Multiprocessor Performance Optimization using Slipstream Execution Mode

Byrd使用滑流执行模式的多处理器性能优化摘要用于多处理器的滑流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。 任务的一个副本通过删除同步事件和存储到共享内存来缩短。 这加快了其他任务的执行，通过预取和其他一致性优化启用共享内存references.The第一个目标，该项目是实现滑流执行模式在实际的CMP为基础的硬件。 给定一个由双核CMP构建的多处理器，每个都有共享的L2缓存，滑流模式可以在不改变硬件的情况下实现。 此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（从紧耦合到松耦合）会显著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。滑流执行模式是共享内存并行计算的一种新选择。 随着越来越多的系统是由CMP和多线程处理器构建的，我们必须研究有效的方法来利用各级并行性。 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，例如量子力学和计算生物学，它们使用共享内存多处理来解决大规模问题。

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.