Scheduling Multiprocessor Tasks to Minimize Schedule Length
IEEE Transactions on Computers
Approximate algorithms scheduling parallelizable tasks
SPAA '92 Proceedings of the fourth annual ACM symposium on Parallel algorithms and architectures
Improved Approximation Algorithms for Shop Scheduling Problems
SIAM Journal on Computing
Scheduling parallelizable tasks to minimize average response time
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
Efficient approximation algorithms for scheduling malleable tasks
Proceedings of the eleventh annual ACM symposium on Parallel algorithms and architectures
Linear-time approximation schemes for scheduling malleable parallel tasks
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Scheduling Algorithms
Scheduling and Automatic Parallelization
Scheduling and Automatic Parallelization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Preemptable Malleable Task Scheduling Problem
IEEE Transactions on Computers
Scheduling parallel jobs to minimize the makespan
Journal of Scheduling
An approximation algorithm for scheduling malleable tasks under general precedence constraints
ACM Transactions on Algorithms (TALG)
A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms
ISPDC '07 Proceedings of the Sixth International Symposium on Parallel and Distributed Computing
Scheduling Parallel Task Graphs on (Almost) Homogeneous Multicluster Platforms
IEEE Transactions on Parallel and Distributed Systems
Improved results for scheduling batched parallel jobs by using a generalized analysis framework
Journal of Parallel and Distributed Computing
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In this article, we present different approximation algorithms for the scheduling of parallel modules. Each module can be executed on an arbitrary number of processors, and its execution time depends on the number of processors assigned to it. The scheduling algorithms assume that there is no dependence between the modules. In the first part of the article, we present algorithms that are based on results for the classical rectangle filling problem. Afterwards we modify an approximation algorithm for the shop scheduling problem. The resulting algorithms are simple, but efficient and guarantee tight worst-case bounds on the suboptimality of the solution. We test the quality of the generated schedules by applying the scheduling algorithm to randomly generated problem instances.