Allocating programs containing branches and loops within a multiple processor system
IEEE Transactions on Software Engineering
Heuristic Algorithms for Task Assignment in Distributed Systems
IEEE Transactions on Computers
Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Optimal selection theory for superconcurrency
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Introduction to algorithms
Software support for heterogeneous computing
ACM Computing Surveys (CSUR)
IEEE Transactions on Parallel and Distributed Systems
A parallel approach for multiprocessor scheduling
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Loop scheduling for heterogeneity
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Graph Theory With Applications
Graph Theory With Applications
IEEE Transactions on Parallel and Distributed Systems
Dynamic scheduling on a PC cluster
Proceedings of the 1999 ACM symposium on Applied computing
A sorting algorithm on a PC cluster
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 2
IEEE Transactions on Parallel and Distributed Systems
A Proposal for a Heterogeneous Cluster ScaLAPACK (Dense Linear Solvers)
IEEE Transactions on Computers
Adaptive parallel computing on heterogeneous networks with mpC
Parallel Computing
A Realistic Model and an Efficient Heuristic for Scheduling with Heterogeneous Processors
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
An Overview of MSHN: The Management System for Heterogeneous Networks
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Journal of Parallel and Distributed Computing
A semi-static approach to mapping dynamic iterative tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing
Data Partitioning with a Functional Performance Model of Heterogeneous Processors
International Journal of High Performance Computing Applications
The impact of heterogeneity on master-slave scheduling
Parallel Computing
Parallel exact inference on the Cell Broadband Engine processor
Journal of Parallel and Distributed Computing
Energy aware DAG scheduling on heterogeneous systems
Cluster Computing
The impact of heterogeneity on master-slave on-line scheduling
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
SPHINX: a scheduling middleware for data intensive applications on a grid
International Journal of Internet Protocol Technology
International Journal of Communication Networks and Distributed Systems
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In a heterogeneous computing (HC) environment consisting of different types of machines, an application program is decomposed into subtasks, each of which is computationally homogeneous. The goal is to execute subtasks on the machines in such a way that the total program execution time is minimized. A mathematical framework is presented that models the matching of subtasks to machines, scheduling of subtasks' computation, scheduling of intermachine communication steps, and selection of sources of shared data items for intermachine communication (data relocation). The goal of this work is to generate a provably optimal scheme for communicating shared data among subtasks as an enhancement to any given matching and scheduling. Initially, it is assumed that at any instant in time, only one machine is being used for program execution and only one subtask is being executed. Based on this assumption, a polynomial algorithm is introduced to optimize scheduling and data relocation with respect to any given matching of subtasks to machines. The data relocation scheme is then extended to reduce intermachine data communication time in an HC environment with a given matching and scheduling of subtasks' computation where: 1) multiple subtasks' computations can be performed concurrently on different machines; 2) subtask computation stteps can be overlapped with other subtasks' communication steps for intermachine data transfers; and 3) machines in the HC suite are interconnected by a shared-bus type of network.