II-New: A Platform for Data-Parallel GPU Computing at Princeton

这是一项机构基础设施建议，旨在建立一个GPU集群，以支持数据并行代码开发和优化方面的研究，以及三个科学领域的研究应用，即地震学、生物学和天体物理学。这些目标建立在与计算机科学的GPU计算专家团队的密切合作基础上。拟议的集群不仅将作为计算的宝贵资源，还将有助于不同学科之间技术和概念的交叉培养，并将用于刺激广泛领域的协作和协同研究活动。尽管领域科学家越来越依赖计算来实现他们的研究目标，但大多数人并不是并行编程或GPU架构方面的专家。GPU集群并行编程的困难是科学进步的一个障碍。为了减轻科学家并行编程的负担，普林斯顿大学的计算机科学家开发了自动并行化GPU程序的系统。在这一成功的基础上，PI计划将这些技术扩展到GPU集群，并与地震学家、生物学家和天体物理学家密切合作，以加快科学的步伐。

This is an Institutional Infrastructure proposal to build a GPU cluster to support research in data-parallel code development and optimization, as well as research applications, in three scientific domains, namely, seismology, biology and astrophysics. These goals build on a close collaboration with an expert team in GPU computing from computer science. The proposed cluster will serve not only as an invaluable resource for computation, but will also aid cross-fostering of techniques and concepts between disciplines and will be used to stimulate collaboration and synergistic research activity in a wide range of areas.Even though domain scientists are increasingly dependent on computation to achieve their research goals, most are not experts in parallel programming or GPU architectures. The difficulty of parallel programming for GPU clusters is an impediment to scientific progress. In order to relieve scientists of the burdens of parallel programming, computer scientists at Princeton have developed systems for automatically parallelizing programs for GPU. Building on this success, the PIs plan to extend these techniques to GPU clusters and work closely with the seismologists, biologists and astrophysicists to accelerate the pace of science.