SHF: Small: Enabling and Analyzing Accuracy-aware Reliable GPU Computing

图形处理单元(GPU)正在成为通用硬件加速的默认选择，因为它们能够使大规模高性能计算应用程序的执行速度加快数量级且节能。由于在大型HPC系统上执行的大多数此类应用程序都是长时间运行的，因此它们处理各种基于硬件和软件的故障非常重要。许多前人的工作表明，实际的高性能计算机系统容易受到软错误的影响。缺乏必要的保护和检查点机制可能会导致科学生产力、运作效率降低，甚至造成金钱损失。然而，这些保护机制(例如纠错码)本身并不是免费的--它们会产生非常高的性能、能量和面积成本。该项目采用整体方法来探索降低这些保护开销的方法，利用了这样一个事实，即所有错误都不会导致应用程序输出的准确性出现不可接受的损失。以前的结果表明，GPGPU应用程序可以进行这种精确度方面的优化。为了实现这些优化，这个项目将解决三个主要的研究问题：a)需要什么硬件/软件支持和工具来确定哪些指令不容易受到软错误的影响；b)基于这个分析，哪些硬件组件(S)不需要保护以及保护多长时间，同时不牺牲超过用户质量要求的应用程序质量；以及c)在资源管理和调度方面需要进行哪些优化，以使低开销但可靠的计算更加有效和高效。这些问题将通过高性能计算(HPC)、大数据分析、机器学习和图形学领域出现的各种GPGPU应用程序来探索。如果成功，该项目将产生几个新的研究见解，将在实现低成本可靠的GPU计算方面发挥重要作用。该项目的成果将纳入现有的和新的关于计算机体系结构和可靠性的本科生和研究生课程，这将有助于培训学生，包括来自不同背景和少数群体的妇女和学生。

Graphics Processing Units (GPUs) are becoming the default choice for general-purpose hardware acceleration because of their ability to enable orders of magnitude faster and energy-efficient execution for large-scale high-performance computing applications. Since the majority of such applications executing on large-scale HPC systems are long-running, it is very important that they cope with a variety of hardware- and software-based faults. Many prior works have shown that real HPC systems are vulnerable to soft errors. An absence of essential protection and checkpointing mechanisms can lead to lower scientific productivity, operational efficiency, and even monetary loss. However, these protection mechanisms (e.g., error correction codes) are themselves not free -- they incur very high performance, energy, and area costs. This project takes a holistic approach to explore the avenues to reduce these protection overheads by taking advantage of the fact that all errors do not lead to an unacceptable loss in the accuracy of application output. Prior results show that GPGPU applications are amenable to such accuracy-aware optimizations. In order to enable these optimizations, this project will address three major research questions: a) What hardware/software support and tools are necessary to determine which instructions are not vulnerable to soft errors, b) Based on this analysis, which hardware component(s) need not be protected and for how long, while not sacrificing application quality beyond the user's quality requirements, and c) What optimizations in terms of resource management and scheduling are necessary to make low-overhead but reliable computation more effective and efficient. These questions will be explored via a variety of GPGPU applications emerging from the areas of high-performance computing (HPC), big-data analytics, machine learning, and graphics. If successful, this project will generate several novel research insights that will play an important role in enabling low-cost reliable GPU computing. The results of this project will be integrated into the existing and new undergraduate and graduate courses on computer architecture and reliability, which will facilitate in training students, including women and students from diverse backgrounds and minority groups.