A Fast Parallel Selection Algorithm on GPUs

Bakunas-Milanowski, Darius; Rego, Vernon; Sang, Janche; Yu, Chansu

doi:10.1109/csci.2015.132

Cited by 7 publications

(1 citation statement)

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“…The general compact algorithm is outlined in Figure 2C). GO Fish's version uses a more advanced variant to speed up compaction and lower its memory requirement as discussed in [18] and adapted from [63].…”

Section: Compactmentioning

confidence: 99%

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Lawrie¹

2016

Preprint

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Forward Wright-Fisher simulations are powerful in their ability to model complex demography and selection scenarios, but suffer from slow execution on the CPU, thus limiting their usefulness. The single-locus Wright-Fisher forward algorithm is, however, exceedingly parallelizable, with many steps which are so-called embarrassingly parallel, consisting of a vast number of individual computations that are all independent of each other and thus capable of being performed concurrently. The rise of modern Graphics Processing Units (GPUs) and programming languages designed to leverage the inherent parallel nature of these processors have allowed researchers to dramatically speed up many programs that have such high arithmetic intensity and intrinsic concurrency. The presented GPU Optimized Wright-Fisher simulation, or GO Fish for short, can be used to simulate arbitrary selection and demographic scenarios while running over 250-fold faster than its serial counterpart on the CPU. Even modest GPU hardware can achieve an impressive speedup of well over two orders of magnitude. With simulations so accelerated, one can not only do quick parametric bootstrapping of previously estimated parameters, but also use simulated results to calculate the likelihoods and summary statistics of demographic and selection models against real polymorphism dataall without restricting the demographic and selection scenarios that can be modeled or requiring approximations to the single-locus forward algorithm for efficiency. Further, as many of the parallel programming techniques used in this simulation can be applied to other computationally intensive algorithms important in population genetics, GO Fish serves as an exciting template for future research into accelerating computation in evolution. GO Fish is part of the Parallel PopGen Package available at: http://dl42.github.io/ParallelPopGen/

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Section: Compactmentioning

confidence: 99%