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Efficient implementation of MrBayes on multi-GPU.

Bao J, Xia H, Zhou J, Liu X, Wang G - Mol. Biol. Evol. (2013)

Bottom Line: This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture.By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets.Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

View Article: PubMed Central - PubMed

Affiliation: College of Information Technical Science, Nankai University, Tianjin, China.

ABSTRACT
MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new "node-by-node" task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

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Related in: MedlinePlus

Execution time of the serial version of MrBayes 3.1.2 and a(MC)3. The horizontal axis represents the DNA length of 10 taxa, and the vertical axis is the logarithm of execution time.
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mst043-F1: Execution time of the serial version of MrBayes 3.1.2 and a(MC)3. The horizontal axis represents the DNA length of 10 taxa, and the vertical axis is the logarithm of execution time.

Mentions: We compare the average execution time of the “fastest known” serial algorithm MrBayes 3.1.2 and a(MC)3 with one GPU card, using 10 artificial data sets (fig. 1). The new algorithm improves calculation speed greatly, and the gap widens as the scale of data set increases.Fig. 1.


Efficient implementation of MrBayes on multi-GPU.

Bao J, Xia H, Zhou J, Liu X, Wang G - Mol. Biol. Evol. (2013)

Execution time of the serial version of MrBayes 3.1.2 and a(MC)3. The horizontal axis represents the DNA length of 10 taxa, and the vertical axis is the logarithm of execution time.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3649675&req=5

mst043-F1: Execution time of the serial version of MrBayes 3.1.2 and a(MC)3. The horizontal axis represents the DNA length of 10 taxa, and the vertical axis is the logarithm of execution time.
Mentions: We compare the average execution time of the “fastest known” serial algorithm MrBayes 3.1.2 and a(MC)3 with one GPU card, using 10 artificial data sets (fig. 1). The new algorithm improves calculation speed greatly, and the gap widens as the scale of data set increases.Fig. 1.

Bottom Line: This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture.By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets.Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

View Article: PubMed Central - PubMed

Affiliation: College of Information Technical Science, Nankai University, Tianjin, China.

ABSTRACT
MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new "node-by-node" task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

Show MeSH
Related in: MedlinePlus