Big data analytics and the LHC
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Abstract
The Large Hadron Collider is one of the largest and most complicated pieces of scientific apparatus ever constructed. The detectors along the LHC ring see as many as 800 million proton-proton collisions per second. An event in 10 to the 11th power is new physics and there is a hierarchical series of steps to extract a tiny signal from an enormous background. High energy physics (HEP) has long been a driver in managing and processing enormous scientific datasets and the largest scale high throughput computing centers. HEP developed one of the first scientific computing grids that now regularly operates 500k processor cores and half of an exabyte of disk storage located on 5 continents including hundred of connected facilities. In this presentation I will discuss the techniques used to extract scientific discovery from a large and complicated dataset. While HEP has developed many tools and techniques for handling big datasets, there is an increasing desire within the field to make more effective use of additional industry developments. I will discuss some of the ongoing work to adopt industry techniques in big data analytics to improve the discovery potential of the LHC and the effectiveness of the scientists who work on it.
Information & Contributors
Information
Published In
May 2016
487 pages
ISBN:9781450341288
DOI:10.1145/2903150
- General Chairs:
- Gianluca Palermo,
- John Feo,
- Program Chairs:
- Antonino Tumeo,
- Hubertus Franke
Copyright © 2016 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- Micron Foundation: Micron Technology Foundation, Inc.
- ACM: Association for Computing Machinery
- Politecnico di Milano: Politecnico di Milano
- SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
- IBM: IBM
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2016
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- Invited-talk
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Acceptance Rates
CF '16 Paper Acceptance Rate 30 of 94 submissions, 32%;
Overall Acceptance Rate 273 of 785 submissions, 35%
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