文章基本信息

标题：High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science
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作者：Florin Pop
期刊名称：Advances in High Energy Physics
印刷版ISSN：1687-7357
电子版ISSN：1687-7365
出版年度：2014
卷号：2014
DOI：10.1155/2014/507690
出版社：Hindawi Publishing Corporation
摘要：Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.