You are indeed right. AI told me the wrong answer, and I was not critical enough. Here a code to check:
#!/usr/bin/env julia
# Demonstration of Vector{MVector} memory layout - corrected analysis
# Copyright (c) 2025 Uwe Fechner
# SPDX-License-Identifier: MIT
using StaticArrays
function demonstrate_mvector_memory_layout()
println("=== Memory Layout Demonstration: Vector{MVector} Storage ===\n")
# Create a Vector of MVectors
mvec_array = [MVector{3, Float64}([i, i+0.1, i+0.2]) for i in 1:5]
# Create a Vector of regular Vectors (stored by reference)
vec_array = [Vector{Float64}([i, i+0.1, i+0.2]) for i in 1:5]
println("1. SIZE COMPARISON:")
println(" Vector{MVector{3,Float64}} size: $(sizeof(mvec_array)) bytes")
println(" Vector{Vector{Float64}} size: $(sizeof(vec_array)) bytes")
println(" MVector element size: $(sizeof(MVector{3, Float64})) bytes")
println(" Pointer size: $(sizeof(Ptr{MVector{3, Float64}})) bytes\n")
# Both are pointer-based storage, but with different heap allocation patterns
expected_ptr_size = 5 * sizeof(Ptr{Any})
println("2. STORAGE ANALYSIS:")
println(" Expected pointer array size: $(expected_ptr_size) bytes")
println(" Actual Vector{MVector} size: $(sizeof(mvec_array)) bytes")
println(" Actual Vector{Vector} size: $(sizeof(vec_array)) bytes")
println(" → Both use pointer-based storage\n")
println("3. HEAP ALLOCATION PATTERN:")
# Check where the actual MVector data is stored
for i in 1:3
mvec_ref = mvec_array[i]
mvec_addr = pointer_from_objref(mvec_ref)
println(" MVector $i:")
println(" Object reference: $(mvec_ref)")
println(" Heap address: $(mvec_addr)")
println(" Data: [$(mvec_ref[1]), $(mvec_ref[2]), $(mvec_ref[3])]")
end
println("\n4. STACK vs HEAP ALLOCATION COMPARISON:")
# Compare with stack-allocated MVector
function test_stack_mvector()
# This MVector is allocated on the stack
stack_mv = MVector{3, Float64}([1.0, 2.0, 3.0])
stack_addr = pointer(stack_mv)
println(" Stack MVector address: $(stack_addr)")
return stack_mv
end
function test_heap_mvector()
# This creates a heap-allocated MVector (when returned)
heap_mv = MVector{3, Float64}([1.0, 2.0, 3.0])
heap_addr = pointer_from_objref(heap_mv)
println(" Heap MVector address: $(heap_addr)")
return heap_mv
end
stack_result = test_stack_mvector()
heap_result = test_heap_mvector()
println("\n5. PERFORMANCE COMPARISON:")
# Create arrays for benchmarking
n_elements = 1000
mvec_large = [MVector{3, Float64}([i, i+0.1, i+0.2]) for i in 1:n_elements]
vec_large = [Vector{Float64}([i, i+0.1, i+0.2]) for i in 1:n_elements]
# Benchmark access patterns
n_iterations = 10_000
# MVector access
mvec_time = @elapsed begin
sum_val = 0.0
for _ in 1:n_iterations
for mv in mvec_large
sum_val += mv[1] + mv[2] + mv[3]
end
end
sum_val
end
# Vector access
vec_time = @elapsed begin
sum_val = 0.0
for _ in 1:n_iterations
for v in vec_large
sum_val += v[1] + v[2] + v[3]
end
end
sum_val
end
println(" MVector access time: $(round(mvec_time*1000, digits=3)) ms")
println(" Vector access time: $(round(vec_time*1000, digits=3)) ms")
println(" MVector is $(round(vec_time/mvec_time, digits=2))x faster")
println("\n=== CORRECTED UNDERSTANDING ===")
println("✓ Vector{MVector} stores REFERENCES to heap-allocated MVectors")
println("✓ Each MVector is a separate heap object (unlike C arrays)")
println("✓ Still faster than Vector{Vector} due to:")
println(" - Fixed-size, stack-like allocation pattern")
println(" - Better memory layout within each MVector")
println(" - Compiler optimizations for StaticArrays")
println("✓ For truly contiguous storage, use regular Arrays or StaticArrays")
end
function demonstrate_true_contiguous_storage()
println("\n=== TRUE CONTIGUOUS STORAGE EXAMPLES ===")
# Method 1: Regular Array (contiguous)
regular_array = [[i, i+0.1, i+0.2] for i in 1:5]
reshaped = reshape(hcat(regular_array...), 3, 5)'
flat_data = vec(reshaped)
println("1. FLATTENED ARRAY (truly contiguous):")
println(" Data: $(flat_data)")
println(" Size: $(sizeof(flat_data)) bytes")
println(" Element size: $(sizeof(Float64)) bytes")
# Method 2: SMatrix storage
smatrix_array = [SVector{3, Float64}([i, i+0.1, i+0.2]) for i in 1:5]
println("\n2. VECTOR{SVECTOR} (immutable, better layout):")
println(" Size: $(sizeof(smatrix_array)) bytes")
println(" Expected if contiguous: $(5 * sizeof(SVector{3, Float64})) bytes")
# Check if SVector is truly stored by value
is_svector_contiguous = sizeof(smatrix_array) == 5 * sizeof(SVector{3, Float64})
println(" → SVector stored contiguously: $(is_svector_contiguous ? "✓ YES" : "✗ NO")")
if is_svector_contiguous
println(" ✓ SVector achieves true value-based storage!")
else
println(" ✗ SVector also uses reference-based storage")
end
println("\n3. PERFORMANCE COMPARISON:")
n_iter = 100_000
# Benchmark flattened access
flat_time = @elapsed begin
sum_val = 0.0
for _ in 1:n_iter
for i in 1:3:length(flat_data)-2
sum_val += flat_data[i] + flat_data[i+1] + flat_data[i+2]
end
end
sum_val
end
# Benchmark SVector access
svec_time = @elapsed begin
sum_val = 0.0
for _ in 1:n_iter
for sv in smatrix_array
sum_val += sv[1] + sv[2] + sv[3]
end
end
sum_val
end
println(" Flattened array time: $(round(flat_time*1000, digits=3)) ms")
println(" SVector array time: $(round(svec_time*1000, digits=3)) ms")
end
# Run the demonstrations
if abspath(PROGRAM_FILE) == @__FILE__
demonstrate_mvector_memory_layout()
demonstrate_true_contiguous_storage()
println("\n" * "="^60)
println("FINAL ANSWER TO THE ORIGINAL QUESTION:")
println("="^60)
println("❌ Vector{MVector} is NOT stored in contiguous memory")
println("✅ Vector{MVector} stores POINTERS to heap-allocated MVectors")
println("✅ Vector{SVector} IS stored contiguously (value-based)")
println("✅ For contiguous storage, use:")
println(" - Vector{SVector{N,T}} (immutable, stack-friendly)")
println(" - Regular flat arrays: Matrix or Vector{T}")
println(" - Custom struct with contiguous fields")
println("="^60)
end
So SVectors are the way to go, and you can mutate them using Accessors.jl for example.