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IEEE Transactions on Computers
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IEEE Transactions on Software Engineering
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Communications of the ACM
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PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
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International Journal of Parallel Programming
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IEEE Transactions on Parallel and Distributed Systems
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IEEE Transactions on Parallel and Distributed Systems
Cacheminer: A Runtime Approach to Exploit Cache Locality on SMP
IEEE Transactions on Parallel and Distributed Systems
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IWCC '01 Proceedings of the NATO Advanced Research Workshop on Advanced Environments, Tools, and Applications for Cluster Computing-Revised Papers
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PARA '00 Proceedings of the 5th International Workshop on Applied Parallel Computing, New Paradigms for HPC in Industry and Academia
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
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Journal of Parallel and Distributed Computing
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Journal of Functional Programming
Feedback guided dynamic loop scheduling: convergence of the continuous case
The Journal of Supercomputing - Special issue: Parallel and distributed processing and applications
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Proceedings of the 2007 workshop on Declarative aspects of multicore programming
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International Journal of Computational Science and Engineering
Dynamic multi phase scheduling for heterogeneous cluste
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Proposal of an analytical solution for the load imbalance problem in parallel systems
ISPDC'03 Proceedings of the Second international conference on Parallel and distributed computing
A new carried-dependence self-scheduling algorithm
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
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Using runtime information of load distributions and processor affinity, we propose an adaptive scheduling algorithm and its variations from different control mechanisms. The proposed algorithm applies different degrees of aggressiveness to adjust loop scheduling granularities, aiming at improving the execution performance of parallel loops by making scheduling decisions that match the real workload distributions at runtime. We experimentally compared the performance of our algorithm and its variations with several existing scheduling algorithms on two parallel machines: the KSR-1 and the Convex Exemplar. The kernel application programs we used for performance evaluation were carefully selected for different classes of parallel loops. Our results show that using runtime information to adaptively adjust scheduling granularity is an effective way to handle loops with a wide range of load distributions when no prior knowledge of the execution can be used. The overhead caused by collecting runtime information is insignificant in comparison with the performance improvement. Our experiments show that the adaptive algorithm and its five variations outperformed the existing scheduling algorithms.