WAP: The Warp Feature Aware Prefetching Method for LLC on CPU-GPU Heterogeneous Architecture

Recently, researchers discovered a GPU has some advantages for non-graphic computing. CPU-GPU heterogeneous architecture combines CPU and GPU to a chip and makes GPU easier to run non-graphic programs. Researchers also proposed LLC(last-level cache) to store and exchange data between CPU and GPU. We discover the LLC hit rate has great influence on memory access performance and system's performance. Therefore, we propose the WAP(warp feature aware prefetching method) for improving the LLC hit rate and memory access performance. We combine GPGPU-sim and GEM5 to a CPU-GPU heterogeneous many-core simulator, add an LLC in this simulator and choose 10 representative benchmarks. We compare this method with the MAP method. The experimental result illustrates the WAP improves 11.8% than the MAP on the LLC hit rate and 11.39% on IPC.