PySchedCL：异构数据并行系统中的并发性利用
PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems
摘要：
在过去的十年中，异构GPGPU平台所展示的高性能计算能力已经导致了数据并行程序设计语言（如CUDA和OpenCL）的流行。然而，这样的语言涉及陡峭的学习曲线以及开发对异构平台中的计算设备的底层架构的广泛理解。这导致了一些高性能计算框架的出现，这些框架提供了高级别的抽象，以简化异构平台上的数据并行应用程序的开发。然而，这样的框架所进行的调度决策只利用粗粒度的并发数据并行应用程序。在本文中，我们提出PySchedCL，一个框架，探讨细粒度的并发感知调度决策，有效地利用异构CPU/GPU架构的力量。%，这是现有HPC框架不提供的功能。我们展示了现有的粗粒度的动态调度方案进行广泛的实验评估的机器学习为基础的推理应用程序，这样的调度机制的功效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
