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Scope-aware data cache analysis for OpenMP programs on multi-core processors
Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Liaoning, Peoples R China.
Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Liaoning, Peoples R China.
2019 (English)In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 98, p. 443-452Article in journal (Refereed) Published
Abstract [en]

OpenMP is the de facto standard parallel programming framework on shared memory architectures, which is not only widely used in general and high-performance computing but also draws increasing interests for real-time embedded systems. Choosing the appropriate assignment of loop iterations to threads is one of the most critical decisions when parallelizing loops, especially considering the large impact by caches behaviors to the program execution time. In this paper, we study data cache analysis for OpenMP programs with parallel loops. We first present a method considering the impact of the schedule clause in OpenMP programs on cache behavior. We capture the dynamic behavior of memory access by computing its temporal scope (the loop iterations where a given memory block is accessed for a given data reference) during address analysis. Based on the ACS representation, we present a temporal scope aware data cache miss calculation technique. Through the experimental result, we propose a convenient way to choose an appropriate parallelization scheme for OpenMP programs.

Place, publisher, year, edition, pages
ELSEVIER , 2019. Vol. 98, p. 443-452
Keywords [en]
OpenMP, Multicores, Parallelism computing, Cache analysis
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-395700DOI: 10.1016/j.sysarc.2019.04.001ISI: 000487166300034OAI: oai:DiVA.org:uu-395700DiVA, id: diva2:1365056
Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-10-23Bibliographically approved

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