CRII: SHF: A Compiler and Runtime Infrastructure for Flexible Scheduling and Scheduling-Enabled Optimizations on GPUs

CRII：SHF：用于 GPU 上灵活调度和启用调度优化的编译器和运行时基础架构

• 批准号: 1464216
• 负责人: Bo Wu
• 金额: $ 17.5万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464216&HistoricalAwards=false
• 关键词: CRII; SHF; Compiler; Runtime; Infrastructure

Title: CRII:SHF: A Compiler and Runtime Infrastructure for Flexible Scheduling and Scheduling-Enabled Optimizations on GPUsThe computing power of a GPU (Graphics Processing Unit) lies in its abundant memory bandwidth and massive parallelism. However, its hardware thread schedulers, despite being able to quickly distribute computation to processors, often fail to capitalize on program characteristics effectively, achieving only a fraction of the GPUs' full potential. Moreover, current GPUs do not allow programmers or compilers to control thread scheduling, forfeiting important optimization opportunities at the program level. This research aims to develop a new software-level infrastructure for flexible scheduling and scheduling-enabled optimizations on GPUs. The intellectual merits of the research are two-fold: 1) It develops compiler techniques to circumvent the restrictions from the hardware thread scheduler, which enable programmers or the runtime to flexibly schedule tasks to the GPU processors; 2) It designs runtime optimizations to leverage the flexible scheduling. The project's broader significance and importance are that it provides essential support enhancing the computing efficiency of data-intensive applications in the era of GPU computing and, due to the importance of these applications, fosters sustained advances in science, engineering, humanity, and health.The project designs a code transformation component to enable flexible scheduling. The transformation, named SM (Streaming Multiprocessor)-centric transformation, consists of two techniques. The first technique is SM-centric task selection, which breaks the mapping between tasks and thread blocks and directly associates tasks with processors. The second technique is a filling and retreating scheme, which addresses some behaviors of the hardware scheduler and flexibly controls the number of active tasks for each processor. The project also designs three types of optimizations, namely parallelism control, affinity-based scheduling, and processor partitioning, which leverage the scheduling support to optimize for parallelism, locality, and resource allocation. The project develops both static and dynamic approaches to efficiently searching for the optimal scheduling strategies adapted to address various program and input features.

职务名称：CRII：SHF：GPU上的灵活调度和支持并行优化的并行和可扩展基础架构GPU（图形处理单元）的计算能力在于其丰富的内存带宽和大规模并行性。然而，尽管其硬件线程处理器能够快速将计算分配给处理器，但通常无法有效地利用程序特性，只能实现GPU全部潜力的一小部分。此外，当前的GPU不允许程序员或编译器控制线程调度，从而丧失了在程序级的重要优化机会。这项研究旨在开发一种新的软件级基础设施，用于在GPU上进行灵活的调度和支持并行处理的优化。该研究的智力价值是两方面的：1）它开发了编译器技术，以规避来自硬件线程调度器的限制，使程序员或运行时能够灵活地将任务调度到GPU处理器; 2）它设计了运行时优化，以利用灵活的调度。该项目的更广泛意义和重要性在于，它为GPU计算时代的数据密集型应用程序的计算效率提供了必要的支持，并由于这些应用程序的重要性，促进了科学，工程，人类和健康的持续进步。该项目设计了代码转换组件，以实现灵活的调度。这种转换称为以流多处理器为中心的转换，它由两种技术组成。第一种技术是以SM为中心的任务选择，它打破了任务和线程块之间的映射，直接将任务与处理器相关联。第二种技术是填充和撤退计划，它解决了硬件调度器的一些行为，并灵活地控制每个处理器的活动任务的数量。该项目还设计了三种类型的优化，即并行度控制，基于亲和力的调度和处理器分区，利用调度支持来优化并行度，局部性和资源分配。该项目开发了静态和动态的方法，以有效地搜索适合于解决各种程序和输入功能的最佳调度策略。