Error Resilient Secure Multi-gigabit Optical Link Design for High Energy Physics Experiment

2016 ◽  
Author(s):  
Jubin Mitra ◽  
Shuaib Ahmad Khan ◽  
Rourab Paul ◽  
Sanjoy Mukherjee ◽  
Amlan Chakrabarti ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Error Resilient ◽  
Optical Link ◽  
Physics Experiment ◽  
Energy Physics

An FPGA-Based High-Speed Error Resilient Data Aggregation and Control for High Energy Physics Experiment

2017 ◽  
Vol 64 (3) ◽  
pp. 933-944 ◽  
Author(s):  
Swagata Mandal ◽  
Jogender Saini ◽  
Wojciech M. Zabolotny ◽  
Suman Sau ◽  
Amlan Chakrabarti ◽  
...  
Keyword(s):  
Data Aggregation ◽  
High Speed ◽  
High Energy Physics ◽  
High Energy ◽  
Error Resilient ◽  
Speed Error ◽  
Physics Experiment ◽  
And Control ◽  
Energy Physics

scholarly journals Evaluation of emerging parallel optical link technology for high energy physics

2012 ◽  
Vol 7 (01) ◽  
pp. C01007-C01007 ◽  
Author(s):  
J Chramowicz ◽  
S Kwan ◽  
A Prosser ◽  
M Winchell
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Optical Link ◽  
Energy Physics

scholarly journals Analysis of Requirements for the Design of a Detector Control System in a High Energy Physics Experiment

2019 ◽  
Author(s):  
Juan Carlos Cabanillas Noris ◽  
Ildefonso León Monzón ◽  
Mario Iván Martínez Hernández ◽  
Solangel Rojas Torres
Keyword(s):  
Control System ◽  
High Energy Physics ◽  
High Energy ◽  
Physics Experiment ◽  
Energy Physics ◽  
Detector Control System

scholarly journals General-purpose parallel computing in a high-energy physics experiment at CERN

1996 ◽  
pp. 251-257 ◽  
Author(s):  
J. Apostolakis ◽  
L. M. Bertolotto ◽  
C. E. Bruschini ◽  
P. Calafiura ◽  
F. Gagliardi ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Energy Physics ◽  
High Energy ◽  
General Purpose ◽  
Physics Experiment ◽  
Energy Physics

scholarly journals The Event Buffer Management for MT-SNiPER

2019 ◽  
Vol 214 ◽  
pp. 05026
Author(s):  
Jiaheng Zou ◽  
Tao Lin ◽  
Weidong Li ◽  
Xingtao Huang ◽  
Ziyan Deng ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
High Energy Physics ◽  
Time Window ◽  
Buffer Management ◽  
High Energy ◽  
General Purpose ◽  
Software Framework ◽  
Neutrino Experiments ◽  
Physics Experiment ◽  
Energy Physics

SNiPER is a general purpose offline software framework for high energy physics experiment. It provides some features that are attractive to neutrino experiments, such as the event buffer. More than one events are available in the buffer according to a customizable time window, so that it is easy for users to apply events correlation analysis. We also implemented the MT-SNiPER to support multithreading computing based on Intel TBB. In MT-SNiPER, the event loop is split into pieces, and each piece is dispatched to a task. The global buffer, an extension and enhancement to the event buffer, is implemented for MT-SNiPER. The global buffer is available by all threads. It keeps all the events being processed in memory. When there is an available task, a subset of its events is dispatched to that task. There can be overlaps between the subsets in different tasks due to the time window. However, it is ensured that each event is processed only once. In the task side, the subsets of events are locally managed by a normal event buffer. So the global buffer can be transparent to most user algorithms. Within the global buffer, the multithreading computing of MT-SNiPER becomes more practicable.

scholarly journals The Data Ocean Project

2019 ◽  
Vol 214 ◽  
pp. 04020 ◽  
Author(s):  
Martin Barisits ◽  
Fernando Barreiro ◽  
Thomas Beermann ◽  
Karan Bhatia ◽  
Kaushik De ◽  
...  
Keyword(s):  
High Energy Physics ◽  
Workflow Management ◽  
High Energy ◽  
Data Management System ◽  
Cloud Platform ◽  
Scientific Experiments ◽  
Physics Experiment ◽  
Future Work ◽  
Work Done ◽  
Energy Physics

Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

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