Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Load balancing and Poisson equation in a graph
Concurrency: Practice and Experience
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
Engineering Diffusive Load Balancing Algorithms Using Experiments
IRREGULAR '97 Proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel
Quality Balancing for Parallel Adaptive FEM
IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
Dynamic repartitioning of adaptively refined meshes
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Heterogeneous Dynamic Load Balancing with a Scheme Based on the Laplacian Polynomial
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Optimal and Alternating-Direction Load Balancing Schemes
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
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We design a general mathematical framework to analyze the properties of nearest neighbor balancing algorithms of the diffusion type. Within this framework we develop a new optimal polynomial scheme (OPS) which we show to terminate within a finite number m of steps, where m only depends on the graph and not on the initial load distribution. We show that all existing diffusion load balancing algorithms, including OPS determine a flow of load on the edges of the graph which is uniquely defined, independent of the method and minimal in the l2-norm. This result can also be extended to edge weighted graphs. The l2-minimality is achieved only if a diffusion algorithm is used as preprocessing and the real movement of load is performed in a second step. Thus, it is advisable to split the balancing process into the two steps of first determining a balancing flow and afterwards moving the load. We introduce the problem of scheduling a flow and present some first results on the approximation quality of local greedy heuristics.