Parallel Metropolis Coupled Markov Chain Monte Carlo for Isolation with Migration Model

Zhou, C.; Lang, Xianyu; Wang, Yangang; Zhu, Chao‐Dong; Lu, Zhonghua; Chi, Xuebin

doi:10.12785/amis/071l30

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For good mixing and convergence, IM program and gPGA need increase number of Markov chains. In our previous work, we have applied IM program effectively on multiple CPU cores, even on cluster [ 36 ]. In future, we will apply gPGA to multiple GPUs on cluster for better speedup.…”

Section: Resultsmentioning

confidence: 99%

gPGA: GPU Accelerated Population Genetics Analyses

et al. 2015

Self Cite

View full text Add to dashboard Cite

BackgroundThe isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application.MethodologyWith strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA.ConclusionsCompared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.

show abstract

Section: Resultsmentioning

confidence: 99%

gPGA: GPU Accelerated Population Genetics Analyses

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

Increasing the degree of parallelism using speculative execution in task-based runtime systems

Bramas

2019

PeerJ Computer Science

View full text Add to dashboard Cite

Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and the assignment of the work to the computational units. However, some applications have a limited degree of parallelism such that no matter how efficient the RS implementation, they may not scale on modern multicore CPUs. In this paper, we propose using speculation to unleash the parallelism when it is uncertain if some tasks will modify data, and we formalize a new methodology to enable speculative execution in a graph of tasks. This description is partially implemented in our new C++ RS called SPETABARU, which is capable of executing tasks in advance if some others are not certain to modify the data. We study the behavior of our approach to compute Monte Carlo and replica exchange Monte Carlo simulations. Subjects Distributed and Parallel ComputingHow to cite this article Bramas B. 2019. Increasing the degree of parallelism using speculative execution in task-based runtime systems. PeerJ Comput. Sci. 5:e183 http://doi.org/10.7717/peerj-cs.183 Bramas (2019), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.183 2/25 Bramas (2019), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.183 3/25 ALGORITHM 2: Replica Exchange Monte Carlo (parallel tempering) simulation algorithm. 1 function REMC(domains[N], temperature[N]) 2 // Compute energy (particle to particle interactions) 3 for s from 1 to N do 4 energy[s] ← compute_energy(domains[s]) 5 end 6 // Iterate for a given number of iterations 7 for iter from 1 to NB_LOOPS_REMC do 8 for s from 1 to N do 9 // Compute usual MC for each simulation 10 MC_Core(domains[s], temperature[s], energy[s]) 11 end 12 // Compare based on a given strategy 13 for s in exchange_list(iter) do 14 // Use the energy difference between s and s+1 to decide to exchange them 15 if random_01() ≤ metropolis(energy[s] -energy[s+1], temperatures[s]) then 16 swap(domains[s], domains[s+1]) 17 swap(energy[s], energy[s+1]) 18 end 19 end 20 end Bramas (2019), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.183 6/25 Thachuk C, Shmygelska A, Hoos HH. 2007. A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinformatics 8(1):342 Nikolopoulos DS. 2018. A taxonomy of task-based parallel programming technologies for highperformance computing. The Journal of Supercomputing 74(4):1422-1434. Tillenius M. 2015. Superglue: a shared memory framework using data versioning for dependency-aware task-based parallelization. SIAM Journal on Scientific Computing 37(6):C617-C642 X. 2013. Parallel metropolis coupled Markov chain Monte Carlo for isolation with migration model. Applied Mathematics & Information Sciences 7(1L):219-224 DOI 10.12785/amis/071L30.

show abstract

Parallel Metropolis Coupled Markov Chain Monte Carlo for Isolation with Migration Model

Cited by 2 publications

References 17 publications

gPGA: GPU Accelerated Population Genetics Analyses

gPGA: GPU Accelerated Population Genetics Analyses

Increasing the degree of parallelism using speculative execution in task-based runtime systems

Contact Info

Product

Resources

About