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https://github.com/JuliaFEM/JuliaFEM.jl.git
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78 lines
1.8 KiB
Markdown
78 lines
1.8 KiB
Markdown
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# Running GPU and MPI Demonstrations
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## Prerequisites
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These demonstrations require MPI and CUDA to be installed globally:
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```bash
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# MPI is already installed on your system
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# CUDA is already installed on your system
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```
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The packages are loaded dynamically, so they don't need to be in `Project.toml`.
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## Running the Demonstrations
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### MPI Communication Test
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Test data transfer between 2 MPI processes:
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```bash
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cd /home/juajukka/dev/JuliaFEM.jl
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mpirun -np 2 julia benchmarks/gpu_mpi_demo.jl
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```
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Expected output:
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- Rank 0 sends typed arrays to Rank 1
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- Shows bytes transferred
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- Validates data integrity
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### GPU + MPI Combined Test
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If CUDA GPU is available, runs full workflow:
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```bash
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mpirun -np 2 julia benchmarks/gpu_mpi_demo.jl
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```
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Expected output:
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- Detects GPU (if available)
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- Compiles type-stable kernel for GPU
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- Executes on real hardware
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- Transfers results via MPI
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### Single-Process GPU Test
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To test GPU without MPI:
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```bash
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julia benchmarks/gpu_only_demo.jl
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```
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## What Gets Demonstrated
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1. **Type Stability Requirement**
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- `Matrix{Float64}` transfers to GPU ✅
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- `Dict{String,Any}` would FAIL GPU compilation ❌
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2. **MPI Fast Transfer**
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- Typed arrays: Fast buffer transfer (memcpy)
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- Mixed types: Slow serialization (~100× slower)
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3. **Real Hardware Execution**
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- Actual CUDA kernel compilation and execution
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- Actual MPI inter-process communication
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- No mocks, no simulation
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## Interpreting Results
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Success indicators:
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- ✅ "MPI communication successful" - Type-stable data transferred
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- ✅ "GPU execution successful" - Kernel compiled and ran on GPU
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- ✅ "Combined GPU+MPI workflow successful" - End-to-end validated
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The key insight: The same type-stable patterns that give 9-92× CPU speedup also enable GPU execution and efficient MPI communication.
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