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

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。该项目通过跨架构、编译器和高性能计算领域的创新来解决混合多处理器设计和开发中的研究挑战。设计了一种混合缓存一致非统一内存访问（CC-NUMA）架构，该架构将cpu、硬件加速器和内存集群起来，以保持局域性并减少内存延迟。开发了分区模型，以便在cpu和硬件加速器之间实现数据的最佳分区。编译器技术的开发是为了检测并行性、其分区以及跨cpu和硬件加速器的分配。该项目支持数据流（推送数据）和数据获取（拉取数据）机制的共存。该研究得益于使用带有fpga的64处理器SGI Altix 4700 CC-NUMA机器、Intel FSB-FPGA架构加速器和带有NVIDIA gpu的Niveus 4000工作站进行的详细测量。该研究对大规模科学计算具有重要影响。混合多处理器技术可能会转移到工业，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，并且在架构、并行处理和编译器课程中介绍了更多关于混合并行计算的项目。该项目有助于少数族裔的计算机科学本科教育，因为UCR以其大量的西班牙裔本科生而闻名。

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.