Towards an architecture-independent analysis of parallel algorithms
SIAM Journal on Computing
Space-efficient scheduling of multithreaded computations
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Exploiting process lifetime distributions for dynamic load balancing
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
LogP: a practical model of parallel computation
Communications of the ACM
On optimal strategies for cycle-stealing in networks of workstations
IEEE Transactions on Software Engineering
In search of clusters (2nd ed.)
In search of clusters (2nd ed.)
Load-balancing heuristics and process behavior
SIGMETRICS '86/PERFORMANCE '86 Proceedings of the 1986 ACM SIGMETRICS joint international conference on Computer performance modelling, measurement and evaluation
A Case for NOW (Networks of Workstations)
IEEE Micro
The Computational Co-op: Gathering Clusters into a Metacomputer
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
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We refine the model underlying our prior work on scheduling cycle-stealing opportunities in NOWs [5, 16], obtaining a model wherein the scheduling guidelines of [16] produce optimal schedules for every such opportunity. Although computing optimal schedules usually requires the use of general (often inefficient) function-optimizing methods, we show how to compute optimal schedules efficiently for the broad class of opportunities whose durations come from a concave probability distribution. Even when no such efficient computation of an optimal schedule is available, our refined model always suggests a natural notion of approximately optimal schedule, which may be efficiently computable. We illustrate such efficient approximability via the important class of cycle-stealing opportunities whose durations come from a heavy-tailed distribution. Such opportunities do not admit any optimal schedule—nor even a natural notion of approximately optimal schedule—within the model of [5, 16]. Within our refined model, though, we derive computationally simple schedules for heavy-tailed opportunities, which can be “tuned” to have expected work-output that is arbitrarily close to optimal.