Dynamic Load Balancing for Cluster Computing

In parallel simulations, partitioning and load-balancing algorithms compute the distribution of application data and work to processors. The effectiveness of this distribution greatly influences the performance of a parallel simulation. Decompositions that balance processor loads while keeping the application's communication costs low are preferred. Although a wide variety of partitioning and load-balancing algorithms have been developed, but the load-balancing problem is not yet solved completely as their effectiveness depends on the characteristics of the application using them. New applications and architectures require new partitioning features. New models are needed for non-square, non-symmetric, and highly connected systems arising from applications in biology, circuits, and materials simulations. Increased use of heterogeneous computing architectures requires partitioners that account for non-uniform computing, network, and memory resources. This paper introduces the topic and proposes algorithm to paralellize adaptive grid generation on a cluster followed by a brief look at the future prospects of DLB.