Supporting data-driven I/O on GPUs using GPUfs

Using discrete GPUs for processing very large datasets is challenging, in particular when an algorithm exhibit unpredictable, data-driven access patterns. In this paper we investigate the utility of GPUfs, a library that provides direct access to files from GPU programs, to implement such algorithms. We analyze the system's bottlenecks, and suggest several modifications to the GPUfs design, including new concurrent hash table for the buffer cache and a highly parallel memory allocator. We also show that by implementing the workload in a warp-centric manner we can improve the performance even further. We evaluate our changes by implementing a real image processing application which creates collages from a dataset of 10 Million images. The enhanced GPUfs design improves the application performance by 5.6× on average over the original GPUfs, and outperforms both 12-core parallel CPU which uses the AVX instruction set, and a standard CUDA-based GPU implementation by up to 2.5× and 3× respectively, while significantly enhancing system programmability and simplifying the application design and implementation.