Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
Diffusive Load-Balancing Policies for Dynamic Applications
IEEE Concurrency
A Practical Approach to Dynamic Load Balancing
IEEE Transactions on Parallel and Distributed Systems
Customized dynamic load balancing for a network of workstations
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
The load unbalancing problem for region growing image segmentation algorithms
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
New challanges in dynamic load balancing
Applied Numerical Mathematics - Adaptive methods for partial differential equations and large-scale computation
Enhancing enterprise service bus capability for load balancing
WSEAS Transactions on Computers
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Distributed load balancing strategies are burdened with a high communication latency compared to centralised strategies. This latency is originated due to a necessary status profile reported from each computing node to every node, (all to all operation involving expensive synchronization costs). This handicap is lessened due to a certain locality character imposed by the way these strategies are implemented. Many scientific applications are affected by specific communications patterns, these patterns establishes relationships among neighboring data configurations. Application performance is severely affected when a migration process is carried out without taking into account this data neighborhood relationship. It is our main aim to evaluate the performance gains obtained when load balancing strategies are complemented with locality awareness techniques and applied to such applications. Doing this we ensure that migrations preserve as much as possible the data locality relationship aforementioned. Load balancing and preservation of locality issues were achieved by means of a multithreaded environment called AMPI. This framework makes possible to embed a MPI process into a user level thread.