Augmentation of Programs with CUDA Streams

A program that is run on a General Purpose Graphics Processing Unit (GPGPU) has to stall if the data is not resident in the GPGPU. With CUDA 2.0 architecture, data can be streamed while the computation is still on. Exploiting this feature requires careful orchestration of data transfer and computation which typically requires a significant effort from the programmer. We propose an approach of transforming C programs to programs that can make use of CUDA streams. We identify the regions where data transfer and computation can be overlapped by using a polyhedral framework called PLUTO[2]. We use the PLUTO framework to do automatic tiling of source code and use the streaming capabilities to overlap data transfer with computation. Our results show an average speedup of 1.5X over CUDA programs without streaming optimizations.