Data networks
Optimal selection theory for superconcurrency
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Stochastic performance models of parallel task systems (extended abstract)
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
A probabilistic framework for estimation of execution time in heterogeneous computing systems
A probabilistic framework for estimation of execution time in heterogeneous computing systems
A tool for performance estimation of networked embedded end-systems
DAC '98 Proceedings of the 35th annual Design Automation Conference
AsaP—a framework for evaluating run-time schedulers in embedded multimedia end-systems
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
IEEE Transactions on Parallel and Distributed Systems
Characterization and enhancement of Static Mapping Heuristics for Heterogeneous Systems
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
A Stochastic Framework for Co-synthesis of Real-Time Systems
LCTES '00 Proceedings of the ACM SIGPLAN Workshop on Languages, Compilers, and Tools for Embedded Systems
Simulation of Task Graph Systems in Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
ACM Transactions on Embedded Computing Systems (TECS)
Self-adaptive task allocation and scheduling of meta-tasks in non-dedicated heterogeneous computing
International Journal of High Performance Computing and Networking
Performance under Failures of DAG-based Parallel Computing
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
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The problem of statically estimating the execution time distribution for a task graph consisting of a collection of subtasks to be executed in a heterogeneous computing (HC) system is considered. Execution time distributions for the individual subtasks are assumed to be known. A mathematical model for the communication network: that interconnects the machines of the HC system is introduced and a probabilistic approach is developed to estimate the overall execution time distribution of the task graph. It is shown that, for a given matching and scheduling, computing the exact distribution of the overall execution time of a task graph is very difficult, and thus impractical. The proposed approach approximates the exact distribution and requires a relatively small amount of calculation time. The accuracy of the proposed approach is demonstrated mathematically through the derivation of bounds that quantify the difference between the exact distribution and that provided by the proposed approach. Numerical studies are also included to further validate the utility of the proposed methodology.