Detail výsledku
GPU Accelerated Solver of Time-Dependent Air Pollutant Transport Equations
Šimek Václav, Ing., UPSY (FIT)
Zbořil František, doc. Ing., CSc., UITS (FIT)
Drábek Vladimír, doc. Ing., CSc., UPGM (FIT), UPSY (FIT)
Main objective of this paper is to outline possible ways how to achieve
a substantial acceleration in case of advection-diffusion equation
(A-DE) calculation, which is commonly used for a description of the
pollutant behavior in atmosphere. A-DE is a kind of partial
differential equation (PDE) and in general case it is usually solved by
numerical integration due to its high complexity. These types of
calculations are time consuming thus the main idea of our work is to
adopt CUDA platform and commodity GPU card to do the calculations in a
faster way. The solution is based on method of lines with 4th order
Runge-Kutta scheme to handle the integration. As a matter of fact, the
selected approach involves number of auxiliary variables and thus the
memory management is critical in order to achieve desired performance.
From a technical point of view, we have implemented a particular
variant of the A-DE system, where the pollutant concentration is
time-dependent. An efficient data handling is primarily based on the
exploitation of shared memory blocks and texture caches inside GPU
chip. Detailed evaluation of the obtained results is given in this
paper where an astonishing execution speed up of GPU-based solution is
demonstrated in comparison to standard CPU.
CUDA; GPU; advection-diffusion equation; partial differential equation; Runge-Kutta; acceleration
@inproceedings{BUT33783,
author="Radim {Dvořák} and Václav {Šimek} and František {Zbořil} and Vladimír {Drábek}",
title="GPU Accelerated Solver of Time-Dependent Air Pollutant Transport Equations",
booktitle="12th EUROMICRO Conference on Digital System Design DSD 2009",
year="2009",
pages="707--713",
publisher="IEEE Computer Society",
address="Patras",
doi="10.1109/DSD.2009.146",
isbn="978-0-7695-3277-6"
}