performance and cost analysis of the supernova factory on the amazon aws cloud
Clicks: 107
ID: 189125
2011
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
107 views
22 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #85 of 121 articles by views in environmental pollution
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 121 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Today, our picture of the Universe radically differs from that of just over a decade ago. We now know that the Universe is not only expanding as Hubble discovered in 1929, but that the rate of expansion is accelerating, propelled by mysterious new physics dubbed “Dark Energy”. This revolutionary discovery was made by comparing the brightness of nearby Type Ia supernovae (which exploded in the past billion years) to that of much more distant ones (from up to seven billion years ago). The reliability of this comparison hinges upon a very detailed understanding of the physics of the nearby events. To further this understanding, the Nearby Supernova Factory (SNfactory) relies upon a complex pipeline of serial processes that execute various image processing algorithms in parallel on ~10 TBs of data. This pipeline traditionally runs on a local cluster. Cloud computing [Above the clouds: a Berkeley view of cloud computing, Technical Report UCB/EECS-2009-28, University of California, 2009] offers many features that make it an attractive alternative. The ability to completely control the software environment in a cloud is appealing when dealing with a community developed science pipeline with many unique library and platform requirements. In this context we study the feasibility of porting the SNfactory pipeline to the Amazon Web Services environment. Specifically we: describe the tool set we developed to manage a virtual cluster on Amazon EC2, explore the various design options available for application data placement, and offer detailed performance results and lessons learned from each of the above design options.
| Reference Key |
jackson2011scientificperformance
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Keith R. Jackson;Krishna Muriki;Lavanya Ramakrishnan;Karl J. Runge;Rollin C. Thomas |
| Journal | environmental pollution |
| Year | 2011 |
| DOI |
10.3233/SPR-2011-0324
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.