Grid, Public and GPU Computing for the Tree of Life

马里兰大学学院公园获得了一笔赠款，用于创造工具，扩大所有ATOL(组装生命树)和其他系统发育研究人员免费可用的计算能力。中心思想是将网格和GPU(图形处理单元)计算相结合，以更好地利用各种计算资源，特别是通过公共计算可用的现有桌面处理能力。主要产品将是一个网格系统，为系统发育树搜索定制，并通过用户友好的网络界面访问，连接世界各地数千台单独的计算机。基于似然的树搜索程序GARLI(用于快速似然推理的遗传算法)将作为开发过程中的初始模型应用。新系统的目的是让分析远远超越在单一台式计算机或中等规模集群上所能做的事情。例如，执行广泛的引导分析的时间应该比现在单个树搜索所需的时间略多一点。该项目还将开发台式计算机内部另一种未得到充分利用的资源，即图形处理单元(GPU)，它可以比中央处理器(CPU)更快地完成许多科学计算。将开发一个免费的开放源码库(C/C++)，用于使用并行CPU/GPU处理进行最大似然计算。将开发利用主要GPU平台的Garli版本。随后的发展将扩展到其他可能性密集型方法(例如，贝叶斯分析)。五个参与的ATOL项目将帮助测试新工具，并发布一份关于新系统在它们所代表的各种系统进化问题上的性能的合作评估。该项目将与鲍伊州立大学、科平州立学院和堪萨斯大学研究中心共同努力。

The University of Maryland College Park is awarded a grant to create tools that expand the computing power freely available to all ATOL (Assembling the Tree of Life), and other phylogenetic researchers. The central idea is to combine Grid and GPU (graphics processing unit) computing to take better advantage of a diversity of computing resources, particularly existing desktop processing capacity available through public-computing. The main product will be a Grid system, customized for phylogenetic tree search and accessed through a user-friendly web interface, that links thousands of individual computers world-wide. The likelihood-based tree search program GARLI (Genetic Algorithm for Rapid Likelihood Inference), will serve as the initial model application during development. The new system is intended to allow analyses that far transcend what one can do on a single desktop computer or moderate-sized cluster. For example, it should become possible to carry out extensive bootstrap analyses in little more time than is now required for a single tree search. The project will also exploit a second under-utilized resource inside desktop computers, namely the graphics processing unit (GPU), which can do many scientific calculations faster than the central processing unit (CPU). A free, open-source library (C/C++) for maximum likelihood calculations using parallel CPU/GPU processing will be developed. GARLI versions will be developed that take advantage of major GPU platforms. Subsequent development will extend to other likelihood-intensive methods (e.g., Bayesian analysis). Five participating ATOL projects will help test the new tools, and publish a collaborative evaluation of the performance of the new system across the diverse phylogenetic problems they represent. The project will be a joint effort with Bowie State University, Coppin State College, and the University of Kansas Center for Research, Inc.