PVM: a framework for parallel distributed computing
Concurrency: Practice and Experience
Graphical development tools for network-based concurrent supercomputing
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
Hardware and software architectures for irregular problem architectures
Unstructured scientific computation on scalable multiprocessors
The interaction of parallel and sequential workloads on a network of workstations
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Effective distributed scheduling of parallel workloads
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Modeling the effects of contention on application performance in multi-user environments
Modeling the effects of contention on application performance in multi-user environments
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Flexible Communication Mechanisms for Dynamic Structured Applications
IRREGULAR '96 Proceedings of the Third International Workshop on Parallel Algorithms for Irregularly Structured Problems
Forecasting network performance to support dynamic scheduling using the network weather service
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
Predicting slowdown for networked workstations
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
Performance Prediction in Production Environments
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Would You Run it Here or There? AHS: Automatic Heterogeneous Supercomputing
ICPP '93 Proceedings of the 1993 International Conference on Parallel Processing - Volume 02
IPDPS '00/JSSPP '00 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
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Data-parallel applications executing in multi-user clustered environments share resources with other applications. Since this sharing of resources dramatically affects the performance of individual applications, it is critical to estimate its effect, i.e., the application slowdown, in order to predict application behavior. In this paper, we develop a new approach for predicting the slowdown imposed on data-parallel applications executing on homogeneous and heterogeneous clusters of workstations. Our model synthesizes the slowdown on each machine used by an application into a contention measure - the aggregate slowdown factor - used to adjust the execution time of the application to account for the aggregate load.The model is parameterized by the work (or data) partitioning policy employed by the targeted application, the local slowdown (due to contention from other users) present in each node of the cluster, and the relative weight (capacity) associated with each node in the cluster. This model provides a basis for predicting realistic execution times for distributed data-parallel applications in production clustered environments.