Efficient Implementation of MrBayes on multi-GPU

Jie Bao, Hongju Xia, Jianfu Zhou, Xiaoguang Liu, Gang Wang
College of Information Technical Science, Nankai University, Tianjin, China
College of Information Technical Science, Nankai University, 2013

   title={PDF Proof: Mol. Biol. Evol.},

   author={Xia, H. and Zhou, J. and Wang, G.},



Download Download (PDF)   View View   Source Source   Source codes Source codes




MrBayes, using Metropolis coupled Markov chain Monte Carlo [MCMCMC, or (MC)^3 for short], is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, now the (MC)^3 Bayesian algorithm and its improved and parallel versions are all not fast enough for Biologists to analyze massive real-world DNA data. Recently Graphics Processor Unit (GPU) has shown its power as a co-processor (or rather, an accelerator) in many fields. This paper describes an efficient implementation a(MC)^3 [aMCMCMC] for MrBayes (MC)^3 on Compute Unified Device Architecture (CUDA). 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 55x speedup over serial MrBayes on a single machine with one GPU card, and up to 154x speedup with four GPU cards, and up to 439x 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.
VN:F [1.9.22_1171]
Rating: 5.0/5 (3 votes cast)
Efficient Implementation of MrBayes on multi-GPU, 5.0 out of 5 based on 3 ratings

* * *

* * *

Follow us on Twitter

HGPU group

1658 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

335 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: