DiffEqGPU.jl
SciMLSensitivity.jl
| DiffEqGPU.jl | SciMLSensitivity.jl | |
|---|---|---|
| 2 | 2 | |
| 296 | 348 | |
| 2.0% | 2.9% | |
| 6.7 | 9.5 | |
| 2 days ago | 4 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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DiffEqGPU.jl
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2023 was the year that GPUs stood still
Indeed, and this year we created a system for compiling ODE code not just optimized CUDA kernels but also OneAPI kernels, AMD GPU kernels, and Metal. Peer reviewed version is here (https://www.sciencedirect.com/science/article/abs/pii/S00457...), open access is here (https://arxiv.org/abs/2304.06835), and the open source code is at https://github.com/SciML/DiffEqGPU.jl. The key that the paper describes is that in this case kernel generation is about 20x-100x faster than PyTorch and Jax (see the Jax compilation in multiple ways in this notebook https://colab.research.google.com/drive/1d7G-O5JX31lHbg7jTzz..., extra overhead though from calling Julia from Python but still shows a 10x).
The point really is that while deep learning libraries are amazing, at the end of the day they are DSL and really pull towards one specific way of computing and parallelization. It turns out that way of parallelizing is good for deep learning, but not for all things you may want to accelerate. Sometimes (i.e. cases that aren't dominated by large linear algebra) building problem-specific kernels is a major win, and it's over-extrapolating to see ML frameworks do well with GPUs and think that's the only thing that's required. There are many ways to parallelize a code, ML libraries hardcode a very specific way, and it's good for what they are used for but not every problem that can arise.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
Link to GitHub repo from the abstract: https://github.com/SciML/DiffEqGPU.jl
SciMLSensitivity.jl
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Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation - Chris Rackauckas (ASA Statistical Computing & Graphics Sections)
Plenty of code examples! https://sensitivity.sciml.ai/dev is the main resource, but most of the papers mentioned have their own code repositories. I'm trying to get most of them updated and into the larger SciMLSensitivity docs so they are all tested, though we need new hosting computers to actually get that done.
- “Why I still recommend Julia”
What are some alternatives?
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
SciMLStyle - A style guide for stylish Julia developers
GPUODEBenchmarks - Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers
StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]