Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Dynamic Remapping of Parallel Computations with Varying Resource Demands
IEEE Transactions on Computers
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Optimal Dynamic Remapping of Data Parallel Computations
IEEE Transactions on Computers
An Analysis of Scatter Decomposition
IEEE Transactions on Computers
Efficient load balancing and data remapping for adaptive grid calculations
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Automated parallelization of discrete state-space generation
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
Dynamic data distribution and processor repartitioning for irregularly structured computations
Journal of Parallel and Distributed Computing - Special issue on irregular problems in supercomputing applications
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
The Journal of Supercomputing
A Practical Approach to Dynamic Load Balancing
IEEE Transactions on Parallel and Distributed Systems
Optimal Periodic Remapping of Bulk Synchronous Computations on Multiprogrammed Distributed Systems
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
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A bulk synchronous computation proceeds in phases that are separated by barrier synchronization. For dynamic bulk synchronous computations that exhibit varying phase-wise computational requirements, remapping at run-time is an effective approach to ensure parallel efficiency. This paper introduces a novel remapping strategy for computations whose workload changes can be modeled as a Markov chain. It is shown that optimal remapping can be formulated as a binary decision process: remap or not at a given synchronizing instant. The optimal strategy is then developed for long lasted computations by employing optimal stopping rules in a stochastic control framework. The existence of optimal controls is established. Necessary and sufficient conditions for the optimality are obtained. Furthermore, a policy iteration algorithm is devised to reduce computational complexity and enhance fast convergence to the desired optimal control.