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对多核计算架构的贝叶斯族遗传学分析:在RevBayes与MPI中实现和评估BEAGLE
Killian Smith1,2, Daniel Ayres3, René Neumaier1
1GeoBio-Center, Ludwig-Maximilians-Universität München, Richard-Wagner Straße 10, 80333 Munich, Germany.
Systematic biology
|January 29, 2024
概括
现在RevBayes集成了BEAGLE库,用于更快的基因分析,显著加快了CPU和GPU上的概率计算. 这种优化对于高效处理大型生物数据集和复杂的进化模型至关重要.
科学领域:
- 计算生物学 计算生物学
- 进化生物学 进化生物学
- 生物信息学是一种生物信息学.
背景情况:
- 遗传学分析在生物学中至关重要,通常采用计算密集的基于概率的方法.
- 大数据集和复杂的模型带来了重大的计算挑战,主要是由于概率计算速度缓慢.
研究的目的:
- 通过优化概率计算来加速RevBayes中的基因推理.
- 为了提高RevBayes的性能,使用多核CPU和GPU进行大规模的基因分析.
主要方法:
- 将高性能BEAGLE库集成到RevBayes中,以实现多线程CPU和GPU加速.
- 在RevBayes中使用消息传递接口 (MPI) 实现本地并行.
- 在各种硬件上使用DNA和氨基酸数据集评估性能,包括NVIDIA GPU.
主要成果:
- 在多核CPU上达到20倍的速度,在多GPU设置上达到90倍以上的速度.
- 证明了部分概率的低效调整可使运行时间增加4倍.
- 引入了一种用于存储部分概率在分支上的新方法,以两倍的内存成本提供1.7倍的加快速度.
结论:
- RevBayes+BEAGLE的实施为遗传学分析提供了显著的性能改进.
- 优化的概率计算和高效的内存管理对于遗传学中的计算效率至关重要.
- 这些进步使得对大型生物数据集进行更快,更可扩展的遗传学推断成为可能.
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