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20th Annual International Conference on Computational Science (ICCS)

作者： Sanjuan, Gemma Margalef, Tomas Cortes, Ana Univ Autonoma Barcelona Comp Architecture & Operating Syst Dept Cerdanyola Del Valles Spain

ISBN: (纸本)9783030504366;9783030504359

Forest fires provoke significant loses from the ecological, social and economical point of view. Furthermore, the climate emergency will also increase the occurrence of such disasters. In this context, forest fire propagation prediction is a key tool to fight against these natural hazards efficiently and mitigate the damages. However, forest fire spread simulators require a set of input parameters that, in many cases, cannot be measured and must be estimated indirectly introducing uncertainty in forest fire propagation predictions. One of such parameters is the wind. It is possible to measure wind using meteorological stations and it is also possible to predict wind using meteorological models such as WRF. However, wind components are highly affected by the terrain topography introducing a large degree of uncertainty in forest fire spread predictions. Therefore, it is necessary to introduce wind field models that estimate wind speed and direction at very high resolution to reduce such uncertainty. Such models are time consuming models that are usually executed under strict time constrains. So, it is critical to minimize the execution time, taking into account the fact that in many cases it is not possible to execute the model on a supercomputer, but must be executed on commodity hardware available on the field or at control centers. This work introduces a new parallelization approach for wind field calculation based on python multiprocessing to accelerate wind field evaluation. The results show that the new approach reduces execution time using a single personal computer.

关键词： Wind field parallelization Forest fire spread simulation python multiprocessing

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Parallelizing X-ray Photon Correlation Spectroscopy Software Tools using python multiprocessing

Parallelizing X-ray Photon Correlation Spectroscopy Software...

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New York Scientific Data Summit (NYSDS)

作者： Abeykoon, Sameera K. Lin, Meifeng van Dam, Kerstin Kleese Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA

ISBN: (纸本)9781538631614

The third generation synchrotron facilities that are designed to deliver highly intense and bright X-ray beams along with the new area detectors capable of achieving high dynamic ratios and fast frame rates have enabled novel Coherent X-ray scattering experiments. X-ray Photon Correlation Spectroscopy is such a technique that measures nano-and mesoscale dynamics in materials. The scikit-beam python analysis library developed at the National Synchrotron Light Source-II at Brookhaven National Laboratory contains a serial version of Xray Photon Correlation Spectroscopy software tools to perform streaming analysis of structural dynamics of materials, which can be time consuming given the anticipated fast data rates and high image resolutions at the National Synchrotron Light Source-II. Therefore, it is essential to parallelize these data analysis tools to achieve the best performance on the available workstations that contain multi-core processors. In this paper, we report the progress that we have made in using the python multiprocessing module to parallelize the time-correlation functions in scikit-beam. We will compare the results from different multiprocessing approaches, and discuss pros and cons associated with each method.

关键词： python multiprocessing X-ray Photon Correlation Spectroscopy Coherent X-rays

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A Reversible Runtime Environment for Parallel Programs 12th

A Reversible Runtime Environment for Parallel Programs

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12th International Conference on Reversible Computation (RC)

作者： Ikeda, Takashi Yuen, Shoji Graduate School of Informatics Nagoya University Furo-cho Chikusa-ku Nagoya 464-8601 Japan

ISBN: (纸本)9783030524814;9783030524821

We present a reversible runtime environment for simple parallel programs and its experimental implementation. We aim at a lightweight implementation of the backtrack reversibility by the state-saving mechanism using stacks. We translate a program to a sequence of simple commands of an executable intermediate representation for reversible stack machines. The parallel composition is implemented using the multiprocessing feature of python. While executing the commands, the stack machines collect the information for the backward execution in the auxiliary stacks for the update history of the variables and the history of jumps. The commands for the backward execution is obtained by reversing the commands for the forward execution by replacing each command with the corresponding reversed command. In the purpose of behaviour analysis with reversibility such as debugging, our runtime is more portable than the source-to-source translation of a high-level programming language.

关键词： Reversible computation Imparative parallel programs Stack machine python multiprocessing

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Parallel astronomical data processing with python: Recipes for multicore machines

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ASTRONOMY AND COMPUTING 2013年 2卷 1-10页

作者： Singh, Navtej Browne, Lisa-Marie Butler, Ray Natl Univ Ireland Galway Sch Phys Ctr Astron Galway Ireland

High performance computing has been used in various fields of astrophysical research. But most of it is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters. With the advent of multicore processors in the last decade, many serial software codes have been re-implemented in parallel mode to utilize the full potential of these processors. In this paper, we propose parallel processing recipes for multicore machines for astronomical data processing. The target audience is astronomers who use python as their preferred scripting language and who may be using PyRAF/IRAF for data processing. Three problems of varied complexity were benchmarked on three different types of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data processing tasks. The native multiprocessing module available in python makes it a relatively trivial task to implement the parallel code. We have also compared the three multiprocessing approaches-Pool/Map, Process/Queue and Parallel python. Our test codes are freely available and can be downloaded from our website. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Astronomical data processing Parallel computing Multicore programming python multiprocessing Parallel python Deconvolution

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