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|"Designing efficient sorting algorithms for manycore GPUs"
Nadathur Satish, Mark Harris, and Michael Garland, in, "NVIDIA Technical Report NVR-2008-001", September 2008
|Author(s):||Nadathur Satish, Mark Harris, and Michael Garland|
|Abstract:||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 reported in the literature, and is up to 4 times faster than the graphics-based GPUSort. It is also highly competitive with CPU implementations, being up to 3.5 times faster than comparable routines on an 8-core 2.33 GHz Intel Core2 Xeon system. Our merge sort is the fastest published comparison-based GPU sort and is also competitive with multi-core 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 highspeed 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.