MIST: Towards MPI Instant Startup and Termination on Tianhe HPC Systems

IF 6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-09-11 DOI:10.1109/TPDS.2025.3608434

Yiqin Dai;Ruibo Wang;Yong Dong;Min Xie;Juan Chen;Wenzhe Zhang;Huijun Wu;Mingtian Shao;Kai Lu

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引用次数: 0

Abstract

As the size of MPI programs grows with expanding HPC resources and parallelism demands, the overhead of MPI startup and termination escalates due to the inclusion of less scalable global operations. Global operations involving extensive cross-machine communication and synchronization are crucial for ensuring semantic correctness. The current focus is on optimizing and accelerating these global operations rather than removing them, as the latter involves systematic changes to the system software stack and may impact program semantics. Given this background, we propose a systematic solution named MIST to safely eliminate global operations in MPI startup and termination. Through optimizing the generation of communication addresses, designing reliable communication protocols, and exploiting the resource release mechanism, MIST eliminates all global operations to achieve MPI instant startup and termination while ensuring correct program execution. Experiments on Tianhe-2 A supercomputer demonstrate that MIST can reduce the MPI_Init() time by 32.5-77.6% and the MPI_Finalize() time by 28.9-85.0%.

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MIST：在天河高性能计算系统上实现MPI即时启动和终止

随着高性能计算资源和并行性需求的扩展，MPI程序的规模也在不断增长，MPI启动和终止的开销也在不断增加，因为包含的可扩展性较低的全局操作。涉及广泛的跨机器通信和同步的全局操作对于确保语义正确性至关重要。当前的重点是优化和加速这些全局操作，而不是删除它们，因为后者涉及系统软件堆栈的系统更改，并可能影响程序语义。在此背景下，我们提出了一个名为MIST的系统解决方案，以安全地消除MPI启动和终止中的全局操作。通过优化通信地址的生成，设计可靠的通信协议，利用资源释放机制，MIST消除了所有全局操作，在保证程序正确执行的同时，实现了MPI的即时启动和终止。在天河2a超级计算机上的实验表明，MIST可以使MPI_Init（）时间缩短32.5 ~ 77.6%,MPI_Finalize（）时间缩短28.9 ~ 85.0%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.