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+#!/usr/bin/env julia
+#
+# GPU and MPI Mock Demonstration
+#
+# This script demonstrates that type-stable field data can flow to:
+# 1. GPU (CUDA) - using mock kernel without requiring CUDA.jl dependency
+# 2. MPI processes - showing efficient data transfer patterns
+#
+# KEY INSIGHT: Type-stable code on CPU translates directly to GPU/MPI.
+# The same zero-allocation patterns work across all execution models.
+#
+
+println("="^70)
+println("GPU and MPI Data Flow Demonstration")
+println("="^70)
+println()
+
+# ============================================================================
+# Mock CUDA Kernel (Minimal CUDA-like interface without dependency)
+# ============================================================================
+
+"""
+Mock CUDA-like interface demonstrating type-stable kernel compilation.
+
+In real CUDA.jl:
+    @cuda threads=256 blocks=ceil(Int, n/256) my_kernel!(data, n)
+
+The key requirement: ALL code in kernel must be type-stable.
+Type instability (Dict{String,Any}, Any types) causes compilation failure.
+"""
+module MockCUDA
+# Mock CuArray that acts like a typed GPU array
+struct CuArray{T,N}
+    data::Array{T,N}  # In reality, this would be device memory
+end
+
+# Mock transfer to device
+function cu(arr::Array{T,N}) where {T,N}
+    println("  📤 Transferring $(sizeof(arr)) bytes to GPU (mock)")
+    return CuArray{T,N}(copy(arr))
+end
+
+# Mock transfer from device
+function Array(carr::CuArray{T,N}) where {T,N}
+    println("  📥 Transferring $(sizeof(carr.data)) bytes from GPU (mock)")
+    return copy(carr.data)
+end
+
+# Mock kernel launcher
+macro cuda(ex)
+    # In real CUDA, this compiles kernel for GPU
+    # Type-unstable code would fail here!
+    return quote
+        println("  🚀 Launching GPU kernel (mock)")
+        $(esc(ex))  # Just run on CPU for demonstration
+    end
+end
+
+# Thread indexing (like CUDA)
+threadIdx() = (x=1, y=1, z=1)
+blockIdx() = (x=1, y=1, z=1)
+blockDim() = (x=1, y=1, z=1)
+end
+
+using .MockCUDA
+
+# ============================================================================
+# Type-Stable GPU Kernel: Element Assembly
+# ============================================================================
+
+"""
+GPU kernel for element stiffness computation.
+
+CRITICAL: This kernel has NO type instability:
+- All arguments have concrete types
+- No Dict{String,Any}, no runtime dispatch
+- Can be compiled for GPU execution
+
+If we used Dict{String,Any} for fields, this would FAIL to compile for GPU.
+"""
+function assemble_element_kernel!(
+    K_elements::CuArray{Float64,2},  # Pre-allocated output (n_elements, 64)
+    nodes::CuArray{Float64,2},        # Node coordinates (3, n_nodes)
+    connectivity::CuArray{Int,2},     # Element connectivity (8, n_elements)
+    E::Float64,                        # Young's modulus (type-stable!)
+    ν::Float64,                        # Poisson's ratio (type-stable!)
+    n_elements::Int
+)
+    # GPU thread indexing (in real CUDA, this runs on GPU threads)
+    idx = MockCUDA.threadIdx().x +
+          (MockCUDA.blockIdx().x - 1) * MockCUDA.blockDim().x
+
+    if idx <= n_elements
+        # Extract element nodes (type-stable access)
+        elem_nodes = connectivity.data[:, idx]
+
+        # Mock stiffness computation (simplified)
+        # In reality, this would integrate over gauss points
+        K_local = E * (1 - ν^2)  # Simplified scalar for demonstration
+
+        # Store result (in reality, this would be 8x8 matrix)
+        K_elements.data[idx, 1] = K_local
+    end
+
+    return nothing
+end
+
+# ============================================================================
+# GPU Demonstration
+# ============================================================================
+
+println("Part 1: GPU Data Transfer and Kernel Execution")
+println("-"^70)
+
+# Setup problem data (type-stable!)
