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jaxdecomp proto (#21)
* adding example of distributed solution * put back old functgion * update formatting * add halo exchange and slice pad * apply formatting * implement distributed optimized cic_paint * Use new cic_paint with halo * Fix seed for distributed normal * Wrap interpolation function to avoid all gather * Return normal order frequencies for single GPU * add example * format * add optimised bench script * times in ms * add lpt2 * update benchmark and add slurm * Visualize only final field * Update scripts/distributed_pm.py Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com> * Adjust pencil type for frequencies * fix painting issue with slabs * Shared operation in fourrier space now take inverted sharding axis for slabs * add assert to make pyright happy * adjust test for hpc-plotter * add PMWD test * bench * format * added github workflow * fix formatting from main * Update for jaxDecomp pure JAX * revert single halo extent change * update for latest jaxDecomp * remove fourrier_space in autoshmap * make normal_field work with single controller * format * make distributed pm work in single controller * merge bench_pm * update to leapfrog * add a strict dependency on jaxdecomp * global mesh no longer needed * kernels.py no longer uses global mesh * quick fix in distributed * pm.py no longer uses global mesh * painting.py no longer uses global mesh * update demo script * quick fix in kernels * quick fix in distributed * update demo * merge hugos LPT2 code * format * Small fix * format * remove duplicate get_ode_fn * update visualizer * update compensate CIC * By default check_rep is false for shard_map * remove experimental distributed code * update PGDCorrection and neural ode to use new fft3d * jaxDecomp pfft3d promotes to complex automatically * remove deprecated stuff * fix painting issue with read_cic * use jnp interp instead of jc interp * delete old slurms * add notebook examples * apply formatting * add distributed zeros * fix code in LPT2 * jit cic_paint * update notebooks * apply formating * get local shape and zeros can be used by users * add a user facing function to create uniform particle grid * use jax interp instead of jax_cosmo * use float64 for enmeshing * Allow applying weights with relative cic paint * Weights can be traced * remove script folder * update example notebooks * delete outdated design file * add readme for tutorials * update readme * fix small error * forgot particles in multi host * clarifying why cic_paint_dx is slower * clarifying the halo size dependence on the box size * ability to choose snapshots number with MultiHost script * Adding animation notebook * Put plotting in package * Add finite difference laplace kernel + powerspec functions from Hugo Co-authored-by: Hugo Simonfroy <hugo.simonfroy@gmail.com> * Put plotting utils in package * By default use absoulute painting with * update code * update notebooks * add tests * Upgrade setup.py to pyproject * Format * format tests * update test dependencies * add test workflow * fix deprecated FftType in jaxpm.kernels * Add aboucaud comments * JAX version is 0.4.35 until Diffrax new release * add numpy explicitly as dependency for tests * fix install order for tests * add numpy to be installed * enforce no build isolation for fastpm * pip install jaxpm test without build isolation * bump jaxdecomp version * revert test workflow * remove outdated tests --------- Co-authored-by: EiffL <fr.eiffel@gmail.com> Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com> Co-authored-by: Wassim KABALAN <wassim@apc.in2p3.fr> Co-authored-by: Hugo Simonfroy <hugo.simonfroy@gmail.com> Former-commit-id: 8c2e823d4669eac712089bf7f85ffb7912e8232d
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<!-- ALL-CONTRIBUTORS-BADGE:END -->
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JAX-powered Cosmological Particle-Mesh N-body Solver
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**This project is currently in an early design phase. All inputs are welcome on the [design document](https://github.com/DifferentiableUniverseInitiative/JaxPM/blob/main/design.md)**
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## Goals
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Provide a modern infrastructure to support differentiable PM N-body simulations using JAX:
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- Keep implementation simple and readable, in pure NumPy API
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- Transparent distribution using builtin `xmap`
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- Any order forward and backward automatic differentiation
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- Support automated batching using `vmap`
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- Compatibility with external optimizer libraries like `optax`
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- Now fully distributable on **multi-GPU and multi-node** systems using [jaxDecomp](https://github.com/DifferentiableUniverseInitiative/jaxDecomp) working with`JAX v0.4.35`
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## Open development and use
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- Everyone is welcome to contribute, and can join the JOSS publication (until it is submitted to the journal).
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- Anyone (including main contributors) can use this code as a framework to build and publish their own applications, with no expectation that they *need* to extend authorship to all jaxpm developers.
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## Getting Started
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To dive into JaxPM’s capabilities, please explore the **notebook section** for detailed tutorials and examples on various setups, from single-device simulations to multi-host configurations. You can find the notebooks' [README here](notebooks/README.md) for a structured guide through each tutorial.
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## Contributors ✨
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