International Journal of High Performance Computing Applications最新文献_第2页

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials Abisko：使用新型神经形态材料的尖峰神经网络架构的深度协同设计

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-06-22 DOI: 10.1177/10943420231178537

J. Vetter, Prasanna Date, F. Fahim, Shruti R. Kulkarni, P. Maksymovych, A. Talin, Marc Gonzalez Tallada, Pruek Vanna-Iampikul, Aaron R. Young, David Brooks, Yu Cao, Wei Gu-Yeon, S. Lim, Frank Liu, Matthew J. Marinella, B. Sumpter, Narasinga Rao Miniskar

{"title":"Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials","authors":"J. Vetter, Prasanna Date, F. Fahim, Shruti R. Kulkarni, P. Maksymovych, A. Talin, Marc Gonzalez Tallada, Pruek Vanna-Iampikul, Aaron R. Young, David Brooks, Yu Cao, Wei Gu-Yeon, S. Lim, Frank Liu, Matthew J. Marinella, B. Sumpter, Narasinga Rao Miniskar","doi":"10.1177/10943420231178537","DOIUrl":"https://doi.org/10.1177/10943420231178537","url":null,"abstract":"The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. The key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"351 - 379"},"PeriodicalIF":3.1,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46219745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fast truncated SVD of sparse and dense matrices on graphics processors 图形处理器上稀疏和密集矩阵的快速截断奇异值分解

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-06-07 DOI: 10.1177/10943420231179699

A. Tomás, E. S. Quintana‐Ortí, H. Anzt

引用次数: 0

Accelerated dynamic data reduction using spatial and temporal properties 使用空间和时间属性加速动态数据缩减

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-06-05 DOI: 10.1177/10943420231180504

Megan Hickman Fulp, Dakota Fulp, Changfeng Zou, Cooper Sanders, Ayan Biswas, Melissa C. Smith, Jon C. Calhoun

{"title":"Accelerated dynamic data reduction using spatial and temporal properties","authors":"Megan Hickman Fulp, Dakota Fulp, Changfeng Zou, Cooper Sanders, Ayan Biswas, Melissa C. Smith, Jon C. Calhoun","doi":"10.1177/10943420231180504","DOIUrl":"https://doi.org/10.1177/10943420231180504","url":null,"abstract":"Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"539 - 559"},"PeriodicalIF":3.1,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48055498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark HipBone:性能可移植的图形处理单元，加速了NekBone基准的c++版本

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-31 DOI: 10.1177/10943420231178552

N. Chalmers, Abhishek Mishra, Damon McDougall, T. Warburton

{"title":"HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark","authors":"N. Chalmers, Abhishek Mishra, Damon McDougall, T. Warburton","doi":"10.1177/10943420231178552","DOIUrl":"https://doi.org/10.1177/10943420231178552","url":null,"abstract":"We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"560 - 577"},"PeriodicalIF":3.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42825171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Graph neural networks for detecting anomalies in scientific workflows 用于检测科学工作流程中异常的图神经网络

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-30 DOI: 10.1177/10943420231172140

Hongwei Jin, Krishnan Raghavan, G. Papadimitriou, Cong Wang, A. Mandal, M. Kiran, E. Deelman, Prasanna Balaprakash

{"title":"Graph neural networks for detecting anomalies in scientific workflows","authors":"Hongwei Jin, Krishnan Raghavan, G. Papadimitriou, Cong Wang, A. Mandal, M. Kiran, E. Deelman, Prasanna Balaprakash","doi":"10.1177/10943420231172140","DOIUrl":"https://doi.org/10.1177/10943420231172140","url":null,"abstract":"Identifying and addressing anomalies in complex, distributed systems can be challenging for reliable execution of scientific workflows. We model these workflows as directed acyclic graphs (DAGs), where the nodes and edges of the DAGs represent jobs and their dependencies, respectively. We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels. We investigate workflow-specific GNN models that are trained on a particular workflow and workflow-agnostic GNN models that are trained across the workflows. Our GNN models, which incorporate both individual job features and topological information from the workflow, show improved accuracy and efficiency compared to conventional learning methods for detecting anomalies. While joint trained with multiple scientific workflows, our GNN models reached an accuracy more than 80% for workflow level and 75% for job level anomalies. In addition, we illustrate the importance of hyperparameter tuning method in our study that can significantly improve the metric(s) measure of evaluating the GNN models. Finally, we integrate explainable GNN methods to provide insights on job features in the workflow that cause an anomaly.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"394 - 411"},"PeriodicalIF":3.1,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46037147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Dynamic spawning of MPI processes applied to malleability 应用于延展性的MPI过程的动态生成

