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SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
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SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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ICS '98 Proceedings of the 12th international conference on Supercomputing
Automatic I/O hint generation through speculative execution
OSDI '99 Proceedings of the third symposium on Operating systems design and implementation
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Performance Evaluation
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IEEE Micro
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Performance '87 Proceedings of the 12th IFIP WG 7.3 International Symposium on Computer Performance Modelling, Measurement and Evaluation
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HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
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IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
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SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
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Large-scale computing often consists of many speculative tasks to test hypotheses, search for insights, and review potentially finished products. E.g., speculative tasks are issued by bioinformaticists comparing DNA sequences and computer graphics artists adjusting scene properties. We promote a way of working that exploits the inherent speculation in application-level search made more common by the cost-effectiveness of grid and cluster computing. Researchers and end-users disclose sets of speculative tasks that search an application space, request specific results as needed, and cancel unfinished tasks if early results suggest no need to continue. Doing so matches natural usage patterns, making users more effective, and also enables a new class of schedulers.In simulation, we show how batchactive schedulers reduce user-observed response times relative to conventional models in which tasks are requested one at a time (interactively) or in batches without specifying which tasks are speculative. Over a range of situations, user-observed response time is about 50% better on average and at least two times better for 20% of our simulations. Moreover, we show how user costs can be reduced under an incentive cost model of charging only for tasks whose results are requested.