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Adds a package for mallocMC, a memory allocator for many core architectures. This project provides a framework for fast memory managers on many core accelerators. Currently, it supports NVIDIA GPUs of compute capability sm_20 or higher through the ScatterAlloc algorithm. mallocMC is header-only. Downstream Dependencies PIConGPU and other projects of HZDR's *Computational Radiation Physics* group References - Markus Steinberger, Michael Kenzel, Bernhard Kainz and Dieter Schmalstieg. *ScatterAlloc: Massively parallel dynamic memory allocation for the GPU*, Innovative Parallel Computing (InPar), 2012, https://doi.org/10.1109/InPar.2012.6339604 - Carlchristian Eckert. *Enhancements of the massively parallel memory allocator ScatterAlloc and its adaption to the general interface mallocMC*, Junior Thesis (grosser Beleg), 2014, http://dx.doi.org/10.5281/zenodo.34461 |
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| lib/spack | ||
| share/spack | ||
| var/spack | ||
| .codecov.yml | ||
| .coveragerc | ||
| .flake8 | ||
| .gitignore | ||
| .mailmap | ||
| .travis.yml | ||
| LICENSE | ||
| pytest.ini | ||
| README.md | ||
Spack is a package management tool designed to support multiple versions and configurations of software on a wide variety of platforms and environments. It was designed for large supercomputing centers, where many users and application teams share common installations of software on clusters with exotic architectures, using libraries that do not have a standard ABI. Spack is non-destructive: installing a new version does not break existing installations, so many configurations can coexist on the same system.
Most importantly, Spack is simple. It offers a simple spec syntax so that users can specify versions and configuration options concisely. Spack is also simple for package authors: package files are written in pure Python, and specs allow package authors to write a single build script for many different builds of the same package.
See the Feature Overview for examples and highlights.
To install spack and install your first package, make sure you have Python (2 or 3). Then:
$ git clone https://github.com/llnl/spack.git
$ cd spack/bin
$ ./spack install libelf
Documentation
Full documentation for Spack is the first place to look.
We've also got a Spack 101 Tutorial, so you can learn Spack yourself, or teach users at your own site.
See also:
- Technical paper and slides on Spack's design and implementation.
- Short presentation from the Getting Scientific Software Installed BOF session at Supercomputing 2015.
Get Involved!
Spack is an open source project. Questions, discussion, and contributions are welcome. Contributions can be anything from new packages to bugfixes, or even new core features.
Mailing list
If you are interested in contributing to spack, the first step is to join the mailing list. We're using a Google Group for this, and you can join it here:
Contributions
Contributing to Spack is relatively easy. Just send us a
pull request.
When you send your request, make develop the destination branch on the
Spack repository.
Your PR must pass Spack's unit tests and documentation tests, and must be PEP 8 compliant. We enforce these guidelines with Travis CI. To run these tests locally, and for helpful tips on git, see our Contribution Guide.
Spack uses a rough approximation of the Git
Flow
branching model. The develop branch contains the latest
contributions, and master is always tagged and points to the
latest stable release.
Authors
Many thanks go to Spack's contributors.
Spack was originally written by Todd Gamblin, tgamblin@llnl.gov.
Citing Spack
If you are referencing Spack in a publication, please cite the following paper:
- Todd Gamblin, Matthew P. LeGendre, Michael R. Collette, Gregory L. Lee, Adam Moody, Bronis R. de Supinski, and W. Scott Futral. The Spack Package Manager: Bringing Order to HPC Software Chaos. In Supercomputing 2015 (SC’15), Austin, Texas, November 15-20 2015. LLNL-CONF-669890.
Release
Spack is released under an LGPL license. For more details see the LICENSE file.
LLNL-CODE-647188