Piper:通过编译器优化性能的管道OpenMP卸载执行

K. Parasyris, G. Georgakoudis, J. Doerfert, I. Laguna, T. Scogland
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引用次数: 0

摘要

OpenMP卸载提高了HPC GPU代码的应用程序开发复杂性,并提供了可移植性。性能差的一个来源是数据传输和计算的同步执行。重叠这些操作可以显著提高性能。但是,开发人员必须手动分割数据传输和内核执行,并有效地安排这些操作的执行,这是一项困难且容易出错的任务。我们提出了一种用于OpenMP卸载的自动机制Piper。Piper静态地分析卸载内核,并将计算与内存位置关联起来。扩展的运行时系统利用这些分析信息,将内核划分为独立的子任务,并将它们调度为流水线执行以实现重叠。在任何时间点,Piper还控制执行的子任务的粗糙程度和数量。通过这样做,Piper允许执行内存需求超过GPU设备内存的内核。与OpenMP卸载执行相比,Piper的执行速度高达2.67倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Piper: Pipelining OpenMP Offloading Execution Through Compiler Optimization For Performance
OpenMP offload improves the application development complexity of HPC GPU codes and provides portability. A source of poor performance is the lockstep execution of data transfers and computation. Overlapping these operations can provide significant performance gains. However, the developer must manually slice data transfers and kernel execution, and efficiently schedule these operations for execution, which is a hard and error-prone task.We propose Piper, an automatic mechanism for OpenMP offload to perform overlapping. Piper statically analyzes offload kernels and associates computations with memory locations. The extended runtime system exploits this analysis information, divides a kernel into independent sub-tasks, and schedules them for pipelined execution for overlapping. At any point in time, Piper also controls the coarseness and number of sub-tasks executed. By doing so, Piper allows the execution of kernels whose memory requirements exceed the GPU device memory. Piper speeds up execution up to 2.67× compared to OpenMP offload execution.
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