Extending the Limits of Large-Scale Shared Memory Multiprocessors

扩展大规模共享内存多处理器的限制

• 批准号: 0444470
• 负责人: Oyekunle Olukotun
• 金额: $ 75万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-11-01 至 2007-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0444470&HistoricalAwards=false
• 关键词: Extending; Limits; Large; Scale; Shared

The objective of this research is to substantially improve the productivity of programmers writingapplications for petaflop-scale systems by using programmer defined light-weight transactions as thesingle abstraction for expressing parallelism, delineating communication, reasoning about memoryconsistency, providing failure recovery, and allowing performance optimization. Transactions as thecentral abstraction for designing and programming parallel systems leads to a shared memoryprogramming and memory coherence model called Transaction Coherence and Consistency (TCC).Transactions simplify parallel programming by providing a way of writing correct shared-memoryprograms without threads, locks and semaphores. TCC systems provide high performance communicationand synchronization with support for hardware mechanisms that can keep memory coherent andconsistent based on programmer-defined transactions.To achieve the research objective, this research program will focus on five activities. First, the researcherswill develop new abstractions that use transactions to provide a shared memory programming model thatmakes it much easier to analyze and optimize application performance. Second, the researchers willdevelop performance monitoring systems that make use of transactions to detect performance bottlenecksand to provide intuitive feedback to programmers. Third, the researchers will use the transaction basedprogramming model to implement compiler-based static and dynamic feedback-directed optimizationsthat automatically detect and eliminate performance bottlenecks and extend the scalability of transactioncoherency to 105 processors. Fourth, the researchers will use transactions to optimize the performance ofparallel storage I/O. Finally, the researchers will develop simulation and emulation technology that willenable us to experiment with petaflop-scale systems that support light-weight transactions before they areavailable.Broader ImpactsThe broad impact of this research is to use transaction-based parallel programming to educate and enablea new class of parallel software developers who can implement parallel software with the same facilitythat sequential software is written today. Enabling parallel software development will be critical toadvancing computing performance from desktop applications to large-scale scientific and commercialapplications. While parallel processing has been essential for large-scale machines for a while, recentannouncements by Intel, AMD and IBM demonstrate that it will soon be critical for desktop applicationsas well. To educate students, other researchers, and industry about the benefits of transaction-basedparallel programming, we will incorporate transactional programming concepts in the parallelprogramming curriculum and make transaction-based applications available to the wider scientificcommunity. The researchers expect that releasing a suite of optimized transaction-based applicationsalong with simulation technology will be instrumental in encouraging other researchers to experimentwith and explore the benefits of transactions. To further promote the use of transaction-based parallelprogramming we will organize a tutorial or workshop at a major scientific computing conference that willcover the principles and experience of programming with transactions.

本研究的目的是通过使用程序员定义的轻量级事务作为表示并行性、描述通信、推理内存一致性、提供故障恢复和允许性能优化的单一抽象，来大幅提高程序员为petaflop规模系统编写应用程序的生产率。事务作为并行系统设计和编程的核心抽象，提出了一种共享内存编程和内存一致性模型，称为事务一致性和一致性（TransactionCoherence and Consistency，TCC），它提供了一种无需线程、锁和信号量就能编写正确的共享内存程序的方法，从而简化了并行程序设计。TCC系统提供了高性能的通信和同步，支持硬件机制，可以根据程序员定义的事务保持内存一致性和一致性。首先，研究人员将开发新的抽象，使用事务来提供共享内存编程模型，使分析和优化应用程序性能变得更加容易。其次，研究人员将开发性能监控系统，利用事务来检测性能检查，并为程序员提供直观的反馈。第三，研究人员将使用基于事务的编程模型来实现基于编译器的静态和动态反馈导向优化，自动检测和消除性能瓶颈，并将transactioncoherency的可扩展性扩展到105个处理器。第四，研究人员将使用事务来优化并行存储I/O的性能。最后，研究人员将开发模拟和仿真技术，使我们能够在支持轻量级事务的千万次浮点运算规模系统可用之前对其进行实验。更广泛的影响这项研究的广泛影响是使用基于事务的并行编程来教育和支持一类新的并行软件开发人员，他们可以使用与今天编写的顺序软件相同的设施来实现并行软件。支持并行软件开发对于提高从桌面应用到大规模科学和商业应用的计算性能至关重要。虽然并行处理对于大规模机器来说已经是必不可少的一段时间了，但英特尔、AMD和IBM最近的声明表明，它很快也将对桌面应用程序至关重要。为了让学生、其他研究人员和业界了解基于事务的并行编程的好处，我们将在并行编程课程中纳入事务编程的概念，并使基于事务的应用程序可供更广泛的科学界使用。研究人员预计，发布一套优化的基于交易的应用程序以及模拟技术将有助于鼓励其他研究人员试验和探索交易的好处。为了进一步促进基于事务的并行编程的使用，我们将在一个主要的科学计算会议上组织一个教程或研讨会，其中将涵盖使用事务编程的原理和经验.