State of the art performance analysis tools, such as Score-P, record performance profiles on a per-thread basis. However, for exascale systems the number of threads is expected to be in the order of a billion threads, and this would result in extremely large performance profiles. In most cases the user almost never inspects the individual per-thread data. In this paper, we propose to aggregate per-thread performance data in each process to reduce its amount to a reasonable size. Our goal is to aggregate the threads such that the thread-level performance issues are still visible and analyzable. Therefore, we implemented four aggregation strategies in Score-P: (i) SUM -- aggregates all threads of a process into a process profile; (ii) SET -- calculates statistical key data as well as the sum; (iii) KEY -- identifies three threads (i.e., key threads) of particular interest for performance analysis and aggregates the rest of the threads; (iv) CALLTREE -- clusters threads that have the same call-tree structure. For each one of these strategies we evaluate the compression ratio and how they maintain thread-level performance behavior information. The aggregation does not incur any additional performance overhead at application run-time.
{"title":"Preventing the explosion of exascale profile data with smart thread-level aggregation","authors":"Daniel Lorenz, Sergei Shudler, F. Wolf","doi":"10.1145/2832106.2832107","DOIUrl":"https://doi.org/10.1145/2832106.2832107","url":null,"abstract":"State of the art performance analysis tools, such as Score-P, record performance profiles on a per-thread basis. However, for exascale systems the number of threads is expected to be in the order of a billion threads, and this would result in extremely large performance profiles. In most cases the user almost never inspects the individual per-thread data. In this paper, we propose to aggregate per-thread performance data in each process to reduce its amount to a reasonable size. Our goal is to aggregate the threads such that the thread-level performance issues are still visible and analyzable. Therefore, we implemented four aggregation strategies in Score-P: (i) SUM -- aggregates all threads of a process into a process profile; (ii) SET -- calculates statistical key data as well as the sum; (iii) KEY -- identifies three threads (i.e., key threads) of particular interest for performance analysis and aggregates the rest of the threads; (iv) CALLTREE -- clusters threads that have the same call-tree structure. For each one of these strategies we evaluate the compression ratio and how they maintain thread-level performance behavior information. The aggregation does not incur any additional performance overhead at application run-time.","PeriodicalId":424753,"journal":{"name":"ESPT '15","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115631776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoqing Luo, F. Mueller, P. Carns, John Jenkins, R. Latham, R. Ross, S. Snyder
Today's rapid development of supercomputers has caused I/O performance to become a major performance bottleneck for many scientific applications. Trace analysis tools have thus become vital for diagnosing root causes of I/O problems. This work contributes an I/O tracing framework with elastic traces. After gathering a set of smaller traces, we extrapolate the application trace to a large numbers of nodes. The traces can in principle be extrapolated even beyond the scale of present-day systems. Experiments with I/O benchmarks on up to 320 processors indicate that extrapolated I/O trace replays closely resemble the I/O behavior of equivalent applications.
{"title":"HPC I/O trace extrapolation","authors":"Xiaoqing Luo, F. Mueller, P. Carns, John Jenkins, R. Latham, R. Ross, S. Snyder","doi":"10.1145/2832106.2832108","DOIUrl":"https://doi.org/10.1145/2832106.2832108","url":null,"abstract":"Today's rapid development of supercomputers has caused I/O performance to become a major performance bottleneck for many scientific applications. Trace analysis tools have thus become vital for diagnosing root causes of I/O problems.\u0000 This work contributes an I/O tracing framework with elastic traces. After gathering a set of smaller traces, we extrapolate the application trace to a large numbers of nodes. The traces can in principle be extrapolated even beyond the scale of present-day systems. Experiments with I/O benchmarks on up to 320 processors indicate that extrapolated I/O trace replays closely resemble the I/O behavior of equivalent applications.","PeriodicalId":424753,"journal":{"name":"ESPT '15","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123037548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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