Efficient Compilation Issues for Scalable Distributed-Memory Multicomputers

可扩展分布式内存多计算机的高效编译问题

• 批准号: 9526325
• 负责人: Prithviraj Banerjee
• 金额: $ 9.14万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-15 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9526325&HistoricalAwards=false
• 关键词: Efficient; Compilation; Issues; Scalable; Distributed

Distributed-memory, massively-parallel multicomputers can provide the high levels of performance required to solve the Grand Challenge computational science problems. Multicomputers offer significant advantages over shared- memory multiprocessors in terms of cost and scalability. Unfortunately, extracting all the computational power from these machines requires users to write efficient software for them, which is an extremely laborious and error-prone process. The distribution of data across processors is of critical importance to the efficiency of the parallel program in a distributed memory system. In this project the problem of automated data distribution in such machines is being investigated. The approach is unique in that it is based on sophisticated cost models of communication and computation that are parameterized by architectural metrics empirically measured for different target machines. Using the cost estimates, data distributions are selected to minimize the overall execution time of the program by maximizing parallelism (while maintaining load balance) and minimizing the amount of communication overhead (by maximizing data locality). Both static and dynamic distribution will be supported in a unified framework to automatically select data distributions which can dynamically change over the course of a program's execution in order to provide scalable parallel performance for large, complex applications. This approach not only has a solid theoretical basis, but is being integrated into a sophisticated compiler which can actually generate code for a variable number of processors supporting all block, cyclic, and block-cyclic data distributions. The compilation techniques investigated in this project will also be integrated with simultaneous support for regular and irregular accesses in parallel applications through a novel interval-based representation. ***

分布式内存、大规模并行的多计算机可以提供解决计算科学问题所需的高水平性能。与共享内存多处理器相比，多计算机在成本和可伸缩性方面具有显著优势。不幸的是，从这些机器中提取所有的计算能力需要用户为它们编写高效的软件，这是一个极其费力和容易出错的过程。在分布式存储系统中，跨处理器的数据分布对于并行程序的效率至关重要。在这个项目中，正在研究这类机器中的自动数据分发问题。该方法的独特之处在于，它基于复杂的通信和计算成本模型，这些模型由针对不同目标机器的经验性测量的体系结构度量进行参数化。使用成本估计，选择数据分布以通过最大化并行性(同时保持负载平衡)和最小化通信开销(通过最大化数据局部性)来最小化程序的总体执行时间。在一个统一的框架中将支持静态和动态分布，以自动选择在程序执行过程中可以动态变化的数据分布，以便为大型、复杂的应用程序提供可扩展的并行性能。这种方法不仅有坚实的理论基础，而且正在被集成到一个复杂的编译器中，该编译器实际上可以为支持所有块、循环和块循环数据分布的可变数量的处理器生成代码。本项目中研究的编译技术还将通过一种新的基于间隔的表示法与并行应用程序中的常规和非常规访问的同时支持相结合。***