Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computational Engineering, Finance, and Science

arXiv:1606.00548 (cs)
[Submitted on 2 Jun 2016]

Title:Large-scale Reservoir Simulations on IBM Blue Gene/Q

Authors:Hui Liu, Kun Wang, Zhangxin Chen
View PDF
Abstract:This paper presents our work on simulation of large-scale reservoir models on IBM Blue Gene/Q and studying the scalability of our parallel reservoir simulators. An in-house black oil simulator has been implemented. It uses MPI for communication and is capable of simulating reservoir models with hundreds of millions of grid cells. Benchmarks show that our parallel simulator are thousands of times faster than sequential simulators that designed for workstations and personal computers, and the simulator has excellent scalability.
Subjects: Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:1606.00548 [cs.CE]
  (or arXiv:1606.00548v1 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.1606.00548

Submission history

From: Hui Liu Mr [view email]
[v1] Thu, 2 Jun 2016 06:06:28 UTC (1,174 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.CE
< prev   |   next >
new | recent | 2016-06
Change to browse by:
cs
cs.DC

References & Citations

DBLP - CS Bibliography

listing | bibtex
Hui Liu
Kun Wang
Zhangxin Chen
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)