Genomics GPUs and next generation computational statistics

描述（由申请人提供）：随着遗传学的计算需求呈指数级增长，人们越来越担心传统的cpu是否能够提供所需的计算能力。并行计算已经被吹捧了好几年，但大规模并行CPU计算机非常昂贵，而且仅限于少数国家中心。图形处理单元（gpu）提供了更便宜、更分布式的解决方案。数百个这样的单元被组装在一张卡片上，几张卡片可以装在一台台式电脑里。因此，目前存在的廉价硬件承诺将许多基本算法的速度提高一百倍。来自gpu供应商的预测表明，这些设备在计算能力和多功能性方面将在未来十年迅速增长。因此，软件开发是阻碍gpu开发的主要障碍。这个建议的目标是现代计算链中的这个薄弱环节。通过一系列的示范项目和低级软件库的生产，我们希望催化gpu在遗传学中的传播。具体项目包括：1)eQTL定位，2)QTL定位的方差成分模型，3)基因型和单倍型构建，4)种族混合估计，5)通过RNA-Seq技术发现同种异构体，6)遗传景观和遗传曲线计算，7)随机多图基因网络构建，8)设计新的数据挖掘并行算法。高维优化是支持所有这些应用程序的通用线程。我们之前的优化研究已经证明了四个基本思想的有效性，即惩罚估计、坐标下降、MM（最大化-最小化）原则和参数分离。这些想法也推动了并行计算的发展。在gpu上实现我们的示范项目将需要生成在计算统计学中具有相当普遍价值的子程序。我们打算向开源社区发布我们的工具箱库，包括C/ c++、Fortran和R软件包装器。这可能会导致乘数效应，从而改善整个健康和物理科学许多学科的计算环境。根据本提案制作的所有其他应用程序将免费分发给科学界。我们生产和发布具有优质文档的可用软件的记录显示了我们对这一哲学的承诺。

DESCRIPTION (provided by applicant): With computational demands in genetics growing exponentially, concerns are rising whether traditional CPUs can deliver the needed computing power. Parallel computing has been touted for several years, but massively parallel CPU computers are enormously expensive and limited to a few national centers. Graphics processing units (GPUs) offer a far cheaper and more distributed solution. Hundreds of these units are fabricated on a single card, and several cards fit inside a desktop computer. Thus, cheap hardware currently exists that promises a hundred-fold speedup of many basic algorithms. Projections from the vendors of GPUs suggest that these devices will grow rapidly in computational power and versatility over the next decade. Thus, software development is the main hurdle hindering the exploitation of GPUs. This proposal targets this weak link in the chain of modern computing. Through a series of demonstration projects and the production of low-level software libraries, we hope to catalyze the spread of GPUs in genetics. The specific projects include: 1) eQTL mapping, 2) variance component models for QTL mapping, 3) genotype and haplotype construction, 4) estimation of ethnic admixture, 5) isoform discovery through RNA-Seq technology, 6) computation of genetic landscapes and clines, 7) construction of gene networks from random multigraphs, and 8) design of new parallel algorithms for data mining. High-dimensional optimization is a common thread enabling all of these applications. Our previous research on optimization has demonstrated the efficacy of four fundamental ideas, namely, penalized estimation, coordinate descent, the MM (majorization-minimization) principle, and separation of parameters. These ideas also propel parallel computing. Implementation of our demonstration projects on GPUs will require the production of subroutines of considerable general value in computational statistics. We intend to release our toolbox libraries to the open source community, including C/C++, Fortran, and R software wrappers. This may lead to a multiplier effect that will improve the computing climate in many disciplines throughout the health and physical sciences. All other application programs produced under this proposal will be freely distributed to the scientific community. Our record of producing and distributing usable software with superior documentation shows our commitment to this philosophy.