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Adaptive Granularity Memory Systems:A Tradeoff between Storage Efficiency and Throughput

机译:自适应粒度存储系统:存储效率和吞吐量之间的权衡

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We propose adaptive granularity to combine the best of finegrained and coarse-grained memory accesses. We augment virtual memory to allow each page to specify its preferred granularity of access based on spatial locality and error-tolerance tradeoffs. We use sector caches and sub-ranked memory systems to implement adaptive granularity. We also show how to incorporate adaptive granularity into memory access scheduling. We evaluate our architecture with and without ECC using memory intensive benchmarks from the SPEC, Olden, PARSEC, SPLASH2, and HPCS benchmark suites and micro-benchmarks. The evaluation shows that performance is improved by 61% without ECC and 44% with ECC in memory-intensive applications, while the reduction in memory power consumption (29% without ECC and 14% with ECC) and traffic (78% without ECC and 66% with ECC) is significant.
机译:我们提出自适应粒度,以结合最佳的细粒度和粗粒度内存访问。我们增加了虚拟内存,以允许每个页面基于空间局部性和容错权衡来指定其首选的访问粒度。我们使用扇区高速缓存和二级存储系统来实现自适应粒度。我们还将展示如何将自适应粒度合并到内存访问调度中。我们使用来自SPEC,Olden,PARSEC,SPLASH2和HPCS基准测试套件和微基准测试的内存密集型基准测试,评估有无ECC的架构。评估显示,在内存密集型应用中,不使用ECC的性能提高了61%,使用ECC的性能提高了44%,同时降低了内存功耗(不使用ECC时为29%,使用ECC时为14%)和流量(不使用ECC时为78%和66 %(使用ECC)的重要性。

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