Cost analysis of logic programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Coordinated allocation of memory and processors in multiprocessors
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Lower bound cost estimation for logic programs
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
ACM Transactions on Programming Languages and Systems (TOPLAS)
Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues
IEEE Transactions on Parallel and Distributed Systems
A scheduling philosophy for multiprocessing systems
Communications of the ACM
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing
IEEE Transactions on Computers
Scheduling in Multiprocessor System Using Genetic Algorithms
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
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Distributed and multiprocessor computing is a a basic need for increasingly complex computational requirements. Scheduling the tasks efficiently is as important in multiprocessor systems as getting the correct results. In this paper, we present a heuristic algorithm for optimized parallelization of tasks in multiprocessor environments. This heuristic can be applied in such multiprocessor environments where task and resource information is completely know at time of scheduling or incomplete task information is known at scheduling time. For the case of incomplete task information, we have given a method of using static cost analysis to get different sets of parallel tasks and one set among these alternatives is chosen in runtime based on the value of input parameter. We have verified our approach using a prolog (CLP) program for random sets of tasks and resource with 100% positive results. Our heuristic algorithm is quite simple yet effective which makes it diiferent than already existing scheduling algorithms, heuristics and genetic algorithms.