MRI: Acquisition of a Shared Memory High Performance Computer for Modeling and Data Analysis in the Mathematical and Physical Sciences

此MRI奖将用于购买256处理器/1536核心SGI Altix UV 1000，9.2 TB的RAM和14.4 TB的原始暂存磁盘空间。该仪器将为普林斯顿大学、高级研究所和合作机构的科学家提供模拟科学和工程中多尺度现象所需的计算资源。Altix架构的灵活性支持共享和分布式内存应用，沿着出色的总线架构支持添加额外的处理单元（如GPGPU），是开发多尺度问题算法的理想平台。在大学里设置仪器？的高性能计算研究中心将促进跨学科的方法，结合应用数学，计算机科学和特定领域的学科专业知识，为内存密集型应用提供创新方法。新仪器将在教育新一代科学家和培训学生在现代计算机平台上使用先进的建模工具方面发挥重要作用，为PACM，应用和计算数学计划以及PICSciE，普林斯顿计算科学与工程研究所提供的新研究生证书课程做出贡献。最后，该仪器将在地方和国家设施之间提供必要的联系，使普林斯顿科学界为未来新兴的多核和大规模并行架构做好准备。该仪器将通过促进慕尼黑-普林斯顿宇宙计算科学合作等项目，并将通过与纽约市的美国自然历史博物馆合作，为公众的科学教育做出贡献，计划在宇宙系列中使用新的可视化技术，并与博物馆合作？地震局正在进行地震和地质运动方面的公众教育工作。女人吗？妇女参与该工具所促成的计算项目将树立榜样，鼓励妇女更多地接触科学。最后，为加州州立大学北岭分校的合作伙伴提供的机会将有助于培训少数民族科学家和工程师。

This MRI award will serve to purchase a 256 Processor/1536 core SGI Altix UV1000 with 9.2 TB of RAM and 14.4 TB of raw scratch disk space. The instrument will provide scientists at Princeton University, the Institute for Advanced Studies, and partner institutions with the computational resources needed to model multi-scale phenomena in the sciences and engineering. The flexibility of the Altix architecture, which supports both shared and distributed memory applications, along with an outstanding bus architecture to support the addition of extra processing units such as GPGPUs is an ideal platform for developing algorithms for multi-scale problems. The setting of the instrument in the University?s High Performance Computing Research Center will facilitate a cross-disciplinary approach combining expertise in applied mathematics, computer science and domain-specific disciplines enabling innovative approaches for memory intensive applications. The new instrument will play an essential role in educating a new generation of scientists and training students across many disciplines in the use of advanced modeling tools on modern computer platforms, contributing to new graduate student certificate programs offered by PACM, the Program in Applied and Computational Mathematics, and PICSciE, the Princeton Institute for Computational Science and Engineering. Finally, the instrument will provide a necessary link between local and national facilities, preparing the Princeton scientific community to the emerging multicore and massively parallel architectures of the future.The instrument will enhance international scientific cooperation by contributing to projects like the Munich-Princeton collaboration in cosmological computational science, and will contribute to science education of the general public through collaboration with the American Museum of Natural History in New York City, with planned new visualizations for use in the Cosmos series and in conjunction with the Museum?s ongoing public education work on earthquakes and geologic movement. Women?s participation in computational projects enabled by the instrument will set examples to encourage greater access of women to science. Finally, access provided to partners atCalifornia State University-Northridge will contribute to training minority scientists and engineers.