How network topology affects dynamic loading balancing

The authors compare the perJbrmances of five dynamic loadbalancing strategies. The simulator they ’ue developed lets them measure these performances across a range of network topologies, including a 20 mesh, a 4 0 hypercube, a linear array, and a composite Fibonacci cube. multiprocessor network without load balancing processes processor-generated tasks locally with little or no sharing of computational resources. Load balancing, on the other hand, uses a multiprocessor network‘s inherently redundant A processing power by redistributing the workload among the processors to improve the application’s overall performance. Load-balancing strategies fall broadly into either statzc or dynamic classifications. A network with static load balancing computes task information, such as execution time (execution cost), from the application before load distribution. The network distributes tasks once, before execution, and the allocation stays the same throughout the application’s execution. A network with dynamic load balancing uses little or no a priori task information, and must satisfy changing requirements by making task-distribution decisions during runtime. For certain applications, dynamic load balancing is preferable, because then the problem’s variable behavior more closely matches available computational resources. But dynamic load balancing incurs communication overheads that are topology-dependent (where topology is the interconnection structure of the multiprocessor network). Researchers have proposed several load-balancing strategies. l-9 However, in most cases, these researchers made performance comparisons using either a simulated distributed computer ~ y s t e m ’ , ~ , ~ or a multiprocessor network with a specific t o p o l ~ g y . ~ , ~ . ~ We have developed a topology-independent simulator to compare the performances of five well-known, dynamic load-balancing strategies: the Gradient Model