Load-balancing spatially located computations using rectangular partitions

Load-Balancing Spatially Located Computations using Rectangular Partitions

Distributing spatially located heterogeneous workloads is an important problem in parallel scientific computing. We investigate the problem of partitioning such workloads (represented as a matrix of non-negative integers) into rectangles, such that the load of the most loaded rectangle (processor) is minimized. Since finding the optimal arbitrary rectangle-based partition is an NP-hard problem,...

Load-Balancing Iterative Computations on Heterogeneous Clusters

A Load Balancing Technique for Multiphase Computations

Parallel computations comprised of multiple, tightly interwoven phases of computation may require a different approach to dynamic load balancing than single-phase computations. This paper presents a load sharing method based on the view of load as a vector, rather than as a scalar. This approach allows multiphase computations to achieve higher efficiency on large-scale multicomputers than possi...

Load balancing strategies for symbolic vision computations

Most intermediate and high-level vision algorithms manipulate symbolic features. A key operation in these vision algorithms is to search symbolic features satisfying certain geometric constraints. Parallelizing this symbolic search needs a non-trivial algorithmic technique due to the unpredictable workload. In this paper, we propose load balancing strategies for parallelizing symbolic search op...

Randomized Receiver Initiated Load-balancing Algorithms for Tree-shaped Computations

This paper considers generic load-balancing algorithms which efficiently parallelize a large class of applications based on traversing implicitly defined trees with irregular shape. First, a previous model is generalized yielding tree-shaped computations which cover the cost for communication and problem splitting, a measure of granularity and an easy to quantify parameter which limits irregula...