A static performance estimator to guide data partitioning decisions
PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and practice of parallel programming
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Efficient Algorithms for Data Distribution on Distributed Memory Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Parallel Incremental Graph Partitioning
IEEE Transactions on Parallel and Distributed Systems
A stochastic disk I/O simulation technique
Proceedings of the 29th conference on Winter simulation
An analytical model of the HINT performance metric
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
HINT: A new way to measure computer performance
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Legion-a view from 50,000 feet
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
(R) MpPVM: A Software System for Non-Dedicated Heterogeneous Computing
ICPP '96 Proceedings of the Proceedings of the 1996 International Conference on Parallel Processing - Volume 3
Network numerical analysis for the smoother and the lagged joint-process estimator
The Journal of Supercomputing
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The last five years have been a period of exponential growth in the number of machines connected to the Internet and the speed at which these machines communicate. The infrastructure is now in place to consider a nationwide cluster of workstations as a viable parallel processing platform. In order to achieve acceptable performance on this kind of a machine, performance prediction tools must provide information on where to place computational objects. Incorrect object placement can result in poor performance and congestion in the network. This research develops a new paradigm for predicting performance in the Wide Area Network (WAN) based cluster arena. Statistical samples of the performance of clusters and applications are used to build characteristic surfaces. These surfaces are then used to provide guidance in placement of new applications. This prediction method is intended to minimize both the execution time of the application and the impact of the application on the nationwide virtual machine. Performance prediction tools are an important prerequisite to effectively utilizing WAN based clusters.