Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Galanin in Cerebellar Granule Cell Migration in the Early Postnatal Mouse during Normal Development and after Injury

Galanin, one of the most inducible neuropeptides, is widely present in developing brains, and its expression is altered by pathologic events (e.g., epilepsy, ischemia, and axotomy). The roles of galanin in brain development under both normal and pathologic conditions have been hypothesized, but the question of how galanin is involved in fetal and early postnatal brain development remains largely unanswered. In this study, using granule cell migration in the cerebellum of early postnatal mice (both sexes) as a model system, we examined the role of galanin in neuronal cell migration during normal development and after brain injury. Here we show that, during normal development, endogenous galanin participates in accelerating granule cell migration via altering the Ca2+ and cAMP signaling pathways. Upon brain injury induced by the application of cold insults, galanin levels decrease at the lesion sites, but increase in the surroundings of lesion sites. Granule cells exhibit the following corresponding changes in migration: (1) slowing down migration at the lesion sites; and (2) accelerating migration in the surroundings of lesion sites. Experimental manipulations of galanin signaling reduce the lesion site-specific changes in granule cell migration, indicating that galanin plays a role in such deficits in neuronal cell migration. The present study suggests that manipulating galanin signaling may be a potential therapeutic target for acutely injured brains during development. SIGNIFICANCE STATEMENT Deficits in neuronal cell migration caused by brain injury result in abnormal development of cortical layers, but the underlying mechanisms remain to be determined. Here, we report that on brain injury, endogenous levels of galanin, a neuropeptide, are altered in a lesion site-specific manner, decreasing at the lesion sites but increasing in the surroundings of lesion sites. The changes in galanin levels positively correlate with the migration rate of immature neurons. Manipulations of galanin signaling ameliorate the effects of injury on neuronal migration and cortical layer development. These results shed a light on galanin as a potential therapeutic target for acutely injured brains during development.
Republication of “Principles of effective course design: What I wish I had known about learning-centered teaching 30 years ago”

Ten years ago, after 20 years as a university professor, I was asked to direct the teaching and learning support center at my university. I quickly realized I had almost no knowledge of the published scholarship on this subject. From my reading of this literature, I found the research on the predictors of student learning particularly informative. In particular, I gained an appreciation for the impact of course design. In this article, I summarize a framework for designing “significant learning experiences. ” In discussing the three key components of course design (learning outcomes, learning activities, and learning assessments), I offer tips and give examples relevant for the field of management. My intent is to share the most important information I have learned from a decade of conversations with experts on student learning—the things I wish someone had taught me 30 years ago.
G-SEPM: Building an Accurate and Efficient Soft Error Prediction Model for GPGPUs

As GPUs become ubiquitous in large-scale general purpose HPC systems (GPGPUs), ensuring the reliable execution of such systems in the presence of soft errors is increasingly essential. To provide insights into how resilient GPU programs are toward soft errors, researchers typically rely on random Fault Injection (FI) to evaluate the tolerance of programs. However, it is expensive to obtain a statistically significant resilience profile and not suitable to identify all the error-critical fault sites of GPU programs. To address the above challenges, in this work, we build a GPGPU-based Soft Error Prediction Model (G-SEPM) that can replace FI to estimate the resilience characteristics of individual fault sites accurately and efficiently. We observe that the instruction-type, bit-position, bit-flip direction, and error propagation information have capabilities to characterize fault site resiliency. Leveraging these heuristic features, G-SEPM drives the machine learning model to reveal the hidden interactions among fault site resiliency and our observed features. Experimental results demonstrate that G-SEPM achieves high accuracy for fault site error estimation and critical fault site identification while introducing negligible overhead. In addition, G-SEPM can provide essential insight for programmers/architects to design more cost-effective soft error mitigation solutions.
Online Optimization of File Transfers in High-Speed Networks

