- Book学术

发布求助

文献互助智能选刊最新文献

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-04-09 DOI:10.1109/TPDS.2025.3549310

Alexandros Nikolaos Ziogas;Timo Schneider;Tal Ben-Nun;Alexandru Calotoiu;Tiziano De Matteis;Johannes de Fine Licht;Luca Lavarini;Torsten Hoefler

{"title":"Productivity, Portability, Performance, and Reproducibility: Data-Centric Python","authors":"Alexandros Nikolaos Ziogas;Timo Schneider;Tal Ben-Nun;Alexandru Calotoiu;Tiziano De Matteis;Johannes de Fine Licht;Luca Lavarini;Torsten Hoefler","doi":"10.1109/TPDS.2025.3549310","DOIUrl":null,"url":null,"abstract":"Python has become the <italic>de facto</i> language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High-Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. This work presents a workflow that retains Python’s high productivity while achieving portable performance across different architectures. The workflow’s key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing Conference (SC) 2022. We present and discuss the student teams’ results.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 5","pages":"804-820"},"PeriodicalIF":5.6000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10960277/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

摘要

Python 已成为事实上的科学计算语言。使用 Python 编程具有很高的生产力，这主要归功于围绕 NumPy 模块构建的丰富的面向科学的软件生态系统。因此，高性能计算（HPC）领域对 Python 支持的需求急剧上升。然而，Python 语言本身并不一定能提供高性能。这项工作提出了一种工作流，既能保持 Python 的高生产力，又能在不同架构间实现可移植的性能。该工作流的主要特点是面向高性能计算的语言扩展和一套由以数据为中心的中间表示法驱动的自动优化。我们展示了在 CPU、GPU、FPGA 和 Piz Daint 超级计算机（多达 23328 核）上的性能结果和扩展，与之前的最佳解决方案相比，速度分别提高了 2.47 倍和 3.75 倍，首次在赛灵思和英特尔 FPGA 上实现了注释 Python，并在 512 个节点上实现了高达 93.16% 的扩展效率。我们的基准在 2022 年超级计算大会（SC）期间的学生集群竞赛（SCC）中重现。我们介绍并讨论了学生团队的成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Productivity, Portability, Performance, and Reproducibility: Data-Centric Python

Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High-Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. This work presents a workflow that retains Python’s high productivity while achieving portable performance across different architectures. The workflow’s key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing Conference (SC) 2022. We present and discuss the student teams’ results.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.