Objectives of PARSECMultithreaded ApplicationsFuture programs must run on multiprocessorsEmerging WorkloadsIncreasing CPU performance enables new applicationsDiverseMultiprocessors are being used for more and more tasksStateArt TechniquesAlgorithms and programming techniques evolve rapidlySupport ResearchOur goal is insight, not numbers JP Singh)Multiple input sets at different scales SPLASH2 and SPLASH2圎xisting SPLASH2 using the same frameworkUse parsecmgmtto manage, build, and run SPLASH2x (joint work with Prof. PARSEC 3.0 is coming soonNew framework Support network workloadsSupport citations to encourage contributionīlackscholes, bodytrack, canneal, dedup, facesim, ferret, fluidanimate, freqmine, vips, History of PARSECJan 2008 PARSEC 1.0 12 workloadsFeb 2009 PARSEC 2.0One new workload, raytraceAug 2009 PARSEC 2.1 Impact of PARSECGoogle Scholar Citations: 400+ The first version of PARSEC was created by Intel and Princeton University.We would like PARSEC to be a community project.Many people and institutions have already contributed. Other An opensource parallel benchmark suite of emerging applications for evaluating multicore and multiprocessor systemsApplication domains: financial, computer vision, physical modeling, future media, contentbased search, deduplicationCurrent releasePARSEC 2.1 (13 applications) The parsecmgmttoolBuilding & Running workloadsConfiguration filesīenchmark Suite for ChipMultiprocessorsStarted as a cooperation between Intel and Princeton University, many more have contributed since thenFreely available at:You can use it for your research OverviewHistory, Impact, Whats NewWorkloadsResearch on PARSEC ![]() The PARSEC Benchmark Suite TutorialPARSEC 3.0 YungangBao, Christian BieniaKai LiPrinceton University 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.Presentation on theme: "The PARSEC Benchmark Suite TutorialPARSEC 3.0 YungangBao, Christian Bi"- Presentation transcript 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. ![]() ![]() 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. 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). 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. The original PARSEC benchmark suite consists of a diverse and representative set of benchmark applications which are useful in evaluating shared-memory multicore architectures.
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