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

University Of Southern California, Los Angeles CA

Investigators

Abstract

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.