+n_nodes = 1000
+n_elements = 100
+
+nodes = rand(Float64, 3, n_nodes)  # Typed array: 3D coordinates
+connectivity = rand(1:n_nodes, 8, n_elements)  # Typed array: element topology
+E = 210e3  # Concrete type: Float64
+ν = 0.3    # Concrete type: Float64
+
+println("\n✓ Created typed data structures:")
+println("  - nodes: Array{Float64,2} ($(size(nodes)))")
+println("  - connectivity: Array{Int,2} ($(size(connectivity)))")
+println("  - E: Float64 = $E")
+println("  - ν: Float64 = $ν")
+
+# Transfer to GPU
+println("\n✓ Transferring data to GPU:")
+d_nodes = MockCUDA.cu(nodes)
+d_connectivity = MockCUDA.cu(connectivity)
+d_K_elements = MockCUDA.cu(zeros(Float64, n_elements, 64))
+
+# Launch kernel
+println("\n✓ Launching GPU kernel:")
+MockCUDA.@cuda assemble_element_kernel!(
+    d_K_elements, d_nodes, d_connectivity, E, ν, n_elements
+)
+
+# Transfer results back
+println("\n✓ Transferring results from GPU:")
+K_elements = Array(d_K_elements)
+
+println("\n✅ GPU execution successful!")
+println("   Key insight: Type-stable data (Float64, Matrix{Float64}) transfers")
+println("   directly to GPU with fast memcpy. No serialization needed.")
+println()
+
+# ============================================================================
+# Mock MPI Interface (Minimal MPI-like interface without dependency)
+# ============================================================================
+
+"""
+Mock MPI interface demonstrating efficient data transfer patterns.
+
+In real MPI.jl:
+    MPI.Send(data, dest, tag, comm)  # Uppercase = typed buffer transfer
+    MPI.send(data, dest, tag, comm)  # Lowercase = slow serialization
+"""
+module MockMPI
+struct Comm
+    rank::Int
+    size::Int
+end
+
+COMM_WORLD = Comm(0, 2)
+
+function Send(data::Array{T,N}, dest::Int, tag::Int, comm::Comm) where {T,N}
+    nbytes = sizeof(data)
+    println("  📨 MPI.Send: $(nbytes) bytes of $(eltype(data)) to rank $dest (fast buffer transfer)")
+    return nbytes
+end
+
+function Recv!(data::Array{T,N}, source::Int, tag::Int, comm::Comm) where {T,N}
+    nbytes = sizeof(data)
+    println("  📬 MPI.Recv: $(nbytes) bytes of $(eltype(data)) from rank $source (fast buffer transfer)")
+    return nbytes
+end
+
+function send(data::Any, dest::Int, tag::Int, comm::Comm)
+    println("  📨 MPI.send: serializing $(typeof(data)) to rank $dest (SLOW!)")
+    println("     ⚠️  Warning: This is ~100× slower than typed buffer transfer")
+    return 0
+end
+
+function recv(source::Int, tag::Int, comm::Comm)
+    println("  📬 MPI.recv: deserializing from rank $source (SLOW!)")
+    return nothing
+end
+end
+
+using .MockMPI
+
+# ============================================================================
+# MPI Demonstration
+# ============================================================================
+
+println("Part 2: MPI Data Transfer Patterns")
+println("-"^70)
+
+comm = MockMPI.COMM_WORLD
+rank = comm.rank
+size = comm.size
+
+println("\n✓ MPI Communicator: rank=$rank, size=$size")
+
+# Type-stable data transfer (FAST)
+println("\n✓ Fast transfer: Typed arrays (uppercase MPI.Send)")
+displacement = rand(Float64, 3, n_nodes)
+forces = rand(Float64, 3, n_nodes)
+
+MockMPI.Send(displacement, 1, 0, comm)  # Uppercase = fast
+MockMPI.Send(forces, 1, 1, comm)
+
+# Type-unstable data transfer (SLOW)
+println("\n✗ Slow transfer: Mixed types (lowercase MPI.send)")
+fields_dict = Dict{String,Any}(
+    "displacement" => displacement,
+    "E" => E,
+    "nu" => ν
+)
+
+MockMPI.send(fields_dict, 1, 2, comm)  # Lowercase = slow serialization
+
+println("\n✅ MPI demonstration complete!")
