Earliest starting and finishing time duplication-based algorithm
SPECTS'09 Proceedings of the 12th international conference on Symposium on Performance Evaluation of Computer & Telecommunication Systems
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
On cluster resource allocation for multiple parallel task graphs
Journal of Parallel and Distributed Computing
Work-stealing for mixed-mode parallelism by deterministic team-building
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Fast approximation algorithms for scheduling independent multiprocessor tasks
Proceedings of the 19th High Performance Computing Symposia
Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
A lightweight task graph scheduler for distributed high-performance scientific computing
PARA'12 Proceedings of the 11th international conference on Applied Parallel and Scientific Computing
Scheduling of scientific workflow in non-dedicated heterogeneous multicluster platform
Journal of Systems and Software
Semi-automatic restructuring of offloadable tasks for many-core accelerators
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Future Generation Computer Systems
Combined scheduling and mapping for scalable computing with parallel tasks
Scientific Programming - Biological Knowledge Discovery and Data Mining
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Applications structured as parallel task graphs exhibit both data and task parallelism and arise in many domains. Scheduling these applications efficiently on parallel platforms has been a long-standing challenge. In the case of a single homogeneous platform, such as a cluster, results have been obtained both in theory, i.e., guaranteed algorithms, and, in practice, i.e., pragmatic heuristics. Due to task parallelism, these applications are well suited for execution on distributed platforms that span multiple clusters possibly in multiple institutions. However, the only available results in this context are nonguaranteed heuristics. In this paper, we develop a scheduling algorithm, MCGAS, which is applicable to multicluster platforms that are almost homogeneous. Such platforms are often found as large subsets of multicluster platforms. Our novel contribution is that MCGAS computes task allocations so that a (tunable) performance guarantee is provided. Since a performance guarantee does not necessarily imply good average performance in practice, we also compare MCGAS with a recently proposed nonguaranteed algorithm. Using simulation over a wide range of experimental scenarios, we find that MCGAS leads to better average application makespans than its competitor.