Collaborative Research: A Study and Implementation of Semantic Constructs for Highly Scalable Leading-edge Scientific Computing

协作研究：高度可扩展的前沿科学计算的语义结构的研究和实现

• 批准号: 0833193
• 负责人: Thomas Sterling
• 金额: $ 7万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833193&HistoricalAwards=false
• 关键词: Collaborative; Research; Study; Implementation; Semantic

Scaling scientific problems to 10,000,000 processors for the next generation HEC systems is today severely challenged by conventional practices of programming models, languages, and their supporting compilation systems. To achieve this goal one must expose greater degree of parallelism and improve parallel computing efficiency than is otherwise feasible with conventional methods such as MPI. The goal of this collaborative research project is to dramatically enhance the scalability of challenging physics problems, through the application of an innovative programming model. The strategy is to replace static message-passing course grained processes using global barrier synchronization in a distributed memory space with a model using dynamic message-driven multiple threads using lightweight synchronization objects in a partitioned global address space. Parallelism is to be extracted directly from the large irregular sparse and time varying data structures. Ephemeral user-threads will permit many simultaneous tasks over the data structures, exposing the intrinsic near-fine grain parallelism. System-wide latency will be hidden by overlapping computation with communication through the advanced communication strategy of asynchronous message-driven processing. Consequently this will enable a class of physics problems that cannot currently be done using conventional methods.

将科学问题扩展到下一代HEC系统的10，000，000个处理器今天受到编程模型，语言及其支持编译系统的传统实践的严重挑战。为了实现这一目标，必须暴露更大程度的并行性和提高并行计算效率，否则与传统的方法，如MPI可行。这个合作研究项目的目标是通过应用创新的编程模型，大大提高具有挑战性的物理问题的可扩展性。该策略是取代静态的消息传递过程中使用全局屏障同步在分布式内存空间的模型，使用动态消息驱动的多线程在分区的全局地址空间中使用轻量级同步对象的粒度进程。直接从大型不规则稀疏和时变数据结构中提取特征。短暂的用户线程将允许在数据结构上同时执行许多任务，从而暴露出内在的近细粒度并行性。通过异步消息驱动处理的高级通信策略，将计算与通信重叠，从而隐藏系统范围的延迟。因此，这将使一类物理问题，目前无法使用传统的方法。