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Automatic Detection of Performance Anomalies in Task-Parallel Programs

Andi Drebes 1 Karine Heydemann 1 Antoniu Pop 2 Albert Cohen 3 Nathalie Drach 1
1 ALSOC - Architecture et Logiciels pour Systèmes Embarqués sur Puce
LIP6 - Laboratoire d'Informatique de Paris 6
3 Parkas - Parallélisme de Kahn Synchrone
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR 8548
Abstract : To efficiently exploit the resources of new many-core architectures, integrating dozens or even hundreds of cores per chip, parallel programming models have evolved to expose massive amounts of parallelism, often in the form of fine-grained tasks. Task-parallel languages, such as OpenStream, X10, Habanero Java and C or StarSs, simplify the development of applications for new architectures, but tuning task-parallel applications remains a major challenge. Performance bottlenecks can occur at any level of the implementation, from the algorithmic level (e.g., lack of parallelism or over-synchronization), to interactions with the operating and runtime systems (e.g., data placement on NUMA architectures), to inefficient use of the hardware (e.g., frequent cache misses or misaligned memory accesses); detecting such issues and determining the exact cause is a difficult task. In previous work, we developed Aftermath, an interactive tool for trace-based performance analysis and debugging of task-parallel programs and run-time systems. In contrast to other trace-based analysis tools, such as Paraver or Vampir, Aftermath offers native support for tasks, i.e., visualization, statistics and analysis tools adapted for performance debugging at task granularity. However, the tool currently does not provide support for the automatic detection of performance bottlenecks and it is up to the user to investigate the relevant aspects of program execution by focusing the inspection on specific slices of a trace file. In this paper, we present ongoing work on two extensions that guide the user through this process.
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Contributor : Andi Drebes <>
Submitted on : Friday, March 27, 2015 - 2:12:28 PM
Last modification on : Wednesday, October 14, 2020 - 4:11:48 AM

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  • HAL Id : hal-01136497, version 1
  • ARXIV : 1405.2281


Andi Drebes, Karine Heydemann, Antoniu Pop, Albert Cohen, Nathalie Drach. Automatic Detection of Performance Anomalies in Task-Parallel Programs. 1st Workshop on Resource Awareness and Adaptivity in Multi-Core Computing (Racing 2014), May 2014, Paderborn, Germany. ⟨hal-01136497⟩



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