GPU shared L1 cache is a promising architecture while still suffering from high resource contentions. We present a GPU shared L1 cache architecture with an aggregated tag array that minimizes the L1 cache contentions and takes full advantage of inter-core locality. The key idea is to decouple and aggregate the tag arrays of multiple L1 caches so that the cache requests can be compared with all tag arrays in parallel to probe the replicated data in other caches. The GPU caches are only accessed by other GPU cores when replicated data exists, filtering out unnecessary cache accesses that cause high resource contentions. The experimental results show that GPU IPC can be improved by 12% on average for applications with a high inter-core locality.

翻译：GPU共享L1缓存是一种有前景的架构，但仍面临高资源争用的问题。我们提出了一种基于聚合标签数组的GPU共享L1缓存架构，该架构能最小化L1缓存争用，并充分利用核间局部性。其核心思想是将多个L1缓存的标签数组解耦并聚合，使得缓存请求能与所有标签数组并行比较，以探测其他缓存中是否存在重复数据。仅当存在重复数据时，GPU缓存才会被其他GPU核访问，从而过滤掉导致高资源争用的非必要缓存访问。实验结果表明，对于具有高核间局部性的应用，GPU的IPC平均提升12%。