File transfers in high-speed networks require network and I/O parallelism to reach high speeds, however, creating arbitrarily large numbers of I/O and network threads overwhelms system resources and causes fairness issues. In this paper, we introduce Falcon that combines a novel utility function with state-of-the-art online op-timization algorithms to discover the degree of I/O and network parallelism for file transfer that can maximize the throughput while keeping system overhead low and ensuring fairness among competing transfers. Our extensive evaluations in several dedicated and production high-speed networks show that Falcon can find near-optimal solution in as little as 20 seconds and outperforms existing transfer application by 2-6x. Moreover, unlike other file transfer optimization solutions that fail to ensure fair resource allocation between competing transfers, Falcon is guaranteed to converge to Nash Equilibrium when multiple Falcon agents compete for the network resources with the help of its game theory-inspired utility function.
Accelerating Applications using Edge Tensor Processing Units

Neural network (NN) accelerators have been integrated into a wide-spectrum of computer systems to accommodate the rapidly growing demands for artificial intelligence (AI) and machine learning (ML) applications. NN accelerators share the idea of providing native hardware support for operations on multidimensional tensor data. Therefore, NN accelerators are theoretically tensor processors that can improve system performance for any problem that uses tensors as inputs/outputs. Unfortunately, commercially available NN accelerators only expose computation capabilities through AI/ML-specific interfaces. Furthermore, NN accelerators reveal very few hardware design details, so applications cannot easily leverage the tensor operations NN accelerators provide. This paper introduces General-Purpose Computing on Tensor Processing Units (GPTPU), an open-source, open-architecture framework that allows the developer and research communities to discover opportunities that NN accelerators enable for applications. GPTPU includes a powerful programming interface with efficient runtime system-level support-similar to that of CUDA/OpenCL in GPGPU computing-to bridge the gap between application demands and mismatched hardware/software interfaces. We built GPTPU machine uses Edge Tensor Processing Units (Edge TPUs), which are widely available and representative of many commercial NN accelerators. We identified several novel use cases and revisited the algorithms. By leveraging the underlying Edge TPUs to perform tensor-algorithm-based compute kernels, our results reveal that GPTPU can achieve a 2.46x speedup over high-end CPUs and reduce energy consumption by 40%.
Postmortem accurate IR-level state recovery for deployed concurrent programs

Debugging failures of deployed concurrent software is important for quality assurance. However, such failures are difficult to debug because their behavior is non-deterministic and limited information can be obtained with conventional means. Reverse debuggers such as REPT [11] assists with debugging by recovering data values before the failure. This is achieved by using a hardware-tracer to log control-flow information, then using the information and a conventional coredump to recover data values via reverse-execution at machine-level. REPT's algorithm for data value recovery is reliable and fast. But the implementation cost is high because of its dependence on architecture. Applying REPT to more abstract IR (Intermediate Representation) level instructions to counter this yielded limited results with low accuracy compared to the original x86_64 implementation. The main reason for this is that the stack layout is abstracted at IR-level. In this paper, we present STRAB (State Recovery at Abstract-level), a collection of our proposed methods to solve these problems. STRAB works in two phases. First, the data values in the coredump are lifted from machine-level to IR-level using rich debug information (DWARF3) and a novel technique we call mid-recovery lifting, the latter helping to recover more heap data values at IR-level. Second, our novel hybrid memory location resolution reduces the accuracy loss due to the abstracted stack layout at IR-level. Experimental results on a variety of real-world concurrent programs show that STRAB has significantly higher accuracy compared to REPT at IR-level (+40% on average) with only minor slowdowns (x2.7 on average), while also achieving architecture-independence.
HatRPC: Hint-Accelerated Thrift RPC over RDMA

