Preventing the explosion of exascale profile data with smart thread-level aggregation

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. General TermsAlgorithms, experientation, measurement Keywords Performance analysis, data compression, exascale computing Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. ESPT2015