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Designing Efficient Sorting Algorithms for Manycore GPUs

posted Jan 15, 2009, 9:59 PM by Nicolas Pinto   [ updated Jan 15, 2009, 10:03 PM ]
N. Satish, M. Harris, and M. Garland. Designing efficient sorting algorithms for manycore GPUs. Proc. 23rd IEEE Int’l Parallel & Distributed Processing Symposium, May 2009. To appear.



We describe the design of high-performance parallel radix sort and merge sort routines for manycore GPUs, taking advantage of the full programmability offered by CUDA. Our radix sort is the fastest GPU sort and our merge sort is the fastest comparison-based sort reported in the literature. Our radix sort is up to 4 times faster than the graphics-based GPUSort and 2 times faster than other CUDA-based radix sorts. It is also highly competitive with even the most carefully optimized multi-core CPU sorting routines.

To achieve this performance, we carefully design our algorithms to expose substantial fine-grained parallelism and decompose the computation into independent tasks that perform minimal global communication. We exploit the high-speed on-chip shared memory provided by NVIDIA’s Tesla architecture and efficient data-parallel primitives, particularly parallel scan. While targeted at GPUs, these algorithms should also be well-suited for other manycore processors.