Adaptive and transparent cache bypassing for GPUs

In the last decade, GPUs have emerged to be widely adopted for general-purpose applications. To capture on-chip locality for these applications, modern GPUs have integrated multilevel cache hierarchy, in an attempt to reduce the amount and latency of the massive and sometimes irregular memory accesses. However, inferior performance is frequently attained due to serious congestion in the caches results from the huge amount of concurrent threads. In this paper, we propose a novel compile-time framework for adaptive and transparent cache bypassing on GPUs. It uses a simple yet effective approach to control the bypass degree to match the size of applications' runtime footprints. We validate the design on seven GPU platforms that cover all existing GPU generations using 16 applications from widely used GPU benchmarks. Experiments show that our design can significantly mitigate the negative impact due to small cache sizes and improve the overall performance. We analyze the performance across different platforms and applications. We also propose some optimization guidelines on how to efficiently use the GPU caches.