TP-PARSEC: A Task Parallel PARSEC Benchmark Suite

• An, Huynh

• Christian, Helm

• Shintaro, Iwasaki

• Wataru, Endo

• Byambajav, Namsraijav

• Kenjiro, Taura

抄録

The original PARSEC benchmark suite consists of a diverse and representative set of benchmark applications which are useful in evaluating shared-memory multicore architectures. However, it supports only three programming models: Pthreads (SPMD), OpenMP (parallel for), TBB (parallel for, pipeline), lacking support for emerging and widespread task parallel programming models. In this work, we present a task-parallelized PARSEC (TP-PARSEC) in which we have added translations for five different task parallel programming models (Cilk Plus, MassiveThreads, OpenMP Tasks, Qthreads, TBB). Task parallelism enables a more intuitive description of parallel algorithms compared with the direct threading SPMD approach, and ensures a better load balance on a large number of processor cores with the proven work stealing scheduling technique. TP-PARSEC is not only useful for task parallel system developers to analyze their runtime systems with a wide range of workloads from diverse areas, but also enables them to compare performance differences between systems. TP-PARSEC is integrated with a task-centric performance analysis and visualization tool which effectively helps users understand the performance, pinpoint performance bottlenecks, and especially analyze performance differences between systems.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)------------------------------

The original PARSEC benchmark suite consists of a diverse and representative set of benchmark applications which are useful in evaluating shared-memory multicore architectures. However, it supports only three programming models: Pthreads (SPMD), OpenMP (parallel for), TBB (parallel for, pipeline), lacking support for emerging and widespread task parallel programming models. In this work, we present a task-parallelized PARSEC (TP-PARSEC) in which we have added translations for five different task parallel programming models (Cilk Plus, MassiveThreads, OpenMP Tasks, Qthreads, TBB). Task parallelism enables a more intuitive description of parallel algorithms compared with the direct threading SPMD approach, and ensures a better load balance on a large number of processor cores with the proven work stealing scheduling technique. TP-PARSEC is not only useful for task parallel system developers to analyze their runtime systems with a wide range of workloads from diverse areas, but also enables them to compare performance differences between systems. TP-PARSEC is integrated with a task-centric performance analysis and visualization tool which effectively helps users understand the performance, pinpoint performance bottlenecks, and especially analyze performance differences between systems.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)------------------------------

収録刊行物

• 情報処理学会論文誌コンピューティングシステム（ACS）

  情報処理学会論文誌コンピューティングシステム（ACS） 12 (1), 2019-01-21

キーワード

• benchmark suite, task parallelism, programming models, performance analysis, scheduling delay

詳細情報

• CRID: 1050282812886480128
• NII論文ID: 170000150052
• NII書誌ID: AA11833852
• ISSN: 18827829
• Web Site: http://id.nii.ac.jp/1001/00193856/
• 本文言語コード: en
• 資料種別: article
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