scholarly journals Editorial: Heterogeneous Computing for AI and Big Data in High Energy Physics

2021 ◽  
Vol 4 ◽  
Author(s):  
Daniele D’Agostino ◽  
Daniele Cesini
Keyword(s):  
Big Data ◽  
Heterogeneous Computing ◽  
High Energy Physics ◽  
High Energy ◽  
Energy Physics

Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case

2019 ◽  
pp. 241-255
Author(s):  
Valentina Avati ◽  
Milosz Blaszkiewicz ◽  
Enrico Bocchi ◽  
Luca Canali ◽  
Diogo Castro ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Energy Physics ◽  
High Energy ◽  
Big Data Analysis ◽  
Use Case ◽  
Energy Physics

scholarly journals High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Numerical Methods ◽  
High Performance ◽  
Data Science ◽  
Experimental Validation ◽  
High Energy Physics ◽  
High Energy ◽  
Performance Computing ◽  
Energy Physics

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.

scholarly journals Editorial: Innovative Analysis Ecosystems for HEP Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Sezen Sekmen ◽  
Gian Michele Innocenti ◽  
Bo Jayatilaka
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
High Energy Physics ◽  
High Energy ◽  
Research Topic ◽  
Energy Physics

This editorial summarizes the contributions to the Frontiers Research topic “Innovative Analysis Ecosystems for HEP Data”, established under the Big Data and AI in High Energy Physics section and appearing under the Frontiers in Big Data and Frontiers in Artificial Intelligence journals.

scholarly journals CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics

2020 ◽  
Vol 3 ◽  
Author(s):  
Marco Rovere ◽  
Ziheng Chen ◽  
Antonio Di Pilato ◽  
Felice Pantaleo ◽  
Chris Seez
Keyword(s):  
Heterogeneous Computing ◽  
Clustering Algorithm ◽  
High Energy Physics ◽  
High Energy ◽  
Spatial Index ◽  
Phase 2 ◽  
Number Of Clusters ◽  
Density Based Clustering ◽  
Parallel Clustering ◽  
Energy Physics

One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high-energy physics.

scholarly journals The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics

2018 ◽  
Vol 5 (1) ◽  
pp. 205395171876883 ◽  
Author(s):  
Andrew Bartlett ◽  
Jamie Lewis ◽  
Luis Reyes-Galindo ◽  
Neil Stephens
Keyword(s):  
Big Data ◽  
High Energy Physics ◽  
High Energy ◽  
Computational Social Science ◽  
Academic Disciplines ◽  
Theoretical Concepts ◽  
General Terms ◽  
The Social ◽  
Knowledge Claims ◽  
Energy Physics

This paper argues that analyses of the ways in which Big Data has been enacted in other academic disciplines can provide us with concepts that will help understand the application of Big Data to social questions. We use examples drawn from our Science and Technology Studies (STS) analyses of -omic biology and high energy physics to demonstrate the utility of three theoretical concepts: (i) primary and secondary inscriptions, (ii) crafted and found data, and (iii) the locus of legitimate interpretation. These help us to show how the histories, organisational forms, and power dynamics of a field lead to different enactments of big data. The paper suggests that these concepts can be used to help us to understand the ways in which Big Data is being enacted in the domain of the social sciences, and to outline in general terms the ways in which this enactment might be different to that which we have observed in the ‘hard’ sciences. We contend that the locus of legitimate interpretation of Big Data biology and physics is tightly delineated, found within the disciplinary institutions and cultures of these disciplines. We suggest that when using Big Data to make knowledge claims about ‘the social’ the locus of legitimate interpretation is more diffuse, with knowledge claims that are treated as being credible made from other disciplines, or even by those outside academia entirely.

scholarly journals Machine Learning as a Service for High Energy Physics on heterogeneous computing resources

2021 ◽  
Author(s):  
Luca Giommi ◽  
Valentin Kuznetsov ◽  
Daniele Bonacorsi ◽  
Daniele Spiga
Keyword(s):  
Machine Learning ◽  
Heterogeneous Computing ◽  
High Energy Physics ◽  
High Energy ◽  
Energy Physics
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