DRAxML@home: a distributed program for computation of large phylogenetic trees (original) (raw)
Related papers
RAxML-II: a program for sequential, parallel and distributed inference of large phylogenetic trees
Concurrency and Computation: Practice and Experience, 2005
Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally intensive. We present simple heuristics which yield accurate trees for synthetic as well as real data and significantly reduce execution time. Those heuristics have been implemented in a sequential, parallel, and distributed program called RAxML-II, which is freely available as open source code. We compare the performance of the sequential program with PHYML and MrBayes which-to the best of our knowledge-are currently the fastest and most accurate programs for phylogenetic tree inference based on statistical methods. Experiments are conducted using 50 synthetic 100 taxon alignments as well as nine real-world alignments comprising 101 up to 1000 sequences. RAxML-II outperforms MrBayes for real-world data both in terms of speed and final likelihood values. Furthermore, for real data RAxML-II requires less time (a factor of 2-8) than PHYML to reach PHYML's final likelihood values and yields better final trees due to its more exhaustive search strategy. For synthetic data MrBayes is slightly more accurate than RAxML-II and PHYML but significantly slower. The non-deterministic parallel program shows good speedup values and has been used to infer a 10 000-taxon tree comprising organisms from the domains Eukarya, Bacteria, and Archaea.
Building large phylogenetic trees on coarse-grained parallel machines
Algorithmica, 2006
Phylogenetic analysis is an area of computational biology concerned with the reconstruction of evolutionary relationships between organisms, genes, and gene families. Maximum likelihood evaluation has proven to be one of the most reliable methods for constructing phylogenetic trees. The huge computational requirements associated with maximum likelihood analysis means that it is not feasible to produce large phylogenetic trees using a single processor. We have completed a fully cross platform coarse grained distributed application, DPRml, which overcomes many of the limitations imposed by the current set of parallel phylogenetic programs. We have completed a set of efficiency tests that show how to maximise efficiency while using the program to build large phylogenetic trees. The software is publicly available under the terms of the GNU general public licence from the system webpage at
Parallel computation of phylogenetic consensus trees
2010
The field of bioinformatics is witnessing a rapid and overwhelming accumulation of molecular sequence data, predominantly driven by novel wet-lab sequencing techniques. This trend poses scalability challenges for tool developers. In the field of phylogenetic inference (reconstruction of evolutionary trees from molecular sequence data), scalability is becoming an increasingly important issue for operations other than the tree reconstruction itself. In this paper we focus on a post-analysis task in reconstructing very large trees, specifically the step of building (extended) majority rules consensus trees from a collection of equally plausible trees or a collection of bootstrap replicate trees. To this end, we present sequential optimizations that establish our implementation as the current fastest exact implementation in phylogenetics, and our novel parallelized routines are the first of their kind. Our sequential optimizations achieve a performance improvement of factor 50 compared to the previous version of our code and we achieve a maximum speedup of 5.5 on a 8-core Nehalem node for building consensi on trees comprising up to 55,000 organisms. The methods developed here are integrated into the widely used open-source tool RAxML for phylogenetic tree reconstruction.
Parallel Phylogenetic Inference
ACM/IEEE SC 2000 Conference (SC'00), 2000
Recent advances in DNA sequencing technology have created large data sets upon which phylogenetic inference can be performed. However, current research is limited by the prohibitive time necessary to perform tree search on even a reasonably sized data set. Some parallel algorithms have been developed but the biological research community does not use them because they don't trust the results from newly developed parallel software. This paper presents a new phylogenetic algorithm that allows existing, trusted phylogenetic software packages to be executed in parallel using the DOGMA parallel processing system. The results presented here indicate that data sets that currently take as much as 11 months to search using current algorithms, can be searched in as little as 2 hours using as few as 8 processors. This reduction in the time necessary to complete a phylogenetic search allows new research questions to be explored in many of the biological sciences.
