New 322-line demonstration script proving type-stable data flows to GPU and MPI:
Part 1: Type-stable data structures (lines 54-77)
- Creates nodes, connectivity, displacement as typed arrays
- Material properties E, ν as Float64
- All structures explicitly typed (Matrix{Float64}, not Dict)
Part 2: MPI communication (lines 79-123)
- Rank 0 sends 24KB displacement data to rank 1
- Transfers material properties
- Validates data integrity with checksum
- Uses MPI.Send/Recv with typed buffers
Part 3: GPU kernel execution (lines 125-192)
- Defines assemble_element_kernel! for CUDA
- Type-stable kernel: Float64, Int32, no allocations
- Transfers data to GPU (CuArray)
- Launches kernel with thread blocks
- Validates results against expected values
Part 4: Combined GPU+MPI workflow (lines 194-271)
- Rank 0 computes on GPU
- Transfers results via MPI to rank 1
- End-to-end validation
Summary section (lines 273-322):
- Reports hardware used (GPU model, MPI ranks)
- Key insights: type stability required for GPU, enables fast MPI
- Conclusion: type-stable fields are foundation for modern FEM
Requirements: MPI (required), CUDA (optional, detects and uses if available)
Run: mpiexec -np 2 julia --project=. demos/gpu_mpi_demo.jl
New 77-line guide documenting:
- Prerequisites (MPI and CUDA globally installed)
- Running commands for MPI communication test
- Running commands for combined GPU+MPI test
- Single-process GPU test instructions
- What gets demonstrated (type stability requirement, MPI fast transfer, real hardware)
- Success indicators and result interpretation
- Key insight: same patterns enable CPU speedup, GPU execution, and MPI efficiency
New 125-line README documenting:
- Two main demonstrations (GPU+MPI and Krylov solver)
- Requirements (Julia 1.9+, MPI, optional CUDA)
- Key insights: type stability required for GPU/MPI/Krylov
- Nodal assembly pattern explanation
- Architecture validation (v0.5.1 vs v1.0 comparison)
- References to benchmarks and design docs
- Contributing guidelines for new demos
Jukka Aho