Application Skeleton
Application Skeleton: Generating Synthetic Applications for Infrastructure Research - Published in JOSS (2016)
Science Score: 100.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 12 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
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4 of 10 committers (40.0%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
a tool to generate skeleton applications that mimic a real applications' parallel or distributed performance at a task level
Basic Info
Statistics
- Stars: 13
- Watchers: 8
- Forks: 2
- Open Issues: 6
- Releases: 2
Metadata Files
README.md
Skeleton
Application Skeleton is a simple and powerful tool to generate skeleton science and engineering applications (for example, modeling and simulation, data analysis) --- easy-to-program, easy-to-run applications --- that closely mimic a real applications' parallel or distributed performance at a task (but not process) level.
It is intended for applied computer scientists who need to use science and engineering applications to verify the effectiveness of new systems designed to efficiently run such applications, so that they can bypass obstacles that they often encounter when accessing and building real science and engineering applications. Using the applications generated by Application Skeleton guarantees that the CS systems' effectiveness on synthetic applications will apply to the real applications.
Application classes that can be represented include: bag of tasks, map reduce, multi-stage workflow, and variations of these with a fixed number of iterations. These applications can be generally considered many-task applications.
Applications are described as one or more stages.
Stages are described as one more more tasks. Stages can also be iterative.
Tasks can be serial or parallel, and have compute and I/O (read and write) elements.
Once created, the applications can be run on single-core, single-node, multi-core, or multi-node (distributed or parallel) computers, depending on what workflow system is used to run them. The generated applications are compatible with workflow system such as Swift and Pegasus, as well as the ubiquitous UNIX shell. The application can also be created as a generic JSON object that can be used by other systems such as the AIMES middleware.
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Documentation about Skeletons can be found in the report directory
If you have questions, need support, want to report a bug, or want to request a feature, please do so by opening a new issue -- https://github.com/applicationskeleton/Skeleton/issues/new
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Contributors are welcome!
Ideally, these can be made via pull requests to the repository.
To receive email about commits to this repository, join the Google Groups "skeleton-commits" group, http://groups.google.com/group/skeleton-commits/subscribe
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A paper about the first version of Application Skeletons is: Z. Zhang and D. S. Katz, "Application Skeletons: Encapsulating MTC Application Task Computation and I/O," Proceedings of 6th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS), (in conjunction with SC13), 2013. https://doi.org/10.1145/2503210.2503222
A paper about the current version is: Z. Zhang and D. S. Katz, "Using Application Skeletons to Improve eScience Infrastructure," Proceedings of 10th IEEE International Conference on eScience, 2014. https://doi.org/10.1109/eScience.2014.9 (paper). http://www.slideshare.net/danielskatz/using-application-skeletons-to-improve-escience-infrastructure (slides).
A longer paper about Application Skeletons and Synapse work is: D. S. Katz, A. Merzky, Z. Zhang, S. Jha, "Application Skeletons: Construction and Use in eScience," Future Generation Computing Systems, v.59. pp 114-124, 2016. https://doi.org/10.1016/j.future.2015.10.001
Owner
- Name: applicationskeleton
- Login: applicationskeleton
- Kind: organization
- Repositories: 1
- Profile: https://github.com/applicationskeleton
JOSS Publication
Application Skeleton: Generating Synthetic Applications for Infrastructure Research
Authors
Tags
computational science data science application modeling system modeling performance modeling parallel and distributed systemsCitation (CITATION)
Daniel S. Katz, Andre Merzky, Matteo Turilli, Michael Wilde. Zhao Zhang. (2015). Application Skeleton 1.2. ZENODO. DOI TBD. Additional citation format available from Zenodo: URL TBD.
GitHub Events
Total
Last Year
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Daniel S. Katz | d****k@c****u | 68 |
| Andre Merzky | a****e@m****t | 57 |
| Daniel S. Katz | d****z@i****g | 37 |
| Zhao Zhang | z****8@g****m | 25 |
| Shantenu Jha | s****a@r****u | 6 |
| Yadu Nand Babuji | y****9@g****m | 3 |
| Matteo Turilli | m****i@g****m | 2 |
| I'm Whedon | w****n | 1 |
| Michael Wilde | w****e@w****l | 1 |
| Michael Wilde | w****e@n****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 13
- Total pull requests: 5
- Average time to close issues: 8 days
- Average time to close pull requests: 7 days
- Total issue authors: 4
- Total pull request authors: 3
- Average comments per issue: 0.85
- Average comments per pull request: 1.8
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- danielskatz (9)
- zhaozhang (2)
- mturilli (1)
- mjwilde (1)
Pull Request Authors
- andre-merzky (3)
- whedon (1)
- danielskatz (1)
