In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep learning curve as well as developing an extensive understanding of the underlying architecture of the compute devices in heterogeneous platforms. This has led to the emergence of several High Performance Computing frameworks which provide high-level abstractions for easing the development of data-parallel applications on heterogeneous platforms. However, the scheduling decisions undertaken by such frameworks only exploit coarse-grained concurrency in data parallel applications. In this paper, we propose PySchedCL, a framework which explores fine-grained concurrency aware scheduling decisions that harness the power of heterogeneous CPU/GPU architectures efficiently. %, a feature which is not provided by existing HPC frameworks. We showcase the efficacy of such scheduling mechanisms over existing coarse-grained dynamic scheduling schemes by conducting extensive experimental evaluations for a Machine Learning based inferencing application.

中文翻译：

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PySchedCL：在异构数据并行系统中利用并发

在过去的十年中，异构GPGPU平台所展示的高性能计算能力导致了CUDA和OpenCL等数据并行编程语言的流行。然而，此类语言涉及陡峭的学习曲线以及对异构平台中计算设备的底层架构的广泛理解。这导致了几个高性能计算框架的出现，这些框架提供了高级抽象，以简化异构平台上数据并行应用程序的开发。然而，由此类框架进行的调度决策仅利用数据并行应用程序中的粗粒度并发。在本文中，我们提出了 PySchedCL，一个探索细粒度并发感知调度决策的框架，可有效利用异构 CPU/GPU 架构的力量。%，现有 HPC 框架不提供的功能。我们通过对基于机器学习的推理应用程序进行广泛的实验评估，展示了这种调度机制相对于现有粗粒度动态调度方案的有效性。