workflow would lead to a more efficient and automated way for generating insights from large simulation data. This work presents a complex case study related to extreme events analysis of future climate data that integrates in the same workflow numerical simulations, Big Data analytics and Machine Learning models. The case study is being implemented in the context of the eFlows4HPC project using the project’s software stack for deployment and orchestration of the workflow. The solution implemented in the project has shown to simplify the development and execution of end-to-end climate workflows with heterogeneous software requirements. Moreover, such an approach can, in the long term, increase the reuse of workflows by scientists and their portability over different HPC infrastructures.
Demonstration of Portable Performance of Scientific Machine Learning on High Performance Computing Systems

With the largest datasets to date and a diverse set of discoveries to be made, the current generation of scientific analyses are well poised to utilize artificial intelligence (AI) and machine learning (ML) on high performance computing (HPC) resources. Like never before, these workflows can be written in one portable language, python, which thanks to highly-optimized ML libraries achieves excellent cross-platform performance with little to no intervention by the user. In this demonstration, we explore the performance of several scientific AI/ML applications across leading HPC resources and highlight best practices for portable performance.
Riesgo de inundación en la subcuenca del río La Antigua, Veracruz, México

El objetivo del presente trabajo fue aplicar el modelo de simulacion hidraulica HEC-RAS para delimitar las zonas con riesgo a inundacion en la subcuenca del rio La Antigua, Veracruz, Mexico, utilizando la metodologia del Centro Nacional de Prevencion de Desastres (Cenapred), que considera la vulnerabilidad y el peligro de un sistema ante la ocurrencia de un fenomeno extremo perturbador. El modelo HEC-RAS usa ecuaciones diferenciales deterministicas que simulan el comportamiento y la dinamica de los niveles de agua en secciones transversales del cauce; su aplicacion bajo condiciones de flujo no permanente y regimen mixto permitio definir las areas de inundacion alcanzadas durante una avenida extraordinaria. Para la simulacion hidraulica se construyeron hidrogramas unitarios sinteticos con datos de precipitacion registrados durante un evento hidrometeorologico extremo; la geometria del cauce se obtuvo con la extension HEC-GeoRAS, herramienta que extrajo y proceso informacion georreferenciada del Modelo Digital de Elevacion (MDE) LiDAR tipo terreno y los coeficientes de rugosidad (h de Manning) se estimaron de acuerdo con las caracteristicas fisicas del canal. Para calibrar los datos estimados por el modelo se utilizo informacion hidrometrica (niveles registrados) de la estacion 28003 Cardel. La aplicacion de tres estadisticas de prueba: R, RMSE y DMA demostraron un alto ajuste entre datos observados versus simulados, respaldando la aplicabilidad del modelo HEC-RAS para simular, predecir y delimitar zonas de riesgo a inundacion.
California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system

[1] A fully coupled regional downscaling system based on the Regional Spectral Model (RSM) for atmosphere and the Regional Ocean Modeling System (ROMS) for the ocean was developed for the purpose of downscaling observed analysis or global model outputs. The two models share the same grid and resolution with efficient parallelization through the use of dual message passing interfaces. Coupled downscaling was performed using historical Simple Ocean Data Assimilation (SODA) oceanic reanalysis and NCEP/DOE (R-2) atmospheric reanalysis in order to study the impact of coupling on the regional scale atmospheric analysis. The results were subsequently compared with the uncoupled downscaling forced by the prescribed observed sea surface temperature (SST). The coupled experiment yielded the SST and ocean current with realistic small-scale oceanic features that are almost absent in the oceanic reanalysis. Upwelling over the California coast is well resolved and comparable to findings obtained from high-resolution observations. The coupling impact on the atmospheric circulation mainly modulates the near surface atmospheric variables when compared to the simulation conducted without coupling. The duration of the Catalina Eddy detected in the coupled experiment increased by about 6.5% when compared to that in the uncoupled experiment. The offshore land breeze is enhanced by about 10%, whereas the change in the onshore sea breeze is very small during the summer.