MrBayes tgMC3 ++: A High Performance and Resource-Efficient GPU-Oriented Phylogenetic Analysis Method
MrBayes tgMC3 ++: A High Performance and Resource-Efficient GPU-Oriented Phylogenetic Analysis Method
复制标题
MrBayes tgMC(3)：一种高性能且资源高效的面向GPU的系统发育分析方法
DOI：
10.1109/tcbb.2015.2495202
复制
发表时间：
2016-09-01
影响因子：
4.5
通讯作者：
Shi, Weifeng
中科院分区：
文献类型：
--
作者：
Ling, Cheng;Hamada, Tsuyoshi;Shi, Weifeng
贝叶斯先生是一种广泛使用的系统发育推断工具，它利用经验进化模型和贝叶斯统计。然而，似然估计的计算代价非常昂贵，导致执行时间过长。虽然已经提出了一些多线程优化来加速MR Bayes，但仍然存在严重限制似然估计的GPU线程级并行性的瓶颈。该研究提出了一种面向GPU的似然估计并行化的高性能和资源高效的方法。该分解存储模型不依赖于经验规划，而是隐式地实现了高性能的数据传输。在性能提升方面，在4块特斯拉K40卡的模拟数据集分析上，加速比最高可达178倍。与其他面向GPU的MRBayes方法相比，本文提出的tgMC(3)++方法比tgMC(3)(v1.0)、NMC(3)(v2.1.1)和OMC(3)(v1.00)方法的加速比分别高达1.6、1.9和2.9倍。此外，tgMC(3)++支持更多的进化模型和Gamma类别，这是以前的面向GPU的方法无法分析的。
MrBayes is a widespread phylogenetic inference tool harnessing empirical evolutionary models and Bayesian statistics. However, the computational cost on the likelihood estimation is very expensive, resulting in undesirably long execution time. Although a number of multi-threaded optimizations have been proposed to speed up MrBayes, there are bottlenecks that severely limit the GPU thread-level parallelism of likelihood estimations. This study proposes a high performance and resource-efficient method for GPU-oriented parallelization of likelihood estimations. Instead of having to rely on empirical programming, the proposed novel decomposition storage model implements high performance data transfers implicitly. In terms of performance improvement, a speedup factor of up to 178 can be achieved on the analysis of simulated datasets by four Tesla K40 cards. In comparison to the other publicly available GPU-oriented MrBayes, the tgMC(3)++ method ( proposed herein) outperforms the tgMC(3) (v1.0), nMC(3) (v2.1.1) and oMC(3) (v1.00) methods by speedup factors of up to 1.6, 1.9 and 2.9, respectively. Moreover, tgMC(3) ++ supports more evolutionary models and gamma categories, which previous GPU-oriented methods fail to take into analysis.