Development of Effective Tools for Cluster Computing and Applications to Problems in Materials Chemistry and Physics

在主要研究仪器(MRI)和化学研究仪器和设施(CRIF)计划以及实验和综合活动部(EIA)的支持下，爱荷华州立大学的马克·S·戈登教授将与IBM、Myricom和Packet Engines合作，设计和建造一台集群计算机。这台计算机的外观和性能在逻辑上类似于一个中等大小的MPP系统，但成本只有它的一小部分。成功开发机器的关键是最佳的节点间通信，PI将利用a)爱荷华州立大学可扩展计算实验室的专业知识以及理论化学和凝聚态物理小组；b)通过他与IBM的合作访问AIX源代码；以及c)通过与Myricom和分组引擎的交互来优化通信技术。戈登将专注于吞吐量的优化、延迟的减少、超越节点的实际应用的可扩展性、集群异构性、分布式文件系统、开发更有效的消息传递工具、系统管理和调度，以及材料科学中的应用。计算科学和工程将在解决我们未来十年面临的重大挑战问题方面发挥主导作用。这些重要问题包括设计具有理想性能的新材料和催化剂，阐明生物过程，寻找生命的起源，以及开发可行的环境修复方法。解决这些紧迫而复杂的问题不仅需要最先进的计算硬件，而且还需要开发必要的模型和算法，以最大限度地利用现代计算机。这项工作将导致集群环境的开发，该环境将支持需要可扩展高性能计算的重大挑战应用程序。

With support from the Major Research Instrumentation (MRI) and Chemistry Research Instrumentation and Facilities (CRIF) Programs, and the Division of Experimental and Integrative Activities (EIA), Prof. Mark S. Gordon of Iowa State University, in collaboration with IBM, Myricom and Packet Engines, will design and build a cluster computer. This computer will look and perform logically like a moderately sized MPP system, but at a fraction of the cost. Key to the development of a successful machine is optimal inter-node communications, and the PI will utilize a) the expertise of the Iowa State University Scalable Computing Lab together with the theoretical chemistry and condensed matter physics groups; b) access to AIX source code via his partnership with IBM; and c) optimization of communications technology via interactions with Myricom and Packet Engines. Gordon will focus on optimization of throughput, reduction of latency, scalability of real applications beyond 64 nodes, cluster heterogeneity, distributed file systems, development of more effective message passing tools, system management and scheduling, and applications in materials science. Computational science and engineering is positioned to play a leading role in solving grand challenge problems that we face in the next decade. These important problems include the design of new materials and catalysts with desirable properties, the elucidation of biological processes, the search for the origin of life, and the development of viable methods for environmental remediation. Solution of such compelling and complex problems requires not only state-of-the-art computational hardware, but also the development of the necessary models and algorithms to take optimal advantage of modern computers. This work will lead to the development of a cluster environment that will enable grand challenge applications that require scalable high-performance computing.