Locality-Aware GPU Register File

Abstract

In many emerging applications such as deep learning, large data set is essential to generate reliable solutions. In these big data workloads, memory latency and bandwidth are the main performance bottlenecks. In this article, we propose a locality-aware GPU register file that enables data sharing for memory-intensive big data workloads on GPUs without relying on small on-chip memories. We exploit two types of data sharing patterns commonly found from the big data workloads and have warps opportunistically share data in physical registers instead of issuing memory loads separately and storing the same data redundantly in their registers as well as small shared memory. With an extended register file mapping mechanism, our proposed design enables warps to share data by simply mapping to the same physical registers or reconstructing from the data in the register file already. The proposed sharing not only reduces the memory transactions but also further decreases the register file usage. The spared registers make rooms for applying orthogonal optimizations for energy and performance improvement. Our evaluation on two deep learning workloads and matrixMul show that the proposed locality-aware GPU register file achieves over 2× speedup and saves register space up to 57 percent.