Abstract
High-energy physics studies collisions of particles traveling near the speed of light. For statistically significant results, physicists need to analyze a huge number of such events. One analysis job can take days and process tens of millions of collisions. Today the experiments of the large hadron collider (LHC) create 10 GB of data per second and a future upgrade will cause a ten-fold increase in data. The data analysis requires not only massive hardware but also a lot of electricity. In this article, we discuss energy efficiency in scientific computing and review a set of intermixed approaches we have developed in our Green Big Data project to improve energy efficiency of CERN computing. These approaches include making energy consumption visible to developers and users, architectural improvements, smarter management of computing jobs, and benefits of cloud technologies. The open and innovative environment at CERN is an excellent playground for different energy efficiency ideas which can later find use in mainstream computing.
| Original language | English |
|---|---|
| Pages (from-to) | 103-113 |
| Number of pages | 11 |
| Journal | Future Generation Computer Systems |
| Volume | 81 |
| Early online date | 2017 |
| DOIs | |
| Publication status | Published - 1 Apr 2018 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- CERN
- Energy efficiency
- Green computing
- Scientific computing
