Identifying Performance Inefficiencies of Parallel Program With Spatial and Temporal Trace Analysis

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

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-03-02 DOI:10.1109/TPDS.2025.3566735

Zhibo Xuan;Xin Sun;Xin You;Hailong Yang;Zhongzhi Luan;Yi Liu;Depei Qian

{"title":"Identifying Performance Inefficiencies of Parallel Program With Spatial and Temporal Trace Analysis","authors":"Zhibo Xuan;Xin Sun;Xin You;Hailong Yang;Zhongzhi Luan;Yi Liu;Depei Qian","doi":"10.1109/TPDS.2025.3566735","DOIUrl":null,"url":null,"abstract":"Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose <italic>STAD</i>, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. <italic>STAD</i> captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, <italic>STAD</i> diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that <italic>STAD</i> can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 7","pages":"1387-1400"},"PeriodicalIF":5.6000,"publicationDate":"2025-03-02","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/10982439/","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

Abstract

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment.

查看原文本刊更多论文

利用空间和时间轨迹分析识别并行程序的性能低下

性能效率低下会导致并行程序中的性能异常。现有的性能分析工具要么检测范围有限，要么需要大量的领域知识才能使用，这限制了它们在识别性能低下方面的实际应用。在本文中，我们提出了STAD，一个并行程序的性能分析工具，它考虑了跟踪数据中的空间和时间模式。STAD使用空间通信模式图捕获进程之间的空间通信模式。然后，采用基于动态图神经网络的无监督模型来学习时间轴上不断变化的时间模式。此外，STAD通过利用沿调用树的异常聚合特征来诊断性能异常的根本原因。我们的评估结果表明，STAD可以在可接受的开销下有效地检测性能异常，并诊断程序本身和运行环境的根本原因。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

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.