Partitioning computational load over different processing elements is a crucial issue in parallel computing. This is particularly relevant in the case of parallel execution of structured grid computational models, suc...
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ISBN:
(纸本)9783030390815
Partitioning computational load over different processing elements is a crucial issue in parallel computing. This is particularly relevant in the case of parallel execution of structured grid computational models, such as Cellular Automata (CA), where the domain space is partitioned in regions assigned to the parallel computing nodes. In this work, we present a dynamic load balancing technique that provides for performance improvements in structured grid model execution on distributed memory architectures. First tests implemented using the MPI technology have shown the goodness of the proposed technique in sensibly reducing execution times with respect to not-balanced parallel versions.
The Cellular Automata paradigm is an efficient tool to model and study complex systems such as traffic simulation, lava flows and swarm based behaviour. In addition, cellular automata can be profitably used in many ma...
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ISBN:
(纸本)9781728116440
The Cellular Automata paradigm is an efficient tool to model and study complex systems such as traffic simulation, lava flows and swarm based behaviour. In addition, cellular automata can be profitably used in many mathematical problems such as differential equations and chaos theory. Due to their inherent parallel nature, cellular automata can be efficiently parallelized among a set of computing nodes in order to scale and speed up their execution. This paper presents a preliminary study on different parallelizzation techniques for structured grid models such as cellular automata on distributed memory architectures. In particular, three strategies are presented and compared in order to evaluate their efficiency in terms of speed-up. An experimental section shows the performance achieved by the three strategies when a real-life application, namely the SciddicaT cellular automata model for debris-flows simulation, is adopted.
GPGPU (General Purpose computing on Graphics Processing Units) has marked a revolution in the field of parallel Computing allowing to achieve computational performance unimaginable until a few years ago. This hardware...
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ISBN:
(纸本)9781467394734
GPGPU (General Purpose computing on Graphics Processing Units) has marked a revolution in the field of parallel Computing allowing to achieve computational performance unimaginable until a few years ago. This hardware has proven to be extremely reliable and suitable to simulate Cellular Automata (CA) models for modeling complex systems whose evolution can be described in terms of local interactions. Starting from previous GPGPU implementations of CA models with CUDA, this paper presents an effective implementation of a well-known numerical model for simulating lava flows on Graphical Processing Units (GPU) based on the OpenCL (Open Computing Language) standard. In addition, a preliminary Civil Defence application related Hazard maps of an area located at Mt. Etna volcano (South Italy), confirms the validity of OpenCL and both low-cost and high-end graphics hardware as an alternative to expensive solutions for the simulation of CA models.
parallel computers differ from conventional serial computers in that they can, in a variety of ways, perform more than one operation at a time. parallel processing, the application of parallel computers, has been succ...
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parallel computers differ from conventional serial computers in that they can, in a variety of ways, perform more than one operation at a time. parallel processing, the application of parallel computers, has been successfully utilized in many fields of science and technology. The purpose of this paper is to review efforts to use parallel processing for statistical computing. We present some technical background, followed by a review of the literature that relates parallel computing to statistics. The review material focuses explicitly on statistical methods and applications, rather than on conventional mathematical techniques. Thus, most of the review material is drawn from statistics publications. We conclude by discussing the nature of the review material and considering some possibilities for the future.
We consider the parallel computation of flows of integral fluids on a heterogeneous network of workstations. The proposed methodology is relevant to computational mechanics problems which involve a compute-intensive t...
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We consider the parallel computation of flows of integral fluids on a heterogeneous network of workstations. The proposed methodology is relevant to computational mechanics problems which involve a compute-intensive treatment of internal variables (e.g. fibre suspension flow and deformation of viscoplastic solids). The main parallel computing issue in such applications is that of load balancing. Both static and dynamic allocation of work to processors are considered in the present paper. The proposed parallel algorithms have been implemented in an experimental, parallel version of the commercial POLYFLOW package developed in Louvain-la-Neuve. The implementation uses the public domain PVM software library (parallel Virtual Machine), which we have extended in order to ease porting to heterogeneous networks. We describe parallel efficiency results obtained with three PVM configurations, involving up to seven workstations with maximum relative processing speeds of five. The physical problems are the stick/slip and abrupt contraction flows of a K.B.K.Z. integral fluid. Using static allocation, parallel efficiencies in the range 67%-85% were obtained on a PVM network with four workstations having relative speeds of 2:1:1:1. parallel efficiencies higher than 90% were obtained on the three PVM configurations using the dynamic load-balancing schemes.
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