A REUSED DISTANCE BASED ANALYSIS AND OPTIMIZATION FOR GPU CACHE

As a throughput-oriented device, Graphics Processing Unit(GPU) has already integrated with cache, which is similar to CPU cores. However, the applications in GPGPU computing exhibit distinct memory access patterns. Normally, the cache, in GPU cores, suffers from threads contention and resources over-utilization, whereas few detailed works excavate the root of this phenomenon. In this work, we adequately analyze the memory accesses from twenty benchmarks based on reuse distance theory and quantify their patterns. Additionally, we discuss the optimization suggestions, and implement a Bypassing Aware(BA) Cache which could intellectually bypass the thrashing-prone candidates. BA cache is a cost efficient cache design with two extra bits in each line, they are flags to make the bypassing decision and find the victim cache line. Experimental results show that BA cache can improve the system performance around 20\% and reduce the cache miss rate around 11\% compared with traditional design