In this paper, we propose a novel hint-accelerated Remote Procedure Call (RPC) framework based on Apache Thrift over Remote Direct Memory Access (RDMA) protocols, called HatRPC. HatRPC proposes a hierarchical hint scheme towards optimizing heterogeneous RPC services and functions. The proposed hint design is composed of service-granularity and function-granularity hints for achieving varied optimization goals and reducing design space for further optimizing the underneath RDMA communication engine. We co-design a key-value store called HatKV with HatRPC and LMDB. The effectiveness and efficiency of HatRPC are validated and evaluated with our proposed Apache Thrift Benchmarks (ATB), YCSB, and TPC-H workloads. Performance evaluations show that the proposed HatRPC approach can deliver up to 55% performance improvement for ATB benchmarks and up to 1.51× speedup for TPC-H queries compared with vanilla Thrift over IPoIB. In addition, the co-designed HatKV can achieve up to 85.5% improvement for YCSB workloads.
Hardware-Supported Remote Persistence for Distributed Persistent Memory

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.
Understanding, Predicting and Scheduling Serverless Workloads under Partial Interference

Interference among distributed cloud applications can be classified into three types: full, partial and zero. While prior research merely focused on full interference, the partial interference that occurs at parts of applications is far more common yet still lacks in-depth study. Serverless computing that structures applications into small-sized, short-lived functions further exacerbate partial interference. We characterize the features of partial interference in serverless as exhibiting high volatility, spatial-temporal variation, and propagation. Given these observations, we propose an incremental learning predictor, named Gsight, which can achieve high precision by harnessing the spatial-temporal overlap codes and profiles of functions via an end-to-end call path. Experimental results show that Gsight can achieve an average error of 1.71%. Its convergence speed is at least 3x faster than that in a serverful system. A scheduling case study shows that the proposed method can improve function density by ≥18.79% while guaranteeing the quality of service (QoS).
LogECMem: Coupling Erasure-Coded In-Memory Key-Value Stores with Parity Logging

In-memory key-value stores are often used to speed up Big Data workloads on modern HPC clusters. To maintain their high availability, erasure coding has been recently adopted as a low-cost redundancy scheme instead of replication. Existing erasure-coded update schemes, however, have either low performance or high memory overhead. In this paper, we propose a novel parity logging-based architecture, HybridPL, which creates a hybrid of in-place update (for data and XOR parity chunks) and log-based update (for the remaining parity chunks), so as to balance the update performance and memory cost, while maintaining efficient single-failure repairs. We realize HybridPL as an in-memory key-value store called LogECMem, and further design efficient repair schemes for multiple failures. We prototype LogECMem and conduct experiments on different workloads. We show that LogECMem achieves better up-date performance over existing erasure-coded update schemes with low memory overhead, while maintaining high basic I/O and repair performance.
HPAC: Evaluating Approximate Computing Techniques on HPC OpenMP Applications

As we approach the limits of Moore's law, researchers are exploring new paradigms for future high-performance computing (HPC) systems. Approximate computing has gained traction by promising to deliver substantial computing power. However, due to the stringent accuracy requirements of HPC scientific applications, the broad adoption of approximate computing methods in HPC requires an in-depth understanding of the application's amenability to approximations. We develop HPAC, a framework with compiler and runtime support for code annotation and transformation, and accuracy vs. performance trade-off analysis of OpenMP HPC applications. We use HPAC to perform an in-depth analysis of the effectiveness of approximate computing techniques when applied to HPC applications. The results reveal possible performance gains of approximation and its interplay with parallel execution. For instance, in the LULESH proxy application approximation provides substantial performance gains due to the reduction of memory accesses. However, in the leukocyte benchmark approximation induces load imbalance in the parallel execution and thus limiting the performance gains.
Hybrid, Scalable, Trace-Driven Performance Modeling of GPGPUs

