Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Load-sharing in heterogeneous systems via weighted factoring
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Dynamic Load Balancing for Parallel Adaptive Mesh Refinement
IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
Mapping and Load-Balancing Iterative Computations
IEEE Transactions on Parallel and Distributed Systems
Dynamic Load Balancing and Efficient Load Estimators for Asynchronous Iterative Algorithms
IEEE Transactions on Parallel and Distributed Systems
Data Partitioning with a Functional Performance Model of Heterogeneous Processors
International Journal of High Performance Computing Applications
Dynamic load balancing with adaptive factoring methods in scientific applications
The Journal of Supercomputing
Dynamic Load Balancing on Dedicated Heterogeneous Systems
Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Scheduling divisible loads on heterogeneous desktop systems with limited memory
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Hi-index | 0.00 |
Traditional load balancing algorithms for data-intensive iterative routines can successfully load balance relatively small problems. We demonstrate that they may fail for large problem sizes on computational clusters with memory heterogeneity. Traditional algorithms use too simplistic models of processors performance which cannot reflect many aspects of heterogeneity. This paper presents a new dynamic load balancing algorithm based on the advanced functional performance model. The model consists of speed functions of problem size, which are built adaptively from a history of load measurements. Experimental results demonstrate that our algorithm can successfully balance data-intensive iterative routines on parallel platforms with memory heterogeneity.