Resource-efficient utilization of CPU/GPU-based heterogeneous supercomputers for Bayesian phylogenetic inference

Authors:
Jun Chai;Huayou Su;Mei Wen;Xing Cai;Nan Wu;Chunyuan Zhang
Affiliations:
School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;Simula Research Laboratory, Lysaker, Norway 1325 and Department of Informatics, University of Oslo, Oslo, Norway 0316;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073
Venue:
The Journal of Supercomputing
Year:
2013

Citing 8
Cited 0

Parallel algorithms for Bayesian phylogenetic inference

Journal of Parallel and Distributed Computing - High-performance computational biology
Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference

Bioinformatics
PBPI: a high performance implementation of Bayesian phylogenetic inference

Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Many-core algorithms for statistical phylogenetics

Bioinformatics
Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
MrBayes on a Graphics Processing Unit

Bioinformatics
Peta-scale phase-field simulation for dendritic solidification on the TSUBAME 2.0 supercomputer

Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
A new hybrid parallel algorithm for mrbayes

ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bayesian inference is one of the most important methods for estimating phylogenetic trees in bioinformatics. Due to the potentially huge computational requirements, several parallel algorithms of Bayesian inference have been implemented to run on CPU-based clusters, multicore CPUs, or small clusters of CPUs and GPUs. To the best of our knowledge, however, none of the existing methods is able to simultaneously and fully utilize both CPUs and GPUs for the computations, leaving idle either the CPU part or the GPU part of modern heterogeneous supercomputers. Aiming at an optimized utilization of heterogeneous computing resources, which is a promising hardware architecture for future bioinformatics applications, we present a new hybrid parallel algorithm and implementation of Bayesian phylogenetic inference, which combines MPI, OpenMP, and CUDA programming. The novelty of our algorithm, denoted as oMC3, is its ability of using CPU cores simultaneously with GPUs for the computations, while ensuring a fair work division between the two types of hardware components. We have implemented oMC3 based on MrBayes, which is one of the most popular software packages for Bayesian phylogenetic inference. Numerical experiments show that oMC3 obtains 2.5脳 speedup over nMC3, which is a cutting-edge GPU implementation of MrBayes, on a single server consisting of two GPUs and sixteen CPU cores. Moreover, oMC3 scales nicely when 128 GPUs and 1536 CPU cores are in use.