SHF: Medium: Hardware/Software Partitioning for Hybrid Shared Memory Multiprocessors

SHF：中：混合共享内存多处理器的硬件/软件分区

• 批准号: 0905509
• 负责人: Laxmi Bhuyan
• 金额: $ 80万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905509&HistoricalAwards=false
• 关键词: SHF; Medium; Hardware; Software; Partitioning

Hybrid multiprocessor architectures present an unprecedented opportunity for high performance computing through the seamless integration of large number of processors and hardware accelerators. This project addresses the research challenges in the design and exploitation of hybrid multiprocessors through innovations that span across the areas of architectures, compilers, and high-performance computing. A hybrid cache coherent non-uniform memory access (CC-NUMA) architecture is designed that clusters CPUs, hardware accelerators, and memories to preserve locality and reduce memory latency. Partitioning models are developed to enable optimal partitioning of data among CPUs and hardware accelerators. Compiler techniques are developed for detection of parallelism, its partitioning, and assignment across CPUs and hardware accelerators. The project enables coexistence of data streaming (push data) and data fetching (pull data) mechanisms. The research benefits from detailed measurements using a 64-processor SGI Altix 4700 CC-NUMA machine with FPGAs, the Intel FSB-FPGA architecture accelerator, and Niveus 4000 workstation with NVIDIA GPUs.The research has impact on large-scale scientific computing. The hybrid multiprocessor technology is likely to be transferred to industry while the developed software (compilers, simulators and Hybrid SPLASH-2 benchmarks) will be distributed to researchers. The project also has impact on education and research. The SGI Altix machine is already being used in our graduate classes and further projects on hybrid parallel computing are introduced in architecture, parallel processing, and compiler classes. The project contributes to minority undergraduate education in Computer Science since UCR is recognized for its large undergraduate Hispanic population.

混合多处理器架构通过大量处理器和硬件加速器的无缝集成为高性能计算提供了前所未有的机会。该项目通过跨越体系结构，编译器和高性能计算领域的创新，解决了混合多处理器设计和开发中的研究挑战。 设计了一种混合高速缓存一致性非均匀存储器访问（CC-NUMA）架构，该架构将CPU、硬件加速器和存储器群集以保持局部性并减少存储器延迟。开发分区模型以实现CPU和硬件加速器之间的数据的最佳分区。开发了用于检测并行性、其分区以及跨CPU和硬件加速器的分配的并行技术。该项目实现了数据流（推送数据）和数据获取（拉取数据）机制的共存。该研究利用64处理器SGI Altix 4700 CC-NUMA机器与FPGA、Intel FSB-FPGA架构加速器和Niveus 4000工作站与NVIDIA GPU进行了详细的测量。混合多处理器技术很可能被转移到工业，而开发的软件（编译器，模拟器和混合SPLASH-2基准）将分发给研究人员。该项目还对教育和研究产生了影响。SGI Altix机器已经在我们的研究生课程中使用，并在体系结构，并行处理和编译器课程中引入了混合并行计算的进一步项目。该项目有助于少数民族本科教育在计算机科学，因为加州大学河滨分校是公认的大本科西班牙裔人口。