通过缓存工作集预测快速线程迁移

Jeffery A. Brown, Leo Porter, D. Tullsen
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引用次数: 44

摘要

当线程在内核之间迁移时,最重要的性能损失来源是缓存状态的丢失。如果缓存工作集可以与线程一起主动移动,那么迁移后的性能就有可能得到显著提升。这项工作通过预测和预取应用程序的工作集到新的缓存中来加速迁移后的线程启动性能。它表明,简单地移动缓存状态的性能很差,并且移动指令工作集可能比数据更重要。本文演示了一种技术,该技术捕获线程的访问行为,将该行为总结为紧凑的形式,以便在内核之间传输,然后根据摘要将适当的数据预取到新的缓存中。它详细研究了单线程迁移的影响,然后演示了它在推测的多线程体系结构上的实用性。工作集预测可以将寿命较短的线程的性能提高一倍,并且在完全推测的多线程实现中,该技术还可以将派生线程的效率提高近一倍。
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Fast thread migration via cache working set prediction
The most significant source of lost performance when a thread migrates between cores is the loss of cache state. A significant boost in post-migration performance is possible if the cache working set can be moved, proactively, with the thread. This work accelerates thread startup performance after migration by predicting and prefetching the working set of the application into the new cache. It shows that simply moving cache state performs poorly, and that moving the instruction working set can be even more critical than data. This paper demonstrates a technique that captures the access behavior of a thread, summarizes that behavior into a compact form for transfer between cores, and then prefetches appropriate data into the new caches based on the summary. It presents a detailed study of single-thread migration effects, and then demonstrates its utility on a speculative multithreading architecture. Working set prediction as much as doubles the performance of short-lived threads, and in a full speculative multithreading implementation, the technique is also shown to nearly double the effectiveness of the spawned threads.
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