Exploiting Fine-Grained Parallelism in the Phylogenetic Likelihood Function with MPI, Pthreads, and OpenMP: A Performance Study

Authors:
Alexandros Stamatakis;Michael Ott
Affiliations:
The Exelixis Lab, Teaching and Research Unit Bioinformatics, Department of Computer Science, Ludwig-Maximilians-University Munich,;Department of Computer Science, Technische Universität München,
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

Citing 10
Cited 6

MPI versus MPI+OpenMP on IBM SP for the NAS benchmarks

Proceedings of the 2000 ACM/IEEE conference on Supercomputing
A comparison of three programming models for adaptive applications on the origin2000

Journal of Parallel and Distributed Computing
pIQPNNI: parallel reconstruction of large maximum likelihood phylogenies

Bioinformatics
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

Bioinformatics
Dynamic multigrain parallelization on the cell broadband engine

Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Large-scale maximum likelihood-based phylogenetic analysis on the IBM BlueGene/L

Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Large-scale phylogenetic analysis on current HPC architectures

Scientific Programming - Large-Scale Programming Tools and Environments
SPMD OpenMP versus MPI on a IBM SMP for 3 Kernels of the NAS Benchmarks

ISHPC '02 Proceedings of the 4th International Symposium on High Performance Computing
Initial experiences porting a bioinformatics application to a graphics processor

PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
RAxML-OMP: an efficient program for phylogenetic inference on SMPs

PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies

Accelerating large-scale DEVS-based simulation on the cell processor

SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Parallel training of artificial neural networks using multithreaded and multicore CPUs

ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Parallel multi-objective approaches for inferring phylogenies

EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems

Parallel Computing
Multicore acceleration of Discrete Event System Specification systems

Simulation
Fast fingerprint identification for large databases

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Emerging multi- and many-core computer architectures pose new challenges with respect to efficient exploitation of parallelism. In addition, it is currently not clear which might be the most appropriate parallel programming paradigm to exploit such architectures, both from the efficiency as well as software engineering point of view. Beyond that, the application of high performance computing techniques and the use of supercomputers will be essential to deal with the explosive accumulation of sequence data. We address these issues via a thorough performance study by example of RAxML, which is a widely used Bioinformatics application for large-scale phylogenetic inference under the Maximum Likelihood criterion. We provide an overview over the respective parallelization strategies with MPI, Pthreads, and OpenMP and assess performance for these approaches on a large variety of parallel architectures. Results indicate that there is no universally best-suited paradigm with respect to efficiency and portability of the ML function. Therefore, we suggest that the ML function should be parallelized with MPI and Pthreads based on software engineering criteria as well as to enforce data locality.