In this paper, we present PPT-GPU, a scalable performance prediction toolkit for GPUs. PPT-GPU achieves scalability through a hybrid high-level modeling approach where some computations are extrapolated and multiple parts of the model are parallelized. The tool primary prediction models use pre-collected memory and instructions traces of the workloads to accurately capture the dynamic behavior of the kernels. PPT-GPU reports an extensive array of GPU performance metrics accurately while being easily extensible. We use a broad set of benchmarks to verify predictions accuracy. We compare the results against hardware metrics collected using vendor profiling tools and cycle-accurate simulators. The results show that the performance predictions are highly correlated to the actual hardware (MAPE: < 16% and Correlation: > 0.98). Moreover, PPT-GPU is orders of magnitude faster than cycle-accurate simulators. This comprehensiveness of the collected metrics can guide architects and developers to perform design space explorations. Moreover, the scalability of the tool enables conducting efficient and fast sensitivity analyses for performance-critical applications.
ndzip-gpu: Efficient lossless Compression of Scientific Floating-Point Data on GPUs

Lossless data compression is a promising software approach for reducing the bandwidth requirements of scientific applications on accelerator clusters without introducing approximation errors. Suitable compressors must be able to effectively compact floating-point data while saturating the system interconnect to avoid introducing unnecessary latencies. We present ndzip-gpu, a novel, highly-efficient GPU parallelization scheme for the block compressor ndzip, which has recently set a new milestone in CPU floating-point compression speeds. Through the combination of intra-block parallelism and efficient memory access patterns, ndzip-gpu achieves high resource utilization in decorrelating multi-dimensional data via the Integer Lorenzo Transform. We further introduce a novel, efficient warp-cooperative primitive for vertical bit packing, providing a high-throughput data reduction and expansion step. Using a representative set of scientific data, we compare the performance of ndzip-gpu against five other, existing GPU compressors. While observing that effectiveness of any compressor strongly depends on characteristics of the dataset, we demonstrate that ndzip-gpu offers the best average compression ratio for the examined data. On Nvidia Turing, Volta and Ampere hardware, it achieves the highest single-precision throughput by a significant margin while maintaining a favorable trade-off between data reduction and throughput in the double-precision case.
High Grashof number turbulent natural convection on an infinite vertical wall

Abstract The present study concerns a temporally evolving turbulent natural convection boundary layer (NCBL) adjacent to an isothermally heated vertical wall, with Prandtl number 0.71. Three-dimensional direct numerical simulations (DNS) are carried out to investigate the turbulent flow up to $\textit {Gr}_\delta =1.21\times 10^8$, where $\textit {Gr}_\delta$ is the Grashof number based on the boundary layer thickness $\delta$. In the near-wall region, there exists a constant heat flux layer, similar to previous studies for the spatially developing flows (e.g. George & Capp, Intl J. Heat Mass Transfer, vol. 22, 1979, pp. 813–826). Beyond a wall-normal distance $\delta _i$, the NCBL can be characterised as a plume-like region. We find that this region is well described by a self-similar integral model with profile coefficients (cf. van Reeuwijk & Craske, J. Fluid Mech., vol. 782, 2015, pp. 333–355) which are $\textit {Gr}_\delta$-independent after $\textit {Gr}_\delta =10^7$. In this Grashof number range both the outer plume-like region and the near-wall boundary layer are turbulent, indicating the beginning of the so-called ultimate turbulent regime (Grossmann & Lohse, J. Fluid Mech., vol. 407, 2000, pp. 27–56; Grossmann & Lohse, Phys. Fluids, vol. 23, 2011, 045108). Solutions to the self-similar integral model are analytically obtained by solving ordinary differential equations with profile coefficients empirically obtained from the DNS results. In the present study, we have found the wall heat transfer of the NCBL is directly related to the top-hat scales which characterise the plume-like region. The Nusselt number is found to follow $\textit {Nu}_\delta \propto \textit {Gr}_\delta ^{0.381}$, slightly higher than the empirical $1/3$-power-law correlation reported for spatially developing NCBLs at lower $\textit {Gr}_\delta$, but is shown to be consistent with the ultimate heat transfer regime with a logarithmic correction suggested by Grossmann & Lohse (Phys. Fluids, vol. 23, 2011, 045108). By modelling the near-wall buoyancy force, we show that the wall shear stress would scale with the bulk velocity only at asymptotically large Grashof numbers.
Velocity gradient statistics in turbulent shear flow: an extension of Kolmogorov's local equilibrium theory

