SGER: A Hybrid Approach for Petascale Computing: Accelerating Scientific

SGER：千万亿级计算的混合方法：加速科学发展

基本信息

批准号：
0849512
负责人：
David August
金额：
$ 10.26万
依托单位：
Princeton University
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：
2009-06-01 至 2010-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0849512&HistoricalAwards=false
关键词：
SGER Hybrid Approach Petascale Computing

项目摘要

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.

智力优点：拟议的工作是一项探索性研究工作，旨在自动从顺序程序中提取并行性，并在许多核心处理器上安排产生的细粒度计算元素。目的是允许现有的顺序程序有效地在多核处理器上运行，并建立基础，以实现混合方法，涉及消息传递和共享记忆，以解决Petascale的可编程性，这本探索性研究将攻击以下问题：设计一个编译器将单个节点上运行的代码分解为细粒度计算任务以利用单个芯片上的内核集合。开发一个高效的运行时系统，以安排细粒度的任务以优化可用的并行性，并最大化片上缓存区域以克服芯片外存储延迟和带宽约束。通过新发布的基准套件parsec评估我们的成功，这使我们能够将自己的成功与手工调整的并行解决方案进行比较。我们还计划评估一种计算科学应用程序的影响：该项目的潜在影响很大。首先，拟议的研究的成功将在并行编程中提高知识和理解，以利用未来平行机器的力量。其次，该项目的成功将加速软件开发，以佩斯卡尔计算。第三，拟议的编译器和运行时系统将提供本能性，以便在不繁重的编程工作的情况下，在Petascale计算机上运行现有的大规模计算科学计划。