forked from Aquila-Consortium/JaxPM_highres
40 lines
2.3 KiB
Markdown
40 lines
2.3 KiB
Markdown
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# Particle Mesh Simulation with JAXPM on Multi-GPU and Multi-Host Systems
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This collection of notebooks demonstrates how to perform Particle Mesh (PM) simulations using **JAXPM**, leveraging JAX for efficient computation on multi-GPU and multi-host systems. Each notebook progressively covers different setups, from single-GPU simulations to advanced, distributed, multi-host simulations across multiple nodes.
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## Table of Contents
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1. **[Single-GPU Particle Mesh Simulation](01-Introduction.ipynb)**
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- Introduction to basic PM simulations on a single GPU.
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- Uses JAXPM to run simulations with absolute particle positions and Cloud-in-Cell (CIC) painting.
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2. **[Advanced Particle Mesh Simulation on a Single GPU](02-Advanced_usage.ipynb)**
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- Explore using diffrax solvers in the ODE step.
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- Explores second order Lagrangian Perturbation Theory (LPT) simulations.
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- Introduces weighted density field projections
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3. **[Multi-GPU Particle Mesh Simulation with Halo Exchange](03-MultiGPU_PM_Halo.ipynb)**
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- Extends PM simulation to multi-GPU setups with halo exchange.
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- Uses sharding and device mesh configurations to manage distributed data across GPUs.
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4. **[Multi-GPU Particle Mesh Simulation with Advanced Solvers](04-MultiGPU_PM_Solvers.ipynb)**
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- Compares different ODE solvers (Leapfrog and Dopri5) in multi-GPU simulations.
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- Highlights performance, memory considerations, and solver impact on simulation quality.
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5. **[Multi-Host Particle Mesh Simulation](05-MultiHost_PM.ipynb)**
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- Extends PM simulations to multi-host, multi-GPU setups for large-scale simulations.
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- Guides through job submission, device initialization, and retrieving results across nodes.
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## Getting Started
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Each notebook includes installation instructions and guidelines for configuring JAXPM and required dependencies. Follow the setup instructions in each notebook to ensure an optimal environment.
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## Requirements
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- **JAXPM** (included in the installation commands within notebooks)
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- **Diffrax** for ODE solvers
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- **JAX** with CUDA support for multi-GPU or TPU setups
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- **SLURM** for job scheduling on clusters (if running multi-host setups)
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> **Note**: These notebooks are tested on the **Jean Zay** supercomputer and may require configuration changes for different HPC clusters.
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