ÐLoad-balancing problems arise in many applications, but, most importantly, they play a special role in the operation of parallel and distributed computing systems. Load-balancing deals with partitioning a program into smaller tasks that can be executed concurrently and mapping each of these tasks to a computational resource such a processor (e.g., in a multiprocessor system) or a computer (e.g...

We describe a dynamic load-balancing strategy for parallel finite element methods with adaptive mesh (h-) and order (p-) refinement. The load-balancing algorithm is based on the tiling load-balancing system, where global balance is achieved by performing local balancing within overlapping neighborhoods of processors. Tiling is applied to each mesh level created by the adaptive h-refinement. Wei...

Computation of the eigenvalues of a symmetric tridiagonal matrix is a problem of great relevance. Many linear algebra libraries provide subroutines for solving it. But none of them is oriented to be executed in heterogeneous distributed memory multicomputers. In this work we focus on this kind of platforms. Two different load balancing schemes are presented and implemented. The experimental res...

We present a multiprocessor “stop-the-world” garbage collection framework that provides multiple forms of load balancing. Our parallel collectors use this framework to balance the work of root scanning, using static overpartitioning, and also to balance the work of tracing the object graph, using a form of dynamic load balancing called work stealing. We describe two collectors written using thi...

Designing a good task allocation algorithm faces the challenge of allowing high levels of throughput, so that tasks are executed fast and processor parallelism is exploited, while still guaranteeing a low level of memory contention, so that performance does not su er because of limitations on processor-to-memory bandwidth. In this work, we present a comparative study of throughput and contentio...

In this paper, we present a load-balancing strategy (Adaptive Load Balancing strategy) for data parallel applications to balance the work load effectively on a distributed system. We study its impact on computation-hungry matrix multiplication application. The ALB strategy enhances the performance with features such as intelligent node selection, pre-task assignment, adaptive task sizing and bu...

The dynamic load balancing problem which can be defined as the effective redistribution of workload among the system processing units during execution time, is dynamic in nature where the load and the processing power of the system may change in time as units of work enter and leave the system and processing units are added to or removed from the processing pool. To address this problem, geneti...

A very important issue in distributed systems is the management of load among system nodes. Load balancing enables a better use of the system resource capabilities and a better performance by allocating nodes that are more suitable for the execution of some tasks. This work proposes a multi-agent load balancing architecture for distributed object applications denominated MALBA which uses artifi...

This paper provides an overview of data structures suitable for distributed storage of nite element mesh data as well as software designed for mesh adaptation and load balancing. During the course of a parallel computation, processor load imbalances are introduced at adaptive enrichment steps. Two predictive load balancing schemes that use a priori estimates of work load for adaptive re-nement ...

Cloud computing is a technology in distributed computing which facilitates pay per model based on user demand and requirement. Cloud can be defined as a collection of virtual machines. This includes both computational and storage facility. The goal of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud Computing is developing day by day and f...