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+# 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:**
+
+```bash
+mpiexec -np 2 julia --project=. demos/gpu_mpi_demo.jl
+```
+
+**Documentation:** [README_GPU_MPI.md](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:**
+
+```bash
+mpiexec -np 2 julia --project=. demos/krylov_mpi_gpu_demo.jl
+```
+
+**Documentation:** [README_KRYLOV_DEMO.md](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.jl` for 9-92× speedup measurements
+- **Design documentation:** See `docs/book/zero_allocation_fields_v2.md` for architectural rationale
+- **Session notes:** See `llm/sessions/2025-11-09_gpu_mpi_validation.md` for 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.