CSR: Medium: Collaborative Research: Programming Abstractions and Systems Support for GPU-Based Acceleration of Irregular Applications

CSR：媒介：协作研究：基于 GPU 的不规则应用加速的编程抽象和系统支持

• 批准号: 1406355
• 负责人: Keshav Pingali
• 金额: $ 73.97万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406355&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Programming

There is growing interest in using Graphics Processing Units (GPUs) to increase the performance and the energy efficiency of applications outside the graphics domain. GPUs are particularly suited to run regular programs that perform operations similar to pixel processing, and they can offer a large advantage over multicore CPUs in terms of performance, price, and energy efficiency in this domain. Not surprisingly, GPUs are increasingly appearing in devices ranging from handhelds to supercomputers.Although regular algorithms are very important, new problem domains such as computational biology, data mining, and social networks necessitate very different algorithmic foundations: they require building, computing with, and updating large graphs. Unfortunately, relatively little is understood about how to implement irregular applications efficiently on current GPU architectures. Features such as lockstep operation and the need to minimize thread divergence and maximize memory coalescing pose particular challenges to efficient implementation of irregular algorithms. Nevertheless, some recent successes in hand-porting irregular codes suggest that the difficulties lie not in the GPU hardware but in the immaturity of the state of the art of writing and tuning GPU code due to the lack of general, well-understood optimization techniques.This work will develop programming notations, compiler optimizations, and runtime system support that will enable programmers to express their algorithms at a high level of abstraction but still yield good performance. Projected tasks include producing highly optimized handwritten GPU implementations of important irregular algorithms and adding them to the LonestarGPU benchmark suite, identifying common patterns of optimizations and runtime systems support needed for efficient GPU implementations, developing a programming notation to permit the software developer to specify irregular algorithms at a high level of abstraction, implementing a synthesis system that automatically generates high-performance GPU code from these high-level specifications, and developing course material for teaching GPU programming of irregular codes.The higher performance and better energy efficiency of GPUs relative to multicore CPUs has sincere societal benifits. This work builds on the realization of these benefits by facilitating simpler and more widespread utilization of GPUs and incorporating more effective practices into future compilers and GPU hardware.

人们越来越有兴趣使用图形处理单元 (GPU) 来提高图形领域之外的应用程序的性能和能效。 GPU 特别适合运行执行类似于像素处理操作的常规程序，并且在该领域的性能、价格和能源效率方面，它们比多核 CPU 具有更大的优势。毫不奇怪，GPU 越来越多地出现在从手持设备到超级计算机等各种设备中。尽管常规算法非常重要，但计算生物学、数据挖掘和社交网络等新问题领域需要非常不同的算法基础：它们需要构建、计算和更新大图。不幸的是，人们对如何在当前 GPU 架构上有效实现不规则应用程序的了解相对较少。 诸如锁步操作以及最小化线程分歧和最大化内存合并的需要等特征对高效实现不规则算法提出了特殊的挑战。然而，最近在手工移植不规则代码方面取得的一些成功表明，困难不在于 GPU 硬件，而在于由于缺乏通用的、易于理解的优化技术，编写和调整 GPU 代码的技术水平还不成熟。这项工作将开发编程符号、编译器优化和运行时系统支持，使程序员能够在高抽象级别表达他们的算法，但仍能产生良好的性能。预计任务包括生成重要不规则算法的高度优化的手写 GPU 实现并将其添加到 LonestarGPU 基准套件中、识别高效 GPU 实现所需的常见优化模式和运行时系统支持、开发编程符号以允许软件开发人员指定不规则算法在高抽象层次上，实现一个综合系统，根据这些高层次规范自动生成高性能 GPU 代码，并开发用于教授不规则代码的 GPU 编程的课程材料。相对于多核，GPU 具有更高的性能和更好的能效CPU有诚意社会效益。这项工作建立在实现这些优势的基础上，通过促进 GPU 的更简单和更广泛的使用以及将更有效的实践纳入未来的编译器和 GPU 硬件中。