Using dual approximation algorithms for scheduling problems theoretical and practical results
Journal of the ACM (JACM)
Partitioning Problems in Parallel, Pipeline, and Distributed Computing
IEEE Transactions on Computers
An Ada multitasking solution for the sieve of Eratosthenes
ACM SIGAda Ada Letters
Impact of self-scheduling order on performance on multiprocessor systems
ICS '88 Proceedings of the 2nd international conference on Supercomputing
Predicting the speedup of parallel Ada programs
Proceedings of the 11th Ada-Europe international conference on Ada: moving towards 2000
An Optimal Execution Time Estimate of Static versus Dynamic Allocation in Multiprocessor Systems
SIAM Journal on Computing
Processor scheduling with improved heuristic algorithms
Processor scheduling with improved heuristic algorithms
Using Recorded Values for Bounding the Minimum Completion Time in Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Bounding the gain of changing the number of memory modules in shared memory multiprocessors
Nordic Journal of Computing
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We first consider an MIMD multiprocessor configuration with n processors. A parallel program, consisting of n processes, is executed on this system—one process per processor. The program terminates when all processes are completed. Due to synchronizations, processes may be blocked waiting for events in other processes. Associated with the program is a parallel profile vector v¯, index i (1≤i≤n) in this vector indicates the percentage of the total execution time when i processes are executing.We then consider a distributed MIMD supercomputer with k clusters, containing u processors each. The same parallel program, consisting of n processes, is executed on this system. Each process can only be executed by processors in the same cluster. Finding a schedule with minimal completion time in this case is NP-hard.We are interested in the gain of using n processors compared to using k clusters containing u processors each. The gain is defined by the ratio between the minimal completion time using processor clusters and the completion time using a schedule with one process per processor. We present the optimal upper bound for this ratio in the form of an analytical expression in n, v¯, k and u. We also demonstrate how this result can be used when evaluating heuristic scheduling algorithms.