ReDirect

ACM Transactions on Architecture and Code Optimization(2017)

引用 0|浏览3
暂无评分
摘要
As we enter the dark silicon era, architects should not envision designs in which every transistor remains turned on permanently but rather ones in which portions of the chip are judiciously turned on/off depending on the characteristics of a workload. At the same time, due to the increasing cost per transistor, architects should also consider new ways to re-purpose transistors to increase their architectural value. In this work, we consider the design of directory-based cache coherence in light of the dark silicon era and the need to re-purpose transistors. We point out that directories are not needed all of the time, and we argue that directories (and coherence) should be off unless it is actually needed for correctness. In our design, directories will be disabled and powered off for workloads with no sharing. Then only when parallel workloads need cache coherence will directories be enabled in proportion to the sharing that is present. At the same time, we exploit the structural similarities of directories and cache. If a directory is idle, then we reconfigure it to be used as extra capacity in the last-level cache. Since our novel approach can keep most directories off, we are free to select sparse overprovisioned directory designs that are reconfigurable to large amounts of cache that can significantly boost performance when the directory is idle. We call these combined features Reconfigured Dark Directories , since directories are usually dark (off) and can be reconfigured. Our results for Reconfigurable Dark Directories running SPEC 2006 applications show a performance benefit, on average, of 17% for an 8× overprovisioned fully mapped directory on a 64-tile system under low system concurrency (10% under heavy concurrency), or a 29% average speedup for a 2× overprovisioned directory on 256-tile system (10% under heavy concurrency) to systems with a conventional sparse directory design using the same overprovisioning factor.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要