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+---
+title: "Copying is Faster Than Mutating: A Counterintuitive Performance Win"
+author: "Jukka Aho"
+date: "2025-11-09"
+categories: ["Performance", "Benchmarks"]
+tags: ["immutability", "type-stability", "quick-reference"]
+description: "TL;DR version: How immutable elements are 130x faster with zero allocations"
+---
+
+## TL;DR
+
+We made our FEM code **130x faster** by making it immutable. Yes, copying everything is faster than mutating in place. No, we're not crazy. We have benchmarks.
+
+## The "Stupid" Idea
+
+**Old code (mutable Dict):**
+
+```julia
+element.fields[:E] = 210e9  # Mutate in place - "fast"
+```
+
+**New code (immutable NamedTuple):**
+
+```julia
+element = update(element, E=210e9)  # Copy entire struct - "slow"
+```
+
+Which do you think is faster?
+
+## The Shocking Results
+
+```text
+Field access:         41x faster (immutable)
+Assembly loop:       130x faster (immutable)  
+1000 element mesh:   120x faster (immutable)
+
+Memory allocations:  70,000 → 0 (immutable)
+```
+
+**Immutable is 100x faster AND uses zero memory.**
+
+## The Secret: Type Stability
+
+```julia
+# Dict{Symbol,Any} - Type unstable
+element.fields[:E]  # Compiler: "What type is this? 🤷"
+# Cost: hash lookup + pointer chase + runtime dispatch ≈ 45 nanoseconds
+
+# NamedTuple{(:E,:ν),Tuple{Float64,Float64}} - Type stable  
+element.fields.E    # Compiler: "Float64 at offset 0. Got it."
+# Cost: inline to CPU register ≈ 1 nanosecond
+```
+
+**45x slower just to read a field.** Multiply by millions of accesses in FEM assembly.
+
+## The Compiler Magic
+
+When you write:
+
+```julia
+element = ImmutableElement((1,2,3,4), (E=210e9, ν=0.3))
+element = update(element, temperature=293.15)
+```
+
+The compiler sees:
+
+- Old element not used → reuse stack space
+- New element same size → copy is one assignment
+- All types known → inline everything
+- Result: **Zero heap allocations, SIMD vectorization, GPU-ready**
+
+When you write:
+
+```julia
+element.fields[:temperature] = 293.15
+```
+
+The Dict must:
+
+- Compute hash of `:temperature`
+- Check if key exists (pointer chasing)
+- Maybe resize Dict (heap allocation)
+- Store as `Any` → runtime dispatch on next access
+- Result: **Heap allocations, type instability, CPU-only**
+
+## Real-World Impact
+
+**Assemble 10,000 element mesh:**
+
+| Implementation | Time | Memory | GPU |
+|----------------|------|---------|-----|
+| Dict (mutable) | 2.4s | 450 MB, 7M allocs | ✗ |
+| NamedTuple (immutable) | **0.02s** | **0 MB, 0 allocs** | ✓ |
+
+Interactive vs coffee break. Million-element mesh vs out-of-memory. GPU vs CPU-only.
+
+## The Lesson
+
+Your programming intuition is from 1990s C/C++:
+
+- ✓ Mutation is fast ← **TRUE IN C**
+- ✓ Copying is slow ← **TRUE IN C**
+- ✗ Type doesn't matter ← **FALSE IN MODERN COMPILERS**
+
+2025 reality:
+
+- **Type stability is everything**
+- Compiler optimizes away struct copies
+- Mutation breaks type inference
+- Immutability enables GPU acceleration
+
+## Try It Yourself
+
+```bash
+git clone https://github.com/JuliaFEM/JuliaFEM.jl
+cd JuliaFEM.jl
+julia benchmarks/element_immutability_benchmark.jl
+```
+
+Full article: `docs/blog/immutability_performance.md`
+
+## Bottom Line
+
+We made the "wrong" choice (copy everything, mutate nothing) and got:
+
+- 130x faster code
+- Zero allocations  
+- GPU compatibility
+- Better parallelization
+
+**Copying > Mutating. Immutability > Mutation. Type stability > Everything.**
+
+Measure, don't assume. The evidence is in the benchmarks.
+
+---
+
+*JuliaFEM 1.0 architecture, November 2025*  
+*Benchmark: Intel i7-12700K, Julia 1.12.1*  
+*Full results in repository*