ALGORITHMS: Hierarchical Computational-space Decomposition: A Framework for Scalable Scientific Computing Beyond Teraflop

该项目将为高端计算研究开发一个可扩展的并行计算框架，该框架将实现超越紧密耦合Teraflop架构的可扩展性，即在多台Teraflop计算机上进行分布式超级计算，以及未来具有深度内存层次的Petaflop计算机。为了实现这一目标，PI将进行以下研究任务：使用结构化消息传递方案进行拓扑保持计算空间分解以最小化消息数量；动态异构元计算环境中基于小波的自适应负载均衡，利用模拟退火最小化负载不平衡和消息大小递归和可重构的处理器分组，通过消息重整和计算/通信重叠来隐藏每个分组级别的延迟；基于空间填充曲线的自适应数据压缩——对可互操作的压缩数据进行现场处理，在用户控制的错误范围内减小消息大小。在新框架内开发的一套可扩展的科学程序将作为计算科学家的工具包通过门户网站传播，对高端计算研究产生重大影响，包括未来量子计算的量子点架构设计。

This project will develop a scalable parallel computing framework for high-end computational research, which will achieve scalability beyond tightly coupled Teraflop architectures, i.e., for distributed supercomputing on multiple Teraflop computers as well as on future Petaflop computers with deep memory hierarchy. To accomplish this goal, the PI will conduct the following research tasks: Topology-preserving computational-space decomposition to minimize the number of messages using a structured message-passing scheme; Wavelet-based adaptive load balancing in dynamic, heterogeneous metacomputing environment using simulated annealing to minimize load imbalance and message sizes; Recursive and reconfigurable grouping of processors with message renormalization and computation/communication overlapping to hide latency at each grouping level; Spacefilling-curve-based adaptive data compression --- in situ processing of interoperable compressed data to reduce message sizes with user-controlled error bound. A suite of scalable scientific programs developed within the new framework will be disseminated as a computational-scientist's toolkit through a Web portal to have significant impacts on high-end computational research, including the design of quantum-dot architectures for future quantum computing.