Speaker: Xian Zhu, ECpE Graduate Student
Advisor: Joseph Zambreno
Title: Improving First Level Cache Efficiency for GPUs Using Dynamic Line Protection
Abstract: A modern Graphics Processing Unit (GPU) utilizes L1 Data (L1D) caches to reduce memory bandwidth requirements and latencies. However, the L1D cache can easily be overwhelmed by many memory requests from GPU function units, which can bottleneck GPU performance. It is shown that the performance of L1D caches is greatly reduced for many GPU applications as a large amount of L1D cache lines are replaced before they are re-referenced. By examining the cache access patterns of these applications, we observe L1D caches with low associativity have difficulty capturing data locality for GPU applications with diverse reuse patterns. These patterns result in frequent line replacements and low data re-usage. To improve the efficiency of L1D caches, we design a Dynamic Line Protection scheme (DLP) that can both preserve valuable cache lines and increase cache line utilization. DLP collects data reuse information from the L1D cache. This information is used to predict protection distances for each memory instruction at runtime, which helps maintain a balance between exploitation of data locality and over-protection of cache lines with long reuse distances. When all cache lines are protected in a set, redundant cache misses are bypassed to reduce the contention for the set. We implement the DLP scheme in a cycle-level GPU simulator and verify its effects on L1D caches and the overall GPU system. The evaluation shows that our proposed solution improves cache hits while reducing cache traffic for cache-insufficient applications, achieving up to 137% and an average of 43% IPC improvement over the baseline architecture.