Allocating Independent Subtasks on Parallel Processors
IEEE Transactions on Software Engineering
Communications of the ACM
Multiprocessor performance
Parallel discrete event simulation
Communications of the ACM - Special issue on simulation
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Design and performance analysis of hardware support for parallel simulations
Journal of Parallel and Distributed Computing - Special issue on parallel and discrete event simulation
Exploiting lookahead in synchronous parallel simulation
WSC '93 Proceedings of the 25th conference on Winter simulation
Performance of Synchronous Parallel Algorithms with Regular Structures
IEEE Transactions on Parallel and Distributed Systems
ICS '97 Proceedings of the 11th international conference on Supercomputing
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
The Journal of Supercomputing
Optimal Remapping in Dynamic Bulk Synchronous Computations via a Stochastic Control Approach
IEEE Transactions on Parallel and Distributed Systems
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Evaluation of Parallel Programs by Measurement of Its Granularity
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Optimal periodic remapping of dynamic bulk synchronous computations
Journal of Parallel and Distributed Computing
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
The Journal of Supercomputing
An analytical model for multilevel performance prediction of Multi-FPGA systems
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
| Hi-index | 0.00 |
Improved performance is a major motivation for using parallel computation. However, performance models are frequently used only to predict an algorithm's execution time, not to accurately evaluate how the choices of architecture, operating system, interprocessor communication protocol, and programming language also dramatically affect parallel performance. We have developed an analytic model for synchronous iterative algorithms running on distributed-memory MIMD machines, and refined it for disrete-event simulation. The model describes the execution time of a single run in terms of application parameters such as the number of iterations and the required computation in each, and architectural parameters such as the number of processors, processor speed, and communication time. Our experience has shown us that an analytic model can not only accurately predict an algorithm's performance but can also match the algorithm to an appropriate architecture, identify ways to improve the algorithm's performance, quantify the performance effects of algorithmic or architectural changes, and provide a better understanding of how the algorithm works.