Efficient data sharing on heterogeneous systems

Abstract

General-purpose computing on GPUs has become more accessible due to features such as shared virtual memory and demand paging. Unfortunately it comes at a price, and that is performance. Automatic memory management is convenient but suffers from many drawbacks, preventing heterogeneous systems from achieving their full potential. In this work we analyze the challenges and inefficiencies of demand paging in GPUs, in particular on collaborative computations where data migrates multiple times between host and device. We establish that demand paging on GPUs introduces significant overheads for these kind of computations, and identify the issues of false sharing and unnecessary data transfers derived from the granularity at which data is migrated. In order to alleviate these problems we propose a memory organization and dynamic migration scheme to efficiently share data between host and device at fine granularities and without software intervention. We evaluate our design with a set of collaborative heterogeneous benchmarks and find it achieves 15% lower execution times on average with cache line-sized migrations, but severely degrading performance on benchmarks that access large blocks of contiguous memory. Page-sized migrations, although inefficient, provide on average a 47% execution time reduction with our design over a baseline system implementing demand paging. Our results suggest that cache line-sized migrations are not feasible in systems using a PCI-Express interconnect. In order to understand how future interconnect technologies will impact the feasibility of fine-grained migrations, we evaluate our scheme with various link latencies. We find interconnect latencies four to five times lower than PCI-Express are sufficient to effectively share data at finer granularities.