Genomics, GPUs, and Next Generation Computational Statistics

 描述(申请人提供)：随着基因数据集的大小及其计算需求呈指数级增长，人们越来越担心传统的统计方法和标准的CPU是否能够提供所需的分析和计算能力。并行计算已经被吹捧了几年，但大规模并行CPU计算机非常昂贵，而且仅限于少数几个国家中心。图形处理单元(GPU)和许多集成核心(MIC)协处理器提供了更便宜、更分布式的解决方案。每个GPU或MIC卡可以同时运行数百个计算线程，并且在一台台式计算机中可以运行几个卡？如今，几乎所有新的笔记本电脑和台式电脑都配备了多个CPU核心和一些GPU协处理器。因此，目前存在的廉价硬件承诺将许多基本计算程序的速度提高100倍。合适的算法设计和软件开发是阻碍GPU和MIC开发的主要障碍。这项提议针对的是现代计算链条中的这一薄弱环节。通过展示大规模并行处理在少数几个遗传问题上的优势，以及通过分发这些问题和许多其他问题的通用低级软件库，我们希望催化GPU和MIC在遗传学中的使用。具体项目包括：使用RNA-SEQ数据来发现和分析异构体、谱系信息的基因分型以及病原体的表型进化分析。高维优化是支持这些应用程序的常见线程。我们将寻求一种特别适用于高维和并行化的有前途的优化新技术，即最近距离算法。这一过程避免了当前技术水平方法的主要缺陷，特别是收缩，它扭曲了参数估计和模型选择。我们在图形处理器和MIC上的示范项目的实施将需要产生在计算统计中具有相当普遍价值的子例程。我们打算向开放源码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，从而通过健康和物理科学改善许多学科的计算环境。根据这项提议制作的所有其他应用程序将免费分发给科学界。我们生产和分发具有卓越文档的可用并行软件的记录表明了我们对这一理念的承诺。

 DESCRIPTION (provided by applicant): With the size of genetic data sets and their computational demands growing exponentially, concerns are rising whether traditional statistical approaches and standard CPUs can deliver the needed analytical and 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 unit (GPU) and many integrated core (MIC) coprocessors offer a far cheaper and more distributed solution. Each GPU or MIC card can run hundreds of computational threads simultaneously, and several cards ¿t inside a desktop computer. Today, almost all new laptop and desktop computers are equipped with multiple CPU cores and some GPU coprocessor. Thus, cheap hardware currently exists that promises a hundred-fold speedup of many basic computational procedures. Appropriate algorithm design and software development is the main hurdle hindering the exploitation of GPUs and MICs. This proposal targets this weak link in the chain of modern computing. By demonstrating the advantages of massively parallel processing on a few genetic problems, and by distributing general low-level software libraries for these and many other problems, we hope to catalyze the use of GPUs and MICs in genetics. The specific projects include: use of RNA-seq data for the discovery and analysis of isoforms, pedigree-informed genotype imputation, and analysis of pathogens' phenotype evolution. High-dimensional optimization is a common thread enabling these applications. We will pursue a promising new technique for optimization that is particularly well adapted to high dimensions and parallelization, the proximal distance algorithms. This procedure avoids major pitfalls of current state of the art methods, especially shrinkage, which distorts parameter estimates and model selection. Implementation of our demonstration projects on GPUs and MICs 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 through- out 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 parallel software with superior documentation shows our commitment to this philosophy.