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

标题：CRII：SHF：用于在GPU上进行灵活调度和启用调度的优化的编译器和运行时基础设施GPU(图形处理单元)的计算能力在于其丰富的内存带宽和巨大的并行性。然而，尽管它的硬件线程调度器能够快速地将计算分配给处理器，但往往无法有效地利用程序特征，仅实现了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，从而失去了程序级别的重要优化机会。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启用调度的优化。该研究的智力价值有两个方面：1)它开发了编译器技术来规避硬件线程调度器的限制，使程序员或运行库能够灵活地将任务调度到GPU处理器上；2)它设计了运行库优化来利用这种灵活的调度。该项目更广泛的意义和重要性在于，它为提高GPU计算时代数据密集型应用的计算效率提供了必要的支持，并由于这些应用的重要性，促进了科学、工程、人文和健康的持续进步。该项目设计了一个代码转换组件，以实现灵活的调度。这种变换被称为以流多处理器为中心的变换，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后退方案，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数量。该项目还设计了三种类型的优化，即并行性控制、基于亲和力的调度和处理器分区，它们利用调度支持对并行性、局部性和资源分配进行优化。该项目开发了静态和动态两种方法，以有效地搜索适合于处理各种节目和输入特征的最佳调度策略。

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.