Multiprocessor Performance Optimization using Slipstream Execution Mode

使用 Slipstream 执行模式优化多处理器性能

• 批准号: 0310847
• 负责人: Gregory Byrd
• 金额: $ 15.23万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2005-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0310847&HistoricalAwards=false
• 关键词: Multiprocessor; Performance; Optimization; using; Slipstream

ByrdMultiprocessor Performance Optimization using Slipstream Execution ModeAbstractSlipstream execution mode for multiprocessors involves running redundant copies of a parallel task on each processor in a dual-core chip multiprocessor (CMP). One copy of the task is shortened by removing synchronization events and stores to shared memory. This speeds up the execution of the other task through prefetching and other coherence optimizations enabled by a future view of shared memory references.The first objective of this project is to implement slipstream execution mode on actual CMP-based hardware. Given a multiprocessor built from dual-core CMP's, each with shared L2 cache, slipstream mode can be implemented with no changes to hardware. This implementation will validate the utility and scalability of sliptream-mode prefetching on full-sized applications. The second objective is to automate and optimize the mechanism for local synchronization between the redundant tasks, known as A-R synchronization. The choice of mode, ranging from tightly coupled to loosely coupled, can significantly affect the performance of an application. The project will develop run-time and compile-time mechanisms to select the best A-R synchronization mode for an application or region of execution.Slipstream execution mode is a new alternative for shared-memory parallel computation. As more systems are built from CMPs and multithreaded processors, it is imperative that we investigate efficient ways to exploit parallelism at all levels. Slipstream mode offers a performance boost for applications that cannot otherwise take advantage of additional thread-level parallelism. This work will impact important scientific applications, such as quantum mechanics and computational biology that use shared-memory multiprocessing to address large-scale problems.

Byrd 使用 Slipstream 执行模式优化多处理器性能摘要多处理器的 Slipstream 执行模式涉及在双核芯片多处理器 (CMP) 中的每个处理器上运行并行任务的冗余副本。 通过删除同步事件并将其存储到共享内存，可以缩短任务的一份副本。 这通过共享内存引用的未来视图实现的预取和其他一致性优化加快了其他任务的执行速度。该项目的第一个目标是在实际的基于 CMP 的硬件上实现滑流执行模式。 如果使用双核 CMP 构建的多处理器（每个处理器都具有共享的 L2 缓存），则无需更改硬件即可实现滑流模式。 该实现将验证滑流模式预取在全尺寸应用程序上的实用性和可扩展性。第二个目标是自动化和优化冗余任务之间的本地同步机制，称为 A-R 同步。 模式的选择（从紧密耦合到松散耦合）可以显着影响应用程序的性能。 该项目将开发运行时和编译时机制，为应用程序或执行区域选择最佳的 A-R 同步模式。Slipstream 执行模式是共享内存并行计算的新替代方案。 随着越来越多的系统由 CMP 和多线程处理器构建，我们必须研究在各个级别利用并行性的有效方法。 滑流模式为无法利用额外线程级并行性的应用程序提供了性能提升。 这项工作将影响重要的科学应用，例如使用共享内存多处理来解决大规模问题的量子力学和计算生物学。