The performance of parallel Monte Carlo transport calculations which use both spatial and particle parallelism is increased by dynamically assigning processors to the most worked domains. Since the particle work load varies over the course of the simulation, each cycle this algorithm determines if dynamic load balancing would speed up the calculation. If load balancing is required, a small numb...

The performance of parallel Monte Carlo transport calculations which use both spatial and particle parallelism is increased by dynamically assigning processors to the most worked domains. Since he particle work load varies over the course of the simulation, this algorithm determines each cycle if dynamic load balancing would speed up the calculation. If load balancing is required, a small numbe...

Diffusion Monte Carlo is a highly accurate Quantum Monte Carlo method for the electronic structure of materials, but it requires frequent load balancing or population redistribution steps to maintain efficiency on parallel machines. This step can be a significant factor affecting performance, and will become more important as the number of processing elements increases. We propose a new dynamic...

There are at least three dimensions of overhead to be considered by any parallel job scheduling algorithm: load balancing, synchronization, and communication overhead. In this work we first study several heuristics to choose the next to run from a global processes queue. After that we present a mechanism to decide at runtime weather to apply Local process queue per processor or Global processes...

Networks of workstations become more and more appropriate for parallel applications, as modern network technology enables high quality communication between powerful workstations. In this perspective, load balancing software must be extremely exible as the set of available nodes for a particular distributed memory application may change at run time. XENOOPS is an advanced environment for parall...

Distributed simulations require partitioning mechanisms to operate, and the best partitioning algorithms try to load-balance the partitions. Dynamic load-balancing, i.e. re-partitioning simulation environments at run-time, becomes essential when the load in the partitions change. In decentralised distributed simulation the information needed to dynamically load-balance seems difficult to collec...

Any code that parallelizes computational work is required to come up with a method to load balance the work across the available processors. Most codes of this nature use some heuristic to decide how to perform this load balancing. In essence, they must have a (cheap) method of coming up with a number to act as a proxy for dividing up the actual computational work. For some applications that us...

| Runtime Incremental Parallel Scheduling (RIPS) is an alternative strategy to the commonly used dynamic scheduling. In this scheduling strategy, the system scheduling activity alternates with the underlying computation work. RIPS utilizes the advanced parallel scheduling technique to produce a low-overhead, high-quality load balancing, as well as adapting to irregular applications. This paper ...

The Design, Optimization, and Performance of an Adaptive Middlware Load Balancing Service by Ossama Othman Master of Science in Electrical and Computer Engineering University of California, Irvine Professor Douglas C. Schmidt, Chair Distributed object computing (DOC) middleware is increasingly used as the infrastructure for applications with stringent quality of service (QoS) requirements, incl...