Cufinufft: a Load-Balanced GPU Library for General-Purpose Nonuniform FFTs
2021 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW)(2021)
摘要
Nonuniform fast Fourier transforms dominate the computational cost in many applications including image reconstruction and signal processing. We thus present a general-purpose GPU-based CUDA library for type 1 (nonuniform to uniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3, in single or double precision. It achieves high performance for a given user-requested accuracy, regardless of the distribution of nonuniform points, via cache-aware point reordering, and load-balanced blocked spreading in shared memory. At low accuracies, this gives on-GPU throughputs around 109 nonuniform points per second, and (even including hostdevice transfer) is typically 4-10x faster than the latest parallel CPU code FINUFFT (at 28 threads). It is competitive with two established GPU codes, being up to 90x faster at high accuracy and/or type 1 clustered point distributions. Finally we demonstrate a 5-12x speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10(-12) accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.
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关键词
Nonuniform FFT,GPU,load balancing
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