Future supercomputers will require application developers to expose much more parallelism than current applications expose. In order to assist application developers in structuring their applications such that this is possible, new programming models and libraries are emerging, the many-task runtimes , to allow for the expression of orders of magnitude more parallelism than currently existing models.;This dissertation describes the challenges that these emerging many-task runtimes will place on performance analysis, and proposes deep integration between runtimes and performance tools as a means of producing correct, insightful, and actionable performance results. I show how tool-runtime integration can be used to aid programmer understanding of performance characteristics and to provide online performance feedback to the runtime for Unified Parallel C (UPC), High Performance ParalleX (HPX), Apache Spark, the Open Community Runtime, and the OpenMP runtime.
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机译:未来的超级计算机将要求应用程序开发人员提供比当前应用程序更多的并行性。为了帮助应用程序开发人员构建应用程序,以实现这种可能,新的编程模型和库不断涌现,多任务运行时允许比当前现有模型表达更多数量级的并行性。这些新兴的多任务运行时将对性能分析提出挑战,并建议在运行时和性能工具之间进行深度集成,以产生正确,有见地和可操作的性能结果。我将展示如何使用工具-运行时集成来帮助程序员理解性能特征,并为运行时提供在线性能反馈,以支持Unified Parallel C(UPC),High Performance ParalleX(HPX),Apache Spark,Open Community Runtime和OpenMP运行时。
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