II-New: A Cluster of Nodes with 32 Cores and 256-GB Memory to Enable Many-Core Systems Research and Education

众核计算系统的研究和教育对NSF CRI计划和研究界都很重要。该项目的目标是多核系统的性能，能耗和可扩展性，这对计算机行业很重要。该团队致力于将该项目的研究工件作为开源软件发布，供研究社区使用。 这个项目将有利于研究生的研究，并有助于本科生和研究生课程的教育活动。该项目将支持由普渡大学各中心赞助的推广活动，例如，通过参与普渡计算研究所的高性能计算研讨会，该基础设施将支持多个领域的研究和教育工作：计算机体系结构，编译器，高性能云计算和托管语言的运行时。计算机架构师将探索性能优化，可编程性和众核架构，片上网络和磁盘优化的权力。作者将探索共享内存优化及其可扩展性，目标是分布式内存机器的共享内存应用程序，以及将看似不规则的内存访问模式转换为常规和并行计算和内存访问的技术。运行时研究人员将研究大型垃圾收集堆的并行垃圾收集以及相关的可伸缩性问题。高性能计算研究人员将探索虚拟化和云计算对集群工作负载的性能开销，沿着降低开销的机制。

Research and education in many-core computing systems are of importance to the NSF CRI program as well as to the research community. This project targets performance, energy consumption, and scalability of many-core systems, which are important for the computer industry. The team is committed to releasing the research artifacts of the project as open-source software to be used by the research community as well. This project will benefit graduate student research and help educational activities in undergraduate and graduate curricula. The project will support outreach activities sponsored by various centers at Purdue University via the involvement of the team in Purdue Computing Research Institute's High Performance Computing workshops, for example.This infrastructure will support research and education efforts in multiple areas: computer architecture, compilers, high-performance cloud computing, and run-times for managed languages. Computer architects will explore optimizations for performance, programmability and power of many-core architectures, on-chip networks, and disk optimizations. Compiler writers will explore shared memory optimizations and their scalability targeting shared-memory applications for distributed memory machines, and techniques to transform seemingly irregular memory access patterns into regular and parallel computations and memory accesses. Run-time researchers will pursue parallel garbage collection of large garbage-collected heaps and associated scalability issues. High performance computing researchers will explore the performance overhead of virtualization and cloud computing for cluster workloads, along with mechanisms for reducing overhead.