Dynamic structured partitioning for parallel scientific applications with pointwise varying workloads

Parallel implementations of scientific applications involving the simulation of reactive flow on structured grids are challenging, since the underlying phenomena include transport processes with uniform computational loads as well as reactive processes having pointwise varying workloads. As a result, traditional parallelization approaches that assume homogeneous loads are not suitable for these simulations. This paper presents “Dispatch”, a dynamic structured partitioning strategy that has been applied to parallel uniform and adaptive formulations of simulations with computational heterogeneity. Dispatch maintains the computational weights associated with pointwise processes in a distributed manner, computes the local workloads and partitioning thresholds, and performs in-situ localitypreserving load balancing. The experimental evaluation of Dispatch using an illustrative 2-D reactive-diffusion kernel demonstrates improvement in load distribution and overall application performance

Dynamic Structured Partitioning for Parallel Scientific Applications with Pointwise Varying Workloads

Parallel implementations of scientific applications involving the simulation of reactive flow on structured grids are challenging, since the underlying phenomena include transport processes with uniform computational loads as well as reactive processes having pointwise varying workloads. As a result, traditional parallelization approaches that assume homogeneous loads are not suitable for these simulations. This paper presents “Dispatch”, a dynamic structured partitioning strategy that has been applied to parallel uniform and adaptive formulations of simulations with computational heterogeneity. Dispatch maintains the computational weights associated with pointwise processes in a distributed manner, computes the local workloads and partitioning thresholds, and performs in-situ localitypreserving load balancing. The experimental evaluation of Dispatch using an illustrative 2-D reactive-diffusion kernel demonstrates improvement in load distribution and overall application performance

A pattern language for parallelizing irregular algorithms

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia InformáticaIn irregular algorithms, data set’s dependences and distributions cannot be statically predicted. This class of algorithms tends to organize computations in terms of data locality instead of parallelizing control in multiple threads. Thus, opportunities for exploiting parallelism vary dynamically, according to how the algorithm changes data dependences. As such, effective parallelization of such algorithms requires new approaches that account for that dynamic nature. This dissertation addresses the problem of building efficient parallel implementations of irregular algorithms by proposing to extract, analyze and document patterns of concurrency and parallelism present in the Galois parallelization framework for irregular algorithms. Patterns capture formal representations of a tangible solution to a problem that arises in a well defined context within a specific domain. We document the said patterns in a pattern language, i.e., a set of inter-dependent patterns that compose well-documented template solutions that can be reused whenever a certain problem arises in a well-known context