基于指令的高效GPU计算的交互式程序调试与优化

Seyong Lee, Dong Li, J. Vetter
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引用次数: 10

摘要

基于指令的GPU编程模型正在获得动力,因为它们透明地将程序员从处理低级GPU编程的复杂性中解放出来,低级GPU编程通常反映底层架构。然而,指令模型中太多的抽象给程序员调试应用程序和调优性能带来了沉重的负担。本文提出了一种基于指令的交互式程序调试与优化系统。该系统支持程序员、编译器和运行时之间的直观和协同交互,以实现更高效的GPU计算。我们在新的开源编译器框架中设计并实现了一系列原型工具,称为open Accelerator Research compiler (open ARC), open ARC支持Opencast V1.0的全部特性集。我们对12个Open ACC基准测试的评估表明,我们的原型调试和优化系统可以检测到各种翻译错误。此外,与完全手动的内存管理方案相比,我们的原型提供的优化最大限度地减少了内存传输。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing
Directive-based GPU programming models are gaining momentum, since they transparently relieve programmers from dealing with complexity of low-level GPU programming, which often reflects the underlying architecture. However, too much abstraction in directive models puts a significant burden on programmers for debugging applications and tuning performance. In this paper, we propose a directive-based, interactive program debugging and optimization system. This system enables intuitive and synergistic interaction among programmers, compilers, and runtimes for more productive and efficient GPU computing. We have designed and implemented a series of prototype tools within our new open source compiler framework, called Open Accelerator Research Compiler (Open ARC), Open ARC supports the full feature set of Opencast V1.0. Our evaluation on twelve Open ACC benchmarks demonstrates that our prototype debugging and optimization system can detect a variety of translation errors. Additionally, the optimization provided by our prototype minimizes memory transfers, when compared to a fully manual memory management scheme.
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