Economies of scale in computing: Grosch's law revisited
Communications of the ACM
Measuring Parallelism in Computation-Intensive Scientific/Engineering Applications
IEEE Transactions on Computers
Static and dynamic processor scheduling disciplines in heterogeneous parallel architectures
Journal of Parallel and Distributed Computing
Optimal Scheduling Algorithm for Distributed-Memory Machines
IEEE Transactions on Parallel and Distributed Systems
On Exploiting Task Duplication in Parallel Program Scheduling
IEEE Transactions on Parallel and Distributed Systems
Solving Linear Algebraic Equations on an MIMD Computer
Journal of the ACM (JACM)
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Duplication-Based Scheduling Algorithm for Interconnection-Constrained Distributed Memory Machines
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
A Static Scheduling Heuristic for Heterogeneous Processors
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Fast and Effective Task Scheduling in Heterogeneous Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
A Scalable Task Duplication Based Scheduling Algorithm for Heterogeneous Systems
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
LDBS: A Duplication Based Scheduling Algorithm for Heterogeneous Computing Systems
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Triplet: A Clustering Scheduling Algorithm for Heterogeneous Systems
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
List scheduling with duplication for heterogeneous computing systems
Journal of Parallel and Distributed Computing
Reliability-aware scheduling strategy for heterogeneous distributed computing systems
Journal of Parallel and Distributed Computing
An effective compaction strategy for bi-criteria DAG scheduling in grids
International Journal of Communication Networks and Distributed Systems
A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks
Journal of Parallel and Distributed Computing
Energy-Aware Scheduling Algorithm with Duplication on Heterogeneous Computing Systems
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
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Scheduling is one of the vital design issues for heterogeneous computing systems. The work available in literature, by and large, focuses on the scheduling of 'metatasks' (set of independent tasks with only few data dependent subtasks) in such an environment. A majority of complex scientific/engineering applications, however, fall in the category of precedence-constrained task graphs. Scheduling of such graphs is generally limited to list-based techniques. Further, diverse performance metrics and heterogeneity models, as adopted by various researchers, make the comparison process quite inconclusive. We have made an attempt to categorize heterogeneity models and performance metrics so as to have better understanding and to facilitate a more uniform platform for developing and comparing such algorithms. Further, a generic scheduling strategy is presented, which improves the performance of list-based heuristics with the addition of limited duplication. A comparison of state-of-the-art scheduling algorithms is next performed using an A-Cube performance model that has been suggested to study the behavior of an algorithm, application and architecture in the presence of heterogeneity. Depending upon the type and extent of heterogeneity, performance of an algorithm is found to degrade with heterogeneity as the penalty imposed for any injudicious move on the part of heuristic tends to be more severe for heterogeneous computing environment in comparison to its homogeneous counterpart. However, the proposed heterogeneous limited duplication algorithm, having its roots in our earlier suggested SD algorithm, succeeds substantially in overcoming these 'stresses' and 'strains; of heterogeneity, and retains the best features of duplication without compromising much on the complexity front.