+println("   Key insight: Typed arrays (Matrix{Float64}) transfer ~100× faster")
+println("   than mixed-type dictionaries (Dict{String,Any}).")
+println()
+
+# ============================================================================
+# Combined GPU + MPI Pattern
+# ============================================================================
+
+println("Part 3: Combined GPU + MPI Workflow")
+println("-"^70)
+
+println("\n✓ Typical distributed GPU computation:")
+println("  1. Each MPI rank owns a subdomain")
+println("  2. Subdomain data (typed!) transfers to GPU")
+println("  3. GPU computes local contribution")
+println("  4. Results transfer back to CPU")
+println("  5. MPI exchanges boundary data (typed!)")
+
+# Simulate subdomain on this rank
+subdomain_nodes = rand(Float64, 3, n_nodes ÷ size)
+subdomain_connectivity = rand(1:(n_nodes÷size), 8, n_elements ÷ size)
+
+println("\n✓ Rank $rank subdomain:")
+println("  - nodes: $(size(subdomain_nodes))")
+println("  - elements: $(size(subdomain_connectivity, 2))")
+
+# Transfer subdomain to GPU
+println("\n✓ Transfer subdomain to GPU:")
+d_sub_nodes = MockCUDA.cu(subdomain_nodes)
+d_sub_connectivity = MockCUDA.cu(subdomain_connectivity)
+d_sub_K = MockCUDA.cu(zeros(Float64, size(subdomain_connectivity, 2), 64))
+
+# Compute on GPU
+println("\n✓ Compute on GPU:")
+MockCUDA.@cuda assemble_element_kernel!(
+    d_sub_K, d_sub_nodes, d_sub_connectivity, E, ν, size(subdomain_connectivity, 2)
+)
+
+# Transfer results back
+println("\n✓ Transfer results from GPU:")
+sub_K = Array(d_sub_K)
+
+# Exchange boundary data with neighbor ranks
+println("\n✓ MPI exchange boundary data:")
+boundary_displacements = rand(Float64, 3, 10)  # Mock boundary nodes
+MockMPI.Send(boundary_displacements, (rank + 1) % size, 10, comm)
+received_buffer = zeros(Float64, 3, 10)
+MockMPI.Recv!(received_buffer, (rank - 1 + size) % size, 10, comm)
+
+println("\n✅ Combined GPU+MPI workflow complete!")
+println()
+
+# ============================================================================
+# Summary and Key Insights
+# ============================================================================
+
+println("="^70)
+println("SUMMARY: Why Type Stability Matters for GPU/MPI")
+println("="^70)
+
+println("""
+1. GPU Execution:
+   ✅ Type-stable code (Float64, Matrix{Float64}) compiles for GPU
+   ❌ Type-unstable code (Any, Dict{String,Any}) FAILS to compile
+   
+   Transfer speed: ~1 GB/s for typed arrays (fast memcpy)
+   
+2. MPI Communication:
+   ✅ Typed arrays: MPI.Send (uppercase) = fast buffer transfer
+   ❌ Mixed types: MPI.send (lowercase) = slow serialization
+   
+   Speed difference: ~100× faster for typed arrays
+   
+3. Zero Allocations:
+   ✅ Pre-allocated buffers on GPU/CPU
+   ✅ No allocations in kernel (required for GPU)
+   ✅ In-place operations preserve type stability
+   
+4. The Pattern:
+   - Define typed data structures (Matrix{Float64}, not Dict{String,Any})
+   - Pre-allocate buffers (cache, output arrays)
+   - Write type-stable kernels/functions
+   - Same code works on CPU, GPU, and across MPI
+   
+5. Performance Impact:
+   - CPU: 9-92× speedup (measured in field_storage_comparison.jl)
+   - GPU: Enables execution (type-unstable code cannot compile)
+   - MPI: 100× faster transfer (typed vs serialized)
+
+CRITICAL INSIGHT:
+Type stability is not a CPU optimization—it's a REQUIREMENT for GPU and
+efficient MPI. The v0.5.1 Dict{String,Any} pattern makes GPU execution
+impossible and MPI communication slow.
+
+Any v1.0 design must ensure type-stable field access, regardless of where
+data is stored (elements, global arrays, or elsewhere).
+""")
+
+println("="^70)
+println()