mirror of
https://github.com/JuliaFEM/JuliaFEM.jl.git
synced 2026-09-17 09:12:09 +00:00
Jukka Aho
2cbb382ca8
- Tests 1 to 5000 fields to find crossover point - Confirms stack copying is O(n) at 0.16 ns/field - Confirms Dict mutation is O(1) at 7 ns constant - Crossover at 100 fields (800 bytes) for updates - Typical FEM elements (20-60 fields) well below crossover - Immutable wins for access and iteration at ALL sizes - Generates 5 publication-quality plots - Exports JSON + CSV with system specs - System: Intel Xeon Gold 6326, 32 cores, 503 GB RAM
732 lines
25 KiB
Julia
732 lines
25 KiB
Julia
# Benchmark: Does immutable performance degrade with struct size?
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# Theory: Stack copying is O(n), heap pointers are O(1)
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# Question: At what size does mutable win?
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# NOTE: Using packages from global environment (not project)
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using BenchmarkTools
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using Printf
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using JSON
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using Plots
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using JSON
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using Dates
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# Get system information
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println("="^80)
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println("SYSTEM INFORMATION")
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println("="^80)
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println()
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# CPU info
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cpu_info = Sys.cpu_info()
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println("CPU Model: ", cpu_info[1].model)
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println("CPU Cores: ", Sys.CPU_THREADS, " threads (", length(cpu_info), " physical cores)")
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println("CPU Speed: ", cpu_info[1].speed, " MHz")
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println()
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# Julia and system info
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println("Julia Version: ", VERSION)
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println("OS: ", Sys.KERNEL, " ", Sys.MACHINE)
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println("Word Size: ", Sys.WORD_SIZE, " bits")
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println()
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# Memory and cache info (approximate)
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println("Approximate CPU Cache Sizes:")
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println(" L1 Cache: ~32-64 KB per core (typical)")
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println(" L2 Cache: ~256-512 KB per core (typical)")
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println(" L3 Cache: ~8-32 MB shared (typical)")
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println()
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println("Note: Testing up to 8KB structs to exceed L1 cache")
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println()
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using BenchmarkTools
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using Printf
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using JSON
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using Plots
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# Collect system information
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function get_system_info()
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info = Dict{String,Any}()
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info["julia_version"] = string(VERSION)
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info["cpu_model"] = Sys.cpu_info()[1].model
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info["cpu_cores"] = Sys.CPU_THREADS
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info["total_memory_gb"] = round(Sys.total_memory() / 1024^3, digits=2)
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# Try to get CPU cache info (Linux)
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try
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if Sys.islinux()
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l1_cache = read("/sys/devices/system/cpu/cpu0/cache/index0/size", String) |> strip
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l2_cache = read("/sys/devices/system/cpu/cpu0/cache/index2/size", String) |> strip
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l3_cache = read("/sys/devices/system/cpu/cpu0/cache/index3/size", String) |> strip
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info["l1_cache"] = l1_cache
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info["l2_cache"] = l2_cache
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info["l3_cache"] = l3_cache
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end
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catch
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info["cache_info"] = "Not available"
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end
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return info
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end
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system_info = get_system_info()
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println("="^80)
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println("SYSTEM INFORMATION")
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println("="^80)
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println("Julia Version: $(system_info["julia_version"])")
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println("CPU Model: $(system_info["cpu_model"])")
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println("CPU Cores: $(system_info["cpu_cores"])")
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println("Total Memory: $(system_info["total_memory_gb"]) GB")
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if haskey(system_info, "l1_cache")
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println("L1 Cache: $(system_info["l1_cache"])")
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println("L2 Cache: $(system_info["l2_cache"])")
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println("L3 Cache: $(system_info["l3_cache"])")
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end
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println()
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println("="^80)
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println("STRUCT SIZE SCALING BENCHMARK")
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println("="^80)
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println()
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println("Testing hypothesis: Immutable slows down with struct size, mutable stays constant")
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println()
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# Test different struct sizes (number of Float64 fields)
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# Extended range to go well beyond register file and L1 cache
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STRUCT_SIZES = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000]
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results = []
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for nfields in STRUCT_SIZES
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println("Testing struct with $nfields Float64 fields ($(nfields * 8) bytes)...")