Abstract This paper presents an extension of Kolmogorov's local similarity hypotheses of turbulence to include the influence of mean shear on the statistics of the fluctuating velocity in the dissipation range of turbulent shear flow. According to the extension, the moments of the fluctuating velocity gradients are determined by the local mean rate of the turbulent energy dissipation $\left \langle \epsilon \right \rangle$ per unit mass, kinematic viscosity $\nu$ and parameter $\gamma \equiv S (\nu /\left \langle \epsilon \right \rangle )^{1/2}$, provided that $\gamma$ is small in an appropriate sense, where $S$ is an appropriate norm of the local gradients of the mean flow. The statistics of the moments are nearly isotropic for sufficiently small $\gamma$, and the anisotropy of moments decreases approximately in proportion to $\gamma$. This paper also presents a report on the second-order moments of the fluctuating velocity gradients in direct numerical simulations (DNSs) of turbulent channel flow (TCF) with the friction Reynolds number $Re_\tau$ up to $\approx 8000$. In the TCF, there is a range $y$ where $\gamma$ scales approximately $\propto y^ {-1/2}$, and the anisotropy of the moments of the gradients decreases with $y$ nearly in proportion to $y^ {-1/2}$, where $y$ is the distance from the wall. The theoretical conjectures proposed in the first part are in good agreement with the DNS results.
Transcriptional variability accelerates preleukemia by cell diversification and perturbation of protein synthesis

Transcriptional variability facilitates stochastic cell diversification and can in turn underpin adaptation to stress or injury. We hypothesize that it may analogously facilitate progression of pre-malignancy to cancer. To investigate this, we initiated pre-leukemia in mouse cells with enhanced transcriptional variability due to conditional disruption of the histone lysine acetyltransferase gene Kat2a. By combining single-cell RNA-sequencing of pre-leukemia with functional analysis of transformation, we show that Kat2a loss results in global variegation of cell identity and accumulation of pre-leukemic cells. Leukemia progression is subsequently facilitated by destabilization of ribosome biogenesis and protein synthesis, which confer a transient transformation advantage. The contribution of transcriptional variability to early cancer evolution reflects a generic role in promoting cell fate transitions, which, in the case of well-adapted malignancies, contrastingly differentiates and depletes cancer stem cells. In other words, transcriptional variability confers forward momentum to cell fate systems, with differential multi-stage impact throughout cancer evolution. One-sentence summary Loss of Kat2a enhances transcriptional variability of ribosome biosynthetic programs and transiently accelerates pre-leukemia
EFHD1 ablation reduces cardiac mitoflash activation and protects cardiomyocytes from ischemia

Altered levels of intracellular calcium (Ca2+) is a highly prevalent feature in different forms of cardiac injury, producing changes in contractility, arrhythmias, and mitochondrial dysfunction. In cardiac ischemia-reperfusion injury, mitochondrial Ca2+ overload leads to pathological production of reactive oxygen species (ROS), activates the permeability transition, and cardiomyocyte death. Here we investigated the cardiac phenotype caused by deletion of EF-hand domain-containing protein D1 (Efhd1-/-), a Ca2+-binding mitochondrial protein whose function is poorly understood. Efhd1-/- mice are viable and have no adverse cardiac phenotypes. They feature reductions in basal ROS levels and mitoflash events, both important precursors for mitochondrial injury, though cardiac mitochondria have normal susceptibility to Ca2+ overload. Notably, we also find that Efhd1-/- mice and their cardiomyocytes are resistant to hypoxic injury.