Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
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SPAA '91 Proceedings of the third annual ACM symposium on Parallel algorithms and architectures
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LogP: a practical model of parallel computation
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Efficient approximation algorithms for scheduling malleable tasks
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Scheduling malleable and nonmalleable parallel tasks
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
List scheduling of general task graphs under LogP
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Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Scheduling malleable tasks with precedence constraints
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Scheduling mixed-parallel applications with advance reservations
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Scheduling mixed-parallel applications with advance reservations
Cluster Computing
A Bi-criteria Algorithm for Scheduling Parallel Task Graphs on Clusters
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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INFORMS Journal on Computing
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Scheduling malleable tasks with precedence constraints
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SIAM Journal on Computing
Scheduling and packing malleable tasks with precedence constraints of bounded width
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
Decision Support and Optimization in Shutdown and Turnaround Scheduling
INFORMS Journal on Computing
Scheduling and packing malleable and parallel tasks with precedence constraints of bounded width
Journal of Combinatorial Optimization
Combined scheduling and mapping for scalable computing with parallel tasks
Scientific Programming - Biological Knowledge Discovery and Data Mining
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In this article, we study the problem of scheduling malleable tasks with precedence constraints. We are given m identical processors and n tasks. For each task the processing time is a function of the number of processors allotted to it. In addition, the tasks must be processed according to the precedence constraints. The goal is to minimize the makespan (maximum completion time) of the resulting schedule. The best previous approximation algorithm (that works in two phases) in Lepère et al. [2002b] has a ratio 3 + &sqrt;5≈ 5.236. We develop an improved approximation algorithm with a ratio at most 100/43 + 100(&sqrt;4349 − 7)/2451 ≈ 4.730598. We also show that our resulting ratio is asymptotically tight.