Development of a low-cost 64 bit cluster supercomputer for engineering and physics simulations

ABSTRACTProposal没有。主要研究人员：J. T. Hauser，犹他州立大学这项资助的重点是结合两种前沿技术，双处理器AMDOpteron系统和基于千兆以太网的平面邻居网络（fnn），开发一种独特设计的集群超级计算机。除了提供64位内存地址空间外，Opteron还使用了一种新的内存架构，可以避免通常与共享内存多处理器相关的内存性能下降。类似地，使用FNN互连模式可以实现单交换机延迟，尽管具有比低成本网络交换机具有端口更多的节点。fnn还提供了优化网络特性的可能性，以适应在提议的集群超级计算机上运行的应用程序的通信模式。该项目将为市面上可用的高性能集群网络解决方案（如Myrinet和Quadrics）提供低成本的替代方案。设计和优化这种网络的软件将免费提供，这样其他研究小组就可以用fnn设计和构建他们自己的集群。与其他可用的存储系统设计相比，所提出的系统还将为新设计的大型应用存储系统的性能提供有价值的见解。将进行的研究包括计算流体动力学、生态水力学、聚变等离子体、地球物理流体动力学和计算化学。为了满足日益增长的对高性能计算人才的需求，工程学院将针对高级本科生和初级研究生开设并行计算工程应用课程。这门课程以及一个为期一周的关于集群计算的研讨会也将向数学、物理、化学和计算机科学的学生开放。此外，拟议中的集群超级计算机将加强与附近国家实验室的关系，如爱达荷国家工程与环境实验室（INEEL）。USU的教师和研究生正在与INEEL的工程师一起开发非常计算密集型的CFD模型。这种合作所带来的能见度将进一步促进吸引更多学生和教师的目标。

ABSTRACTProposal No. CTS-0321170Principal Investigator: J. T. Hauser, Utah State UniversityThis grant focuses on combining two leading-edge technologies, dual-processor AMDOpteron systems and Flat Neighborhood Networks (FNNs) based on Gigabit Ethernet, to develop a uniquely designed cluster supercomputer. In addition to providing a 64-bit memory address space, the Opteron uses a new memory architecture that should avoid the memory performance degradation normally associated with shared-memory multiprocessors. Similarly, using a FNN interconnection pattern makes it possible to achieve single-switch latency despite having more nodes than a low-cost network switch has ports. FNNs also provide the possibility to optimize the networking characteristics toward the communication patterns of applications running on the proposed cluster supercomputer. This project will provide a low-cost alternative to commercially available high performance cluster networking solutions such as Myrinet and Quadrics. The software to design and optimize such a network will be made available for free, so that other research groups can design and build their own cluster with FNNs. The proposed system will also provide valuable insight in the performance of a newly designed memory system for large applications compared to other available memory system designs.The research to be conducted includes computational fluid dynamics, ecohydraulics, fusion plasmas, geophysical fluid dynamics and computational chemistry. To meet the rising demand for students skilled in high-performance computing, the College of Engineering will introduce a course in parallel computing for engineering applications targeted at senior level undergraduate and beginning graduate students. This course as well as a one-week workshop on cluster computing will also be open to students in mathematics, physics, chemistry and computer science.In addition, the proposed cluster supercomputer will enhance current relationships with nearby national laboratories such as the Idaho National Engineering and Environmental Laboratory (INEEL). USU faculty and graduate students are developing very computationally intensive CFD models in conjunction with INEEL engineers. The visibility arising from this cooperative effort will further enhance the goal of attracting more students and faculty.