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Article type: Research Article
Authors: Tao, Jie | Karl, Wolfgang | Schulz, Martin
Affiliations: LRR-TUM, Institut für Informatik, Technische Universität München, 80290 München, Germany. Tel: +49-89-289-{28397,28278,28399}; E-mail: {tao,karlw,schulzm}@in.tum.de
Note: [] Jie Tao is a staff member of Jilin University, China and is currently pursuing her Ph.D at the Technische Universität München, Germany
Abstract: Shared memory applications running transparently on top of NUMA architectures often face severe performance problems due to bad data locality and excessive remote memory accesses. Optimizations with respect to data locality are therefore necessary, but require a fundamental understanding of an application's memory access behavior. The information necessary for this cannot be obtained using simple code instrumentation due to the implicit nature of the communication handled by the NUMA hardware, the large amount of traffic produced at runtime, and the fine access granularity in shared memory codes. In this paper an approach to overcome these problems and thereby to enable an easy and efficient optimization process is presented. Based on a low-level hardware monitoring facility in coordination with a comprehensive visualization tool, it enables the generation of memory access histograms capable of showing all memory accesses across the complete address space of an application's working set. This information can be used to identify access hot spots, to understand the dynamic behavior of shared memory applications, and to optimize applications using an application specific data layout resulting in significant performance improvements.
Journal: Scientific Programming, vol. 10, no. 1, pp. 45-53, 2002
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