SGER: A Hybrid Approach for Petascale Computing: Accelerating Scientific

智力价值：建议的工作是一项探索性的研究工作，旨在自动从顺序程序中提取并行性，并在多核处理器上调度产生的细粒度计算元素。目标是允许现有的顺序程序在多核处理器上高效运行，并为实现混合方法奠定基础，包括消息传递和共享内存，以解决千万亿级的可编程性这一探索性研究将解决以下问题：设计一个编译器，将运行在单个节点上的代码分解成细粒度的计算任务，以利用单芯片上的核心集合。开发一个高效的运行时系统来调度细粒度的任务，以优化可用的并行性，并最大限度地提高片上缓存的局部性，以克服片外内存延迟和带宽限制。使用最新发布的基准测试套件Parsec评估我们的成功，该套件允许我们将我们的成功与手动调整的并行解决方案进行比较。我们还计划评估一个计算科学应用程序布罗德影响：这个项目的潜在影响是巨大的。首先，拟议研究的成功将促进并行编程方面的知识和理解，以开发未来并行机的能力。其次，该项目的成功将加速千万亿级计算的软件开发。第三，拟议的编译器和运行时系统将提供在PB级计算机上运行现有大型计算科学程序的能力，而不需要繁琐的编程工作。

Intellectual Merit: The proposed work is an exploratory research effort to automatically extract parallelism from sequential program and to schedule the resulting fine-grained computational elements on manycore processors. The goal is to allow existing sequential programs to run on many-core processors efficiently and build the foundation to enable a hybrid approach, involving message passing andshared memory, to address petascale programmabilityThis exploratory research will attack the following issues:? Design a compiler to decompose the code running on a single node into fine-grained computation tasks to utilize the collection of cores on a single chip.? Develop a highly-efficient runtime system to schedule fine-grained tasks to optimize for available parallelisms and to maximize on-chip cache locality to overcome off-chip memory latency and bandwidth constrains.? Evaluate our success with a newly released benchmark suite PARSEC which allows us to compare our success with hand-tuned parallel solutions. We also plan to evaluate one computational science applicationBroader Impact:The potential impact of this project is significant. First, the success of the proposed research would advance knowledge and understanding in parallel programming to exploit the power of future parallel machines. Second, the success of the project will accelerate software developmentfor petascale computing. Third, the proposed compiler and runtime systems will provide thecapability to run existing large-scale computational science programs on petascale computers without burdensome programming efforts.