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

将科学问题扩展到下一代HEC系统的10,000,000个处理器，目前受到编程模型、语言及其支持编译系统的传统实践的严重挑战。要实现这一目标，必须提供更大程度的并行性，并提高并行计算效率，而不是使用MPI等传统方法。这个合作研究项目的目标是通过应用创新的编程模型，显著提高具有挑战性的物理问题的可扩展性。该策略是将使用分布式内存空间中的全局屏障同步的静态消息传递过程粒度流程替换为使用动态消息驱动的多线程的模型，该模型使用分区全局地址空间中的轻量级同步对象。并行性是直接从大型不规则、稀疏和时变的数据结构中提取。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乎细粒度的并行性。通过异步消息驱动处理的高级通信策略，通过与通信的重叠计算来隐藏系统级延迟。因此，这将使一类目前无法用传统方法解决的物理问题成为可能。

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.