GPU COMPUTING RESOURCE TO ENABLE INNOVATION IN IMAGING AND NETWORK BIOLOGY

产品说明：许多复杂的疾病，如癌症、心血管疾病和精神分裂症，可以被理解为生物分子和细胞网络的嵌套层次功能的失败。这些嵌套的层级控制着一系列过程，包括细胞的分化和迁移，细胞外基质和组织的重塑，以及神经元子系统中的信息编码。华盛顿大学在尖端成像、分子生物学和基因组技术方面建立了专业知识，这些技术与机器学习等计算方法协同作用，并揭示了复杂系统的分层组织和动力学原理。这种集体的专业知识正在被用来开发新药，提高我们解释复杂成像数据的能力，了解神经元群体如何集体行动以完成复杂任务，并将复杂疾病的发作和进展建模为分层，多尺度网络的动态重新布线。生物网络分析提供了一套丰富的工具，用于组织和解释由最先进的实验方案产生的大量数据。这些领域中计算密集型研究的快速发展正在超越基于CPU的高性能计算（HPC）系统的能力。这一应用程序将支持收购和整合IBM的图形处理器单元（GPU）的大规模高性能集群，作为对现有IBM设计的异构高性能计算环境的升级，以形成最先进的混合计算能力。这种资源对于满足华盛顿大学日益增长的高性能计算需求以及支持针对GPU计算进行优化的最先进研究软件应用程序至关重要。收购和集成高性能GPU集群将解决华盛顿大学不断增长的NIH研究组合中存在的关键计算挑战。拟议的最先进的GPU/CPU混合计算能力将部署在一个稳定、高效和快速增长的资源中心框架内。高容量GPU计算能力的增加将使关键计算在数小时内而不是数天内完成，并使覆盖广泛应用领域的现有项目的生产力大幅提高 为新的研究方向提供了可能。

DESCRIPTION: Many complex diseases such as cancer, cardiovascular disorders, and schizophrenia may be understood as failures in the functioning of nested hierarchies of biomolecular and cellular networks. These nested hierarchies control a range of processes including the differentiation and migration of cells, remodeling of extracellular matrices and tissues, and information encoding in neuronal subsystems. Washington University has established expertise in cutting edge imaging, molecular biology and genomic technologies synergistic with computational approaches such as machine learning and unraveling the principles of hierarchical organization and dynamics of complex systems. This collective expertise is being leveraged to develop new drugs, improve our ability to interpret sophisticated imaging data, understand how populations of neurons act collectively to accomplish complex tasks, and model the onset and progression of complex diseases as dynamical rewiring of hierarchical, multi-scale networks. Biological network analyses provide a rich set of tools for organizing and interpreting the vast quantities of data produced by state-of-the-art experimental protocols. The rapid advancement of computationally intensive research in these areas is outstripping the capabilities of CPU-based high performance computing (HPC) systems. This application would support the acquisition and integration of a large-scale IBM high performance cluster of Graphics Processor Units (GPUs) to be added as an upgrade to the existing IBM-designed Heterogeneous High Performance Computing environment to form a state-of-the-art hybrid computing capability. Such a resource is essential to match the growing need for high performance computing at Washington University and to support state of the art research software applications that are optimized for GPU computing. The acquisition and integration of a high performance GPU cluster will solve critical computing challenges that exist within Washington University's growing NIH research portfolio. The proposed state-of-the-art hybrid GPU/CPU computing capabilities will be deployed within the framework of a stable, productive and rapidly growing resource center. The addition of high-capacity GPU computing capabilities will allow critical calculation to be performed in hours instead of days and enable substantial increases in productivity for existing projects covering a broad range of application areas as well as enabling new research directions.