Jukka Aho
3daed70615
GPU implementation for 10-node tetrahedral elements demonstrating higher-order finite elements with quadratic shape functions. Tet10 specifics: - 10 nodes per element (vertices + edge midpoints) - 4-point Gauss quadrature (order 2) - Quadratic shape functions (N_i second-order polynomials) - Shape function derivatives via analytical formulas Challenges vs Tet4: - More integration points (4 vs 1) - More DOFs per element (30 vs 12) - More complex shape functions - Larger local stiffness (10×10 vs 4×4 blocks) GPU kernel modifications: - Loop over 4 Gauss points instead of 1 - Evaluate quadratic shape functions at each IP - Accumulate contributions from all IPs - Scatter 30 DOFs per element (not 12) Benefits of Tet10: - Better stress/strain representation - Fewer elements needed for accuracy - Curved boundary representation - Higher convergence rate Same problem setup: 3D cantilever with steel properties Test validates GPU higher-order element implementation (430 lines).
JuliaFEM Technology Demonstrations
This directory contains demonstrations of key technologies and architectural decisions for JuliaFEM v1.0.
Overview
These demos validate that type-stable field storage enables modern high-performance computing patterns: GPU execution, MPI communication, and Krylov iterative solvers.
Demonstrations
1. GPU and MPI Communication (gpu_mpi_demo.jl)
Purpose: Prove that type-stable data structures flow efficiently to GPU and MPI.
What it demonstrates:
- Real CUDA GPU kernel execution
- MPI data transfer between processes
- Combined GPU+MPI workflow
Run:
mpiexec -np 2 julia --project=. demos/gpu_mpi_demo.jl
Documentation: README_GPU_MPI.md
2. Multi-GPU MPI Krylov Solver (krylov_mpi_gpu_demo.jl)
Purpose: Complete distributed FEM solver workflow with nodal assembly.
What it demonstrates:
- Nodal assembly pattern (row-by-row matrix construction)
- Distributed matrix-vector products
- Conjugate Gradient solver with MPI
- Multi-GPU execution
- Solution verification (10×10 SPD system)
Run:
mpiexec -np 2 julia --project=. demos/krylov_mpi_gpu_demo.jl
Documentation: README_KRYLOV_DEMO.md
Results:
- Converges in 9 iterations
- Relative error: 7.73 × 10⁻¹⁴
- Validates complete distributed solving workflow
Requirements
Required
- Julia 1.9+
- MPI installation (e.g., OpenMPI, MPICH)
- MPI.jl package
Optional (for GPU demos)
- CUDA-capable GPU
- CUDA.jl package
If CUDA is not available, demos will fall back to CPU execution while still demonstrating the distributed computing patterns.
Key Insights
Type Stability is Not Optional
These demos prove that type-stable field storage is required (not just "nice to have") for:
| Feature | Why Type Stability Required |
|---|---|
| GPU execution | CUDA kernels cannot compile with abstract types |
| Fast MPI | Typed buffers avoid serialization overhead |
| Krylov solvers | Matrix-free operators need concrete types |
| CPU performance | 9-92× speedup measured (see CPU benchmarks) |
Nodal Assembly Pattern
The Krylov demo shows row-by-row matrix construction (get_row() abstraction), which:
- Aligns naturally with contact mechanics (nodal constraints)
- Enables domain decomposition (each rank owns nodes)
- Supports matrix-free solving (never form global matrix)
- Scales to large problems (O(N) memory vs O(N²))
Architectural Validation
These demos validate the design decisions for JuliaFEM v1.0:
v0.5.1 (2019):
- Dict-based fields → Type instability
- Element assembly → Global matrix
- Direct solvers → O(N³) time, O(N²) memory
- Single-threaded CPU
v1.0 (target, validated here):
- Type-stable fields → GPU/MPI capable
- Nodal assembly → Distributed construction
- Krylov solvers → O(N·iter) time, O(N) memory
- Multi-GPU + MPI
References
- CPU benchmarks: See
benchmarks/field_storage_comparison.jlfor 9-92× speedup measurements - Design documentation: See
docs/book/zero_allocation_fields_v2.mdfor architectural rationale - Session notes: See
llm/sessions/2025-11-09_gpu_mpi_validation.mdfor development history
Contributing
These demos are educational and meant to be:
- Clear: Understand what's being demonstrated
- Minimal: No unnecessary complexity
- Runnable: Work on typical hardware (fall back to CPU if needed)
- Validated: Compare against exact solutions
When adding new demos, follow this pattern and document thoroughly.