在多处理器中为线程级推测缓冲内存状态的权衡

M. Garzarán, Milos Prvulović, J. Llabería, V. Viñals, Lawrence Rauchwerger, J. Torrellas
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引用次数: 55

摘要

线程级推测为并行运行难以分析的代码提供了架构支持。由于推测任务并发运行,它们会生成不安全或推测的内存状态,这些状态需要在分布式缓存和缓冲区中单独缓冲和管理。这种状态可能包含同一变量的多个版本。在本文中,我们介绍了在多处理器中缓冲和管理多版本推测内存状态的一种新的方法分类。我们还对不同方法进行了详细的复杂性-效益权衡分析。最后,我们使用数值应用来评估在单一架构框架下这些方法的性能。我们的主要见解是,支持缓冲每个处理器的多个推测任务和版本的状态比支持惰性地合并主存的任务状态更有效。此外,这两种支撑可以有效地组合在一起,在大型机器中,它们的效果几乎是完全相加的。最后,对主存中未来状态的更复杂的支持可以在缓冲区处于压力下时提高性能,但在频繁挤压时损害性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tradeoffs in buffering memory state for thread-level speculation in multiprocessors
Thread-level speculation provides architectural support to aggressively run hard-to-analyze code in parallel. As speculative tasks run concurrently, they generate unsafe or speculative memory state that needs to be separately buffered and managed in the presence of distributed caches and buffers. Such state may contain multiple versions of the same variable. In this paper, we introduce a novel taxonomy of approaches to buffering and managing multi-version speculative memory state in multiprocessors. We also present a detailed complexity-benefit tradeoff analysis of the different approaches. Finally, we use numerical applications to evaluate the performance of the approaches under a single architectural framework. Our key insights are that support for buffering the state of multiple speculative tasks and versions per processor is more complexity-effective than support for merging the state of tasks with main memory lazily. Moreover, both supports can be gainfully combined and, in large machines, their effect is nearly fully additive. Finally, the more complex support for future state in main memory can boost performance when buffers are under pressure, but hurts performance when squashes are frequent.
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