Automatic determination of grain size for efficient parallel processing
Communications of the ACM - Special issue: multiprocessing
Profile guided code positioning
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
IMPACT: an architectural framework for multiple-instruction-issue processors
ISCA '91 Proceedings of the 18th annual international symposium on Computer architecture
Profile-driven compilation
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Program repartitioning on varying communication cost parallel architectures
Journal of Parallel and Distributed Computing
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
DeBugging and Performance Tuning for Parallel Computing Systems
DeBugging and Performance Tuning for Parallel Computing Systems
Grain Size Determination for Parallel Processing
IEEE Software
Compile-Time Estimation of Communication Costs on Multicomputers
IPPS '92 Proceedings of the 6th International Parallel Processing Symposium
Gprof: A call graph execution profiler
SIGPLAN '82 Proceedings of the 1982 SIGPLAN symposium on Compiler construction
Adaptive optimization for self: reconciling high performance with exploratory programming
Adaptive optimization for self: reconciling high performance with exploratory programming
Trace Scheduling: A Technique for Global Microcode Compaction
IEEE Transactions on Computers
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Increasingly, feedback of measured run-time information is being used in the optimization of computation execution. This paper introduces a model relating the static view of a computation to its run-time variance that is useful in this context. A notion of uncertainty is then used to provide bounds on key scheduling parameters of the run-time computation. To illustrate the relationship between fidelity in measured information and minimum schedulable, grain size, we apply the bounds to three existing parallel architectures for the case of run-time variance caused by monitoring intrusion. We also outline a hybrid static-dynamic scheduling paradigm-SEDIA-that uses the model of uncertainty to optimize computation for execution in the presence of run-time variance from sources other than monitoring intrusion.