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-29 DOI: 10.1177/10943420231176527

Iker Martín-Álvarez, J. Aliaga, M. Castillo, Sergio Iserte, R. Mayo

{"title":"Dynamic spawning of MPI processes applied to malleability","authors":"Iker Martín-Álvarez, J. Aliaga, M. Castillo, Sergio Iserte, R. Mayo","doi":"10.1177/10943420231176527","DOIUrl":"https://doi.org/10.1177/10943420231176527","url":null,"abstract":"Malleability allows computing facilities to adapt their workloads through resource management systems to maximize the throughput of the facility and the efficiency of the executed jobs. This technique is based on reconfiguring a job to a different resource amount during execution and then continuing with it. One of the stages of malleability is the dynamic spawning of processes in execution time, where different decisions in this stage will affect how the next stage of data redistribution is performed, which is the most time-consuming stage. This paper describes different methods and strategies, defining eight different alternatives to spawn processes dynamically and indicates which one should be used depending on whether a strong or weak scaling application is being used. In addition, it is described for both types of applications which strategies benefit most the application performance or the system productivity. The results show that reducing the number of spawning processes by reusing the older ones can reduce reconfiguration time compared to the classical method by up to 2.6 times for expanding and up to 36 times for shrinking. Furthermore, the asynchronous strategy requires analysing the impact of oversubscription on application performance.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48600798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modeling, evaluating, and orchestrating heterogeneous environmental leverages for large-scale data center management 为大规模数据中心管理建模、评估和协调异构环境利用

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-24 DOI: 10.1177/10943420231172978

Vladimir Ostapenco, L. Lefèvre, Anne-Cécile Orgerie, Benjamin Fichel

引用次数: 0

Finding the forest in the trees: Enabling performance optimization on heterogeneous architectures through data science analysis of ensemble performance data 在树中寻找森林：通过集成性能数据的数据科学分析实现异构体系结构的性能优化

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-23 DOI: 10.1177/10943420231175687

Olga Pearce, S. Brink

引用次数: 0

IO-aware Job-Scheduling: Exploiting the Impacts of Workload Characterizations to select the Mapping Strategy IO感知作业调度：利用工作负载特征的影响来选择映射策略

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-15 DOI: 10.1177/10943420231175854

E. Jeannot, Guillaume Pallez, Nicolas Vidal

{"title":"IO-aware Job-Scheduling: Exploiting the Impacts of Workload Characterizations to select the Mapping Strategy","authors":"E. Jeannot, Guillaume Pallez, Nicolas Vidal","doi":"10.1177/10943420231175854","DOIUrl":"https://doi.org/10.1177/10943420231175854","url":null,"abstract":"In high performance, computing concurrent applications are sharing the same file system. However, the bandwidth which provides access to the storage is limited. Therefore, too many I/O operations performed at the same time lead to conflicts and performance loss due to contention. This scenario will become more common as applications become more data intensive. To avoid congestion, job-schedulers have to play an important role in selecting which application run concurrently. However I/O-aware mapping strategies need to be simple, robust and fast. Hence, in this article, we discuss two plain and practical strategies to mitigate I/O congestion. They are based on the idea of scheduling I/O access so as not to exceed some prescribed I/O bandwidth. More precisely, we compare two approaches: one grouping applications into packs that will be run independently (i.e., pack-scheduling), the other one scheduling greedily applications using a predefined order (i.e. list-scheduling). Results show that performances depend heavily on the I/O load and the homogeneity of the underlying workload. Finally, we introduce the notion of characteristic time that represents information on the average time between consecutive I/O transfers. We show that it could be important to the design of schedulers and that we expect it to be easily obtained by analysis tools.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"213 - 228"},"PeriodicalIF":3.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46118985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Black-box statistical prediction of lossy compression ratios for scientific data 科学数据有损压缩比的黑箱统计预测

IF 3.1 3区计算机科学

International Journal of High Performance Computing Applications Pub Date : 2023-05-15 DOI: 10.1177/10943420231179417

Robert Underwood, J. Bessac, David Krasowska, Jon C. Calhoun, S. Di, F. Cappello

{"title":"Black-box statistical prediction of lossy compression ratios for scientific data","authors":"Robert Underwood, J. Bessac, David Krasowska, Jon C. Calhoun, S. Di, F. Cappello","doi":"10.1177/10943420231179417","DOIUrl":"https://doi.org/10.1177/10943420231179417","url":null,"abstract":"Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"37 1","pages":"412 - 433"},"PeriodicalIF":3.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45578601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2