Analyzing the Impact of Data Movement on GPU Computations

摘要

Recently, GPU computing has taken the scientific computing landscape by storm, fueled by the attractive nature of the massively parallel arithmetic hardware. When porting their code, researchers rely on a set of best practices that have been developed over the few years that general purpose GPU computing has been employed. This paper challenges a widely held belief that transfers to and from the GPU device must be minimized to achieve the best speedups over existing codes by presenting a case study on CULA, our library for dense linear algebra computation on GPU. Among the topics to be discussed include the relationship between computation and transfer time for both synchronous and asynchronous transfers, as well as the impact that data allocations have on memory performance and overall solution time.