A new approach to distributed computing in high energy physics

1992 ◽  
Author(s):  
Paul Avery ◽  
Chandra Chegireddy ◽  
John Brothers ◽  
Theodore Johnson ◽  
Aric Zion
Keyword(s):  
Distributed Computing ◽  
High Energy Physics ◽  
High Energy ◽  
New Approach ◽  
Energy Physics

scholarly journals Computing in High Energy Physics

2005 ◽  
Vol 20 (14) ◽  
pp. 3021-3032
Author(s):  
Ian M. Fisk
Keyword(s):  
Distributed Computing ◽  
Large Scale ◽  
High Energy Physics ◽  
High Energy ◽  
Next Generation ◽  
Physical Infrastructure ◽  
Commodity Computing ◽  
Physics Experiments ◽  
Insight Into ◽  
Energy Physics

In this review, the computing challenges facing the current and next generation of high energy physics experiments will be discussed. High energy physics computing represents an interesting infrastructure challenge as the use of large-scale commodity computing clusters has increased. The causes and ramifications of these infrastructure challenges will be outlined. Increasing requirements, limited physical infrastructure at computing facilities, and limited budgets have driven many experiments to deploy distributed computing solutions to meet the growing computing needs for analysis reconstruction, and simulation. The current generation of experiments have developed and integrated a number of solutions to facilitate distributed computing. The current work of the running experiments gives an insight into the challenges that will be faced by the next generation of experiments and the infrastructure that will be needed.

scholarly journals Coffea Columnar Object Framework For Effective Analysis

2020 ◽  
Vol 245 ◽  
pp. 06012
Author(s):  
Nicholas Smith ◽  
Lindsey Gray ◽  
Matteo Cremonesi ◽  
Bo Jayatilaka ◽  
Oliver Gutsche ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Data Delivery ◽  
Future Directions ◽  
New Approach ◽  
Big Data Technologies ◽  
Python Programming ◽  
Python Package ◽  
Effective Analysis ◽  
Energy Physics

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

Development of Silicon Sensor Characterization System for Future High Energy Physics Experiments

2015 ◽  
Vol 3 (1) ◽  
pp. 41-45
Author(s):  
Preeti Kumari ◽  
◽  
Kavita Lalwani ◽  
Ranjit Dalal ◽  
Ashutosh Bhardwaj ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Physics Experiments ◽  
Silicon Sensor ◽  
Energy Physics
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