期刊
IEEE 27TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2013)
卷 -, 期 -, 页码 1195-1204出版社
IEEE COMPUTER SOC
DOI: 10.1109/IPDPS.2013.70
关键词
phylogenetics; likelihood; parallelization; MPI
The molecular data avalanche generated by novel wet-lab sequencing technologies allows for reconstructing phylogenies (evolutionary trees) using hundreds of complete genomes as input data. Therefore, scalable codes are required to infer trees on these data under likelihood-based models of molecular evolution. We recently introduced a checkpointable and scalable MPI-based code for this purpose called RAxML-Light and are currently using it for several real-world data analysis projects. It turned out that the scalability of RAxML-Light is nonetheless still limited because of the fork-join parallelization approach that is deployed. To this end, we introduce a novel, generally applicable, approach to computing the phylogenetic likelihood in parallel on whole-genome datasets and implement it in ExaML (Exascale Maximum Likelihood). ExaML executes up to 3.2 times faster than RAxML-Light because of the more efficient parallelization and communication scheme, while implementing exactly the same tree search algorithm. Moreover, the new parallelization approach exhibits lower code complexity and a more appropriate structure for implementing fault tolerance with respect to hardware failures.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据