devito
DSL and compiler framework for automated finite-differences and stencil computation (by devitocodes)
mpi4jax
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap: (by PhilipVinc)
| devito | mpi4jax | |
|---|---|---|
| 2 | 1 | |
| 617 | 491 | |
| 0.8% | 0.6% | |
| 9.9 | 6.0 | |
| 8 days ago | 12 days ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
devito
Posts with mentions or reviews of devito.
We have used some of these posts to build our list of alternatives
and similar projects.
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Galerkin Approximation
Another project that works like this is devito https://www.devitoproject.org/ - the python code generates C code, calls gcc to compile it, dynamically links the object code with dlopen(), then calls the compiled code. That way, the hot code loop doesn't run in Python
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Where should I look to learn about how to apply my math skills to options?
This may help https://github.com/devitocodes/devito/blob/master/examples/finance/bs_ivbp.ipynb
mpi4jax
Posts with mentions or reviews of mpi4jax.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-03.
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[D] Jax (or other libraries) when not using GPUs/TPUs but CPUs.
I've seen a couple of posts of folks using JAX for scientific computing (e.g. physics) workloads without much issue. The parallel primitives work just as well across multiple CPUs as they do on accelerators. If you're on a cluster, also worth looking into https://github.com/PhilipVinc/mpi4jax.
What are some alternatives?
When comparing devito and mpi4jax you can also consider the following projects:
AMaDiA - Astus' Mathematical Display Application : A GUI for Mathematics (Calculator, LaTeX Converter, Plotter, ... )
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
chocopy-python-compiler - Ahead-of-time compiler for Chocopy, a statically typed subset of Python 3, built in Python 3, targeting CIL/CLR, JVM, LLVM IR, and WASM.
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
qiskit-symb - Symbolic evaluation of parameterized quantum circuits in Qiskit
extending-jax - Extending JAX with custom C++ and CUDA code