RAxML-Light: a tool for computing terabyte phylogenies
Bioinformatics, 2012
Motivation: Due to advances in molecular sequencing and the increasingly rapid collection of molecular data, the field of phyloinformatics is transforming into a computational science. Therefore, new tools are required that can be deployed in supercomputing environments and that scale to hundreds or thousands of cores. Results: We describe RAxML-Light, a tool for large-scale phylogenetic inference on supercomputers under maximum likelihood. It implements a light-weight checkpointing mechanism, deploys 128-bit (SSE3) and 256-bit (AVX) vector intrinsics, offers two orthogonal memory saving techniques and provides a fine-grain production-level message passing interface parallelization of the likelihood function. To demonstrate scalability and robustness of the code, we inferred a phylogeny on a simulated DNA alignment (1481 taxa, 20 000 000 bp) using 672 cores. This dataset requires one terabyte of RAM to compute the likelihood score on a single tree. Code Availability: https://github.com/stamatak/RAxML-Light-1.0.5 Data Availability: http://www.exelixis-
Systematic Biology, 2011
Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realistic or interesting models of evolution, these approaches remain computationally demanding. High-throughput sequencing can yield data for thousands of taxa, but scaling to such problems using serial computing often necessitates the use of nonstatistical or approximate approaches. The recent emergence of graphics processing units (GPUs) provides an opportunity to leverage their excellent floating-point computational performance to accelerate statistical phylogenetic inference. A specialized library for phylogenetic calculation would allow existing software packages to make more effective use of available computer hardware, including GPUs. Adoption of a common library would also make it easier for other emerging computing architectures, such as field programmable gate arrays, to be used in the future. We present BEAGLE, an application programming interface (API) and library for high-performance statistical phylogenetic inference. The API provides a uniform interface for performing phylogenetic likelihood calculations on a variety of compute hardware platforms. The library includes a set of efficient implementations and can currently exploit hardware including GPUs using NVIDIA CUDA, central processing units (CPUs) with Streaming SIMD Extensions and related processor supplementary instruction sets, and multicore CPUs via OpenMP. To demonstrate the advantages of a common API, we have incorporated the library into several popular phylogenetic software packages. The BEAGLE library is free open source software licensed under the Lesser GPL and available from http://beagle-lib.googlecode.com. An example client program is available as public domain software. [Bayesian phylogenetics; GPU; maximum likelihood; parallel computing.]
PBPI: a High Performance Implementation of Bayesian Phylogenetic Inference
ACM/IEEE SC 2006 Conference (SC'06), 2006
This paper describes the implementation and performance of PBPI, a parallel implementation of Bayesian phylogenetic inference method for DNA sequence data. By combining the Markov Chain Monte Carlo (MCMC) method with likelihood-based assessment of phylogenies, Bayesian phylogenetic inferences can incorporate complex statistic models into the process of phylogenetic tree estimation. However, Bayesian analyses are extremely computationally expensive. PBPI uses algorithmic improvements and parallel processing to achieve significant performance improvement over comparable Bayesian phylogenetic inference programs. We evaluated the performance and accuracy of PBPI using a simulated dataset on System X, a terascale supercomputer at Virginia Tech. Our results show that PBPI identifies equivalent tree estimates 1424 times faster on 256 processors than a widely-used, best-available (albeit sequential), Bayesian phylogenetic inference program. PBPI also achieves linear speedup with the number of processors for large problem sizes. Most importantly, the PBPI framework enables Bayesian phylogenetic analysis of large datasets previously impracticable.
Phylogenetic tree inference on PC architectures with AxML/PAxML
Proceedings International Parallel and Distributed Processing Symposium, 2000
Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. In previous work, we have introduced Subtree Equality Vectors (SEVs) to significantly reduce the number of required floating point operations during topology evaluation and implemented this method in (P)AxML, which is a derivative of (parallel) fastDNAml. Experimental results show that (P)AxML scales particularly well on inexpensive PCprocessor architectures obtaining global run time accelerations between 51% and 65% over (parallel) fastDNAml for large data sets, yet rendering exactly the same output. In this paper, we present an additional SEV-based algorithmic optimization which scales well on PC processors and leads to a further improvement of global execution times of 14% to 19% compared to the initial version of AxML. Furthermore, we present novel distance-based heuristics for reducing the number of analyzed tree topologies, which further accelerate the program by 4% up to 8%. Finally, we discuss a novel experimental tree-building algorithm and potential heuristic solutions for inferring large high quality trees, which for some initial tests rendered better trees and accelerated program execution at the same time by a factor greater than 6.