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https://github.com/sandialabs/tenzing
Core library for optimizing CUDA+MPI programs as sequential decision problems.
https://github.com/sandialabs/tenzing
cuda mpi scr-2759 sequential-decision-problem
Last synced: about 1 month ago
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Core library for optimizing CUDA+MPI programs as sequential decision problems.
- Host: GitHub
- URL: https://github.com/sandialabs/tenzing
- Owner: sandialabs
- Created: 2022-02-24T21:18:21.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2022-03-04T13:50:23.000Z (almost 3 years ago)
- Last Synced: 2024-11-12T15:16:01.407Z (3 months ago)
- Topics: cuda, mpi, scr-2759, sequential-decision-problem
- Language: C++
- Homepage:
- Size: 1.59 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING
Awesome Lists containing this project
README
# tenzing-core
The core library of the Tenzing project.
tenzing-core provides facilities for interacting with CUDA + MPI programs as sequential decision problems.
This facilitates optimizing CUDA + MPI programs using sequential decision strategies.
Two solvers are available
* [tenzing-mcts](https://github.com/sandialabs/tenzing-mcts): Uses Monte-Carlo tree search
* [tenzing-dfs](https://github.com/sandialabs/tenzing-dfs): Uses depth-first search
[](https://github.com/sandialabs/tenzing-core/actions/workflows/ubuntu2004_cuda116_openmpi.yml)
## Build
On a supported platform:
```bash
source load-env.sh
```
In any case:
```bash
mkdir build && cd build
cmake .. -DCMAKE_CUDA_ARCHITECTURES=70
make
```
## Tests
Tests are split into two locations:
* unit tests may be defined in source files
* tests with a more "itegration" flavor are in `test/`
To run tests, you can do
* `make test`
* `ctest`
* `tenzing-all`
* `-ltc`: list tests cases
* `-tc="a,b"`: only run test cases named `a` and `b`
This creates some CMake complexity, as the test functions present in static libraries will not be linked into the resulting test binary.
Therefore, we use a CMake object library to generate the test binary, and then generate a static library from the object library.
object library properties do not get propagated properly / at all, so we have to redefine what needs to be linked and included, etc
tenzing-core has been tested on the following platforms:
* NERSC perlmutter: g++ 10.3 / nvcc 11.4 / Cray MPICH 8.1.13
* Sandia vortex (similar to ORNL Lassen and OLCF Summit): g++ 7.5.0 / nvcc 10.1 / IBM Spectrum MPI
* Sandia ascicgpu
## Documentation
* Visit the API documentation in [docs/api.md](docs/api.md)
* `ascicgpu` system documentation in [docs/ascicgpu.md](docs/ascicgpu.md)
* `vortex` system documentation in [docs/vortex.md](docs/vortex.md)
* `perlmutter` ssytem documentation in [docs/perlmutter.md](docs/perlmutter.md)
## Roadmap
- [ ] python bindings (with pybind11)
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.
## Design Issues
- [ ] enable / disable CUDA / MPI
- [ ] isolate Ser/Des
- [ ] isolate platform assignments
- [ ] a `BoundOp` cannot produce the `std::shared_ptr` of it's unbound self, only `OpBase`
- can't ask an `std::shared_ptr` for `std::shared_ptr`
- maybe std::shared_from_this?
- [ ] special status of `Start` and `End` is a bit clumsy.
- maybe there should be a `StartEnd : BoundOp` that they both are instead of separate classes
- in the algs they're probably treated the same (always synced, etc)
- [ ] `Platform` is a clumsy abstraction, since it also tracks resources that are only valid for a single order
- e.g., each order requires a certain number of events, which can be resued for the next order
## Copyright and License
Please see [NOTICE.md](https://github.com/sandialabs/tenzing-core/blob/master/NOTICE.md) for copyright and license information.