Science Score: 72.0%
This score indicates how likely this project is to be science-related based on various indicators:
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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
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✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
1 of 1 committers (100.0%) from academic institutions -
✓Institutional organization owner
Organization lbl-eesa has institutional domain (eesa.lbl.gov) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.2%) to scientific vocabulary
Repository
Heterogeneous Accelerator Memory Resource
Basic Info
- Host: GitHub
- Owner: LBL-EESA
- License: other
- Language: C++
- Default Branch: master
- Size: 363 KB
Statistics
- Stars: 14
- Watchers: 2
- Forks: 1
- Open Issues: 10
- Releases: 2
Metadata Files
README.md
HAMR
HAMR is a library defining an accelerator technology agnostic memory model that bridges between accelerator technologies (CUDA, HIP, ROCm, OpenMP, Kokos, etc) and traditional CPUs in heterogeneous computing environments. HAMR is light weight and implemented in modern C++. HAMR includes Python integration that enables zero-copy data transfer between C++ and Python technogies such as Numba and Cupy.
Citing
If you've used HAMR in your application please cite us.
Source Code
The source code can be obtained at the HAMR github repository.
Documentation
The HAMR User's Guide documents compiling and use of HAMR and contains simple examples.
The HAMR Doxygen site documents the APIs. Most users will want to start with the hamr::buffer, a container that has capabilities similar to std::vector and can provide access to data in different accelerator execution environments.
Regression Testing and CI
License
HAMR's license is a BSD license with an ADDED paragraph at the end that makes it easy for us to accept improvements. See license for more information.
Copyright Notice
HAMR - Heterogeneous Accelerator Memory Resource (HAMR) Copyright (c) 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
Owner
- Name: LBL-EESA
- Login: LBL-EESA
- Kind: organization
- Location: Berkeley, CA
- Website: http://eesa.lbl.gov/
- Repositories: 11
- Profile: https://github.com/LBL-EESA
Lawrence Berkeley National Laboratory's Earth and Environmental Sciences Area
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Loring
given-names: Burlen
orcid: https://orcid.org/0000-0002-4678-8142
title: "HAMR the Heterogeneous Accelerator Memory Resource"
version: 1.0.0
doi: https://zenodo.org/record/6471012
date-released: 2022-04-19
GitHub Events
Total
- Watch event: 4
Last Year
- Watch event: 4
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Burlen Loring | b****g@l****v | 215 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 2 years ago
All Time
- Total issues: 9
- Total pull requests: 63
- Average time to close issues: 4 days
- Average time to close pull requests: 2 days
- Total issue authors: 2
- Total pull request authors: 1
- Average comments per issue: 0.44
- Average comments per pull request: 0.06
- Merged pull requests: 58
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 4
- Pull requests: 25
- Average time to close issues: N/A
- Average time to close pull requests: 1 day
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.08
- Merged pull requests: 21
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- burlen (8)
- YibaiMeng (1)
Pull Request Authors
- burlen (63)