Interprocedural compilation of irregular applications for distributed memory machines
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
The utility of exploiting idle workstations for parallel computation
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic resource management on distributed systems using reconfigurable applications
IBM Journal of Research and Development - Special issue: performance analysis and its impact on design
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Adaptive data parallel computing on workstation clusters
Journal of Parallel and Distributed Computing
Adaptive time/space sharing with SCOJO
International Journal of High Performance Computing and Networking
A self-adaptive computing framework for parallel maximum likelihood evaluation
The Journal of Supercomputing
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For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at runtime. In this paper, we discuss runtime support for data parallel programming in such an adaptive environment. Executing data parallel-programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a runtime library to provide this support. We also present performance results for a multiblock Navier-Stokes solver run on a network of workstations using PVM for message passing. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computations.