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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
-
-### Assembly Strategy Comparison (`cantilever_cpu_comparison.jl`)
-
-**Purpose:** Algorithm-level comparison of assembly strategies (RESEARCH).
-
-⚠️ **NOT a user-facing example!** This is low-level performance research.
-
-**What it demonstrates:**
-
-- Element-by-element assembly (traditional FEM)
-- Node-by-node assembly (GPU-friendly, contact-ready)
-- Matrix-free iterative solvers
-- Direct comparison: assembly time, iterations, accuracy
-
-**Why low-level:**
-
-- Uses `ElementAssemblyData` and `NodeToElementsMap` structures directly
-- Manually constructs element stiffness matrices
-- Not representative of user workflow
-
-**For users:** See `examples/linear_static.jl` or `cantilever_gmsh_gpu.jl` instead.
-
-**Results (135 Tet4 elements):**
-
-- Nodal assembly: 4.7× faster than element assembly
-- Nodal CG: 2× fewer iterations (215 vs 417)
-- All methods give identical displacements
-
-### 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.