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Understanding How Non-uniform Distribution of Memory Accesses on Cache Sets Affects the System Performance of Chip Multiprocessors

机译:了解缓存集上的内存访问的不均匀分布如何影响芯片多处理器的系统性能

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Non-uniform distribution of memory accesses across cache sets has been recognized as one of the sources of inefficiency of cache architecture on single-core platform. Several schemes target the problem for performance boost. As chip multiprocessors (CMPs) pick up steam as the mainstream processor design choice, how non-uniform distribution of memory accesses across cache sets affects the cache management of CMPs is becoming an open question. We address the question by presenting several cache management schemes on CMP platforms, aiming at balance the memory access distribution across cache sets on shared caches or private caches. We show that on CMP platforms with multi-programmed workloads: (a) for shared caches, the non-uniform memory access distribution across different cache sets is biased by the fact that multiple applications are running concurrently and sharing the cache capacity. The scheme, which we put forward to make use of the non-uniformity to improve performance on shared caches, is proved to be of little to no benefit or even lead to degradation, (b) for caches that are organized as private caches, direct adaption of a scheme that targets this kind of non-uniformity outperforms the baseline private cache design by 2% on average, (c) however, for a private cache based cache management scheme we proposed, further effort to take advantage of this kind of non-uniformity for performance boost (on top of our proposed scheme) is also proved to be of little to no benefit. Therefore, We draw to the conclusion that on CMP platforms with multiprogrammed workloads, the non-uniform distribution of memory accesses across cache sets is partially circumvented by the interactions between multiple applications. Efforts seeking to make use of the non-uniformity to derive more benefit may end up in vain in CMPs.
机译:跨缓存集的内存访问的不均匀分布已被认为是单核平台上缓存体系结构效率低下的原因之一。有几种方案针对提高性能的问题。随着芯片多处理器(CMP)成为主流处理器设计的选择,跨缓存集的内存访问的不均匀分布如何影响CMP的缓存管理已成为一个悬而未决的问题。我们通过在CMP平台上提出几种缓存管理方案来解决这个问题,旨在平衡共享缓存或私有缓存上各个缓存集之间的内存访问分布。我们显示了在具有多程序工作负载的CMP平台上:(a)对于共享缓存,多个应用程序同时运行并共享缓存容量的事实使不同缓存集之间的内存访问分布不均匀。我们提出了利用非均匀性来提高共享缓存性能的方案,事实证明这种方案几乎没有好处,甚至不会导致性能下降。(b)对于组织为私有缓存的直接缓存针对此类不均匀性的方案的适应性能平均比基准专用缓存设计高出2%,(c)但是,对于我们提出的基于专用缓存的缓存管理方案,需要进一步努力以利用此类非一致性(在我们提出的方案之上)提高性能的均匀性也几乎没有好处。因此,我们得出的结论是,在具有多程序工作负载的CMP平台上,跨多个缓存集的内存访问的不均匀分布在一定程度上被多个应用程序之间的交互作用所规避。试图利用不均匀性来获得更多利益的努力可能最终在CMP中失败。

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