SHF:Small:Scalable Scheduling for Program Transformations in Heterogeneous Computing

SHF:Small：异构计算中程序转换的可扩展调度

• 批准号: 1321147
• 负责人: Louis-Noel Pouchet
• 金额: $ 42.42万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321147&HistoricalAwards=false
• 关键词: SHF; Small; Scalable; Scheduling; Program

Optimizing compilers are asked to automatically achieve good performance over an increasingly larger and heterogeneous set of architectures. Complex high-level program transformations are required to address this problem, to map the proper grain of independent computation and the proper data locality to a complex hierarchy of memory, computing and interconnection resources. The polyhedral compilation framework is one of the most powerful and flexible loop transformation system, with numerous compelling results achieved in recent years in terms of automatic program optimization (CPUs, GPUs and FPGAs). But a difficult challenge remains the deployment of those research results to larger-scale programs. Indeed, this framework uses complex mathematical algorithms that are the reason for the better program performance achieved, but which are often too time consuming for production use.The goal of this project is to significantly improve the scalability and effectiveness of polyhedral optimizations, through the design of exact optimization methods and their associated approximation heuristics for increased scalability. We will develop novel program transformation algorithms operating under hardware resources constraints, for a variety of devices currently available on heterogeneous computing systems: for multi-core CPUs using short-vector SIMD units; for FPGAs with the help of high-level synthesis tool-chain; and for GPUs. The proposed work has the potential to significantly enhance the effectiveness of optimizing compilers thereby reducing the manual performance tuning required, with significant cost savings. The developed tools will be made publicly and freely available to the research community.

优化编译器被要求在越来越大和不同种类的体系结构集上自动获得良好的性能。需要复杂的高级程序转换来解决这个问题，将适当的独立计算粒度和适当的数据局部性映射到内存、计算和互连资源的复杂层次结构。多面体编译框架是最强大和最灵活的循环转换系统之一，近年来在自动程序优化(CPU、GPU和FPGA)方面取得了许多引人注目的成果。但一个困难的挑战仍然是将这些研究成果部署到更大规模的项目中。事实上，该框架使用了复杂的数学算法，这是获得更好的程序性能的原因，但对于生产使用来说，这些算法往往太耗时。该项目的目标是通过设计精确的优化方法及其相关的近似启发式算法来显著提高多面体优化的可伸缩性和有效性。我们将开发在硬件资源限制下运行的新的程序转换算法，适用于目前在异质计算系统上可用的各种设备：用于使用短矢量SIMD单元的多核CPU；用于借助高级综合工具链的现场可编程门阵列；以及用于图形处理器。拟议的工作有可能显著提高优化编译器的有效性，从而减少所需的手动性能调整，并显著节省成本。开发的工具将向研究界公开和免费提供。