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. 2021 May 4;38(5):2177-2178.
doi: 10.1093/molbev/msaa305.

dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units

Ryan N Gutenkunst  1
Affiliations

dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units

Ryan N Gutenkunst. Mol Biol Evol. .

Abstract

dadi is a popular but computationally intensive program for inferring models of demographic history and natural selection from population genetic data. I show that running dadi on a Graphics Processing Unit can dramatically speed computation compared with the CPU implementation, with minimal user burden. Motivated by this speed increase, I also extended dadi to four- and five-population models. This functionality is available in dadi version 2.1.0, https://bitbucket.org/gutenkunstlab/dadi/.

Keywords: GPU computing; dadi; demographic history; population genetics.

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Figures

Fig. 1.
Fig. 1.
(A) Illustration of dadi integration. During each timestep, the population allele density ϕ is updated for each population axis. Each row and column over which ϕ is approximated yields a tridiagonal linear system. In the GPU implementation, these systems are solved in parallel. (B) Ratios of CPU to GPU times to compute the AFS for several models on several computing systems, versus AFS size. Absolute computation times are shown in supplementary figure S1, Supplementary Material online. The largest AFS size tested on each system was constrained by GPU memory.
See this image and copyright information in PMC

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