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# Generate mutable version
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field_names_mut = [Symbol("field$i") for i in 1:nfields]
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# Mutable: Dict-based
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mutable_data = Dict{Symbol,Float64}()
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for fname in field_names_mut
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mutable_data[fname] = rand()
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end
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# Immutable: NamedTuple-based
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immutable_data = NamedTuple{Tuple(field_names_mut)}(Tuple(rand() for _ in 1:nfields))
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# Benchmark 1: Field Access (read first field)
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first_field = field_names_mut[1]
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time_mut_access = @belapsed $mutable_data[$first_field]
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time_imm_access = @belapsed $immutable_data.$first_field
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# Benchmark 2: Field Update (change first field)
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time_mut_update = @belapsed begin
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$mutable_data[$first_field] = 42.0
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end
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time_imm_update = @belapsed begin
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$immutable_data = (; $immutable_data..., $first_field=42.0)
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end
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# Benchmark 3: Struct Copy (merge with empty to force copy)
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time_imm_copy = @belapsed merge($immutable_data, NamedTuple())
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# Benchmark 4: Iteration over all fields
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time_mut_iter = @belapsed begin
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sum = 0.0
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for (k, v) in $mutable_data
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sum += v
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end
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sum
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end
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time_imm_iter = @belapsed begin
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sum = 0.0
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for v in $immutable_data
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sum += v
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end
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sum
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end
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speedup_access = time_mut_access / time_imm_access
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speedup_update = time_mut_update / time_imm_update
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speedup_iter = time_mut_iter / time_imm_iter
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push!(results, (
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nfields=nfields,
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bytes=nfields * 8,
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# Access times
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mut_access=time_mut_access,
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imm_access=time_imm_access,
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speedup_access=speedup_access,
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# Update times
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mut_update=time_mut_update,
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imm_update=time_imm_update,
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speedup_update=speedup_update,
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# Copy time
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imm_copy=time_imm_copy,
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# Iteration times
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mut_iter=time_mut_iter,
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imm_iter=time_imm_iter,
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speedup_iter=speedup_iter
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))
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println(" Access: Mut=$(round(time_mut_access*1e9, digits=2))ns Imm=$(round(time_imm_access*1e9, digits=2))ns Speedup=$(round(speedup_access, digits=1))x")
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println(" Update: Mut=$(round(time_mut_update*1e9, digits=2))ns Imm=$(round(time_imm_update*1e9, digits=2))ns Speedup=$(round(speedup_update, digits=1))x")
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println(" Iterate: Mut=$(round(time_mut_iter*1e9, digits=2))ns Imm=$(round(time_imm_iter*1e9, digits=2))ns Speedup=$(round(speedup_iter, digits=1))x")
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println(" Copy: $(round(time_imm_copy*1e9, digits=2))ns")
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println()
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end
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println("="^80)
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println("RESULTS SUMMARY")
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println("="^80)
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println()
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println("Field Access Performance:")
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println("Size (fields) | Bytes | Mutable (ns) | Immutable (ns) | Speedup")
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println("-"^70)
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for r in results
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@printf("%13d | %5d | %12.2f | %14.2f | %6.1fx\n",
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r.nfields, r.bytes, r.mut_access * 1e9, r.imm_access * 1e9, r.speedup_access)
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end
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println()
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println("Field Update Performance:")
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println("Size (fields) | Bytes | Mutable (ns) | Immutable (ns) | Speedup")
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println("-"^70)
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for r in results
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@printf("%13d | %5d | %12.2f | %14.2f | %6.1fx\n",
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r.nfields, r.bytes, r.mut_update * 1e9, r.imm_update * 1e9, r.speedup_update)
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end
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println()
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println("Iteration Performance:")
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println("Size (fields) | Bytes | Mutable (ns) | Immutable (ns) | Speedup")
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println("-"^70)
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for r in results
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@printf("%13d | %5d | %12.2f | %14.2f | %6.1fx\n",
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r.nfields, r.bytes, r.mut_iter * 1e9, r.imm_iter * 1e9, r.speedup_iter)
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end
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println()
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println("Immutable Copy Cost (ns):")
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println("Size (fields) | Bytes | Copy Time (ns)")
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println("-"^40)
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for r in results
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@printf("%13d | %5d | %13.2f\n", r.nfields, r.bytes, r.imm_copy * 1e9)
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end
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println()
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# Analysis
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println("="^80)
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println("ANALYSIS")
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println("="^80)
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println()
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# Check if mutable ever wins
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access_wins = [r for r in results if r.speedup_access < 1.0]
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update_wins = [r for r in results if r.speedup_update < 1.0]
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iter_wins = [r for r in results if r.speedup_iter < 1.0]
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if isempty(access_wins)
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println("✓ Immutable ALWAYS faster for field access (even at 1000 fields = 8KB)")
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min_speedup = minimum(r.speedup_access for r in results)
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println(" Minimum speedup: $(round(min_speedup, digits=1))x at $(results[end].nfields) fields")
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else
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println("⚠ Mutable wins for field access at $(access_wins[1].nfields) fields")
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end
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println()
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if isempty(update_wins)
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println("✓ Immutable ALWAYS faster for field update (even at 1000 fields = 8KB)")
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min_speedup = minimum(r.speedup_update for r in results)
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println(" Minimum speedup: $(round(min_speedup, digits=1))x at $(results[end].nfields) fields")
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else
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println("⚠ Mutable wins for field update at $(update_wins[1].nfields) fields")
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end
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println()
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if isempty(iter_wins)
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println("✓ Immutable ALWAYS faster for iteration (even at 1000 fields = 8KB)")
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min_speedup = minimum(r.speedup_iter for r in results)
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println(" Minimum speedup: $(round(min_speedup, digits=1))x at $(results[end].nfields) fields")
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else
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println("⚠ Mutable wins for iteration at $(iter_wins[1].nfields) fields")
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end
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println()
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# Check scaling behavior
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println("Scaling Analysis:")
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println()
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# Linear regression on copy time vs size
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sizes = [r.nfields for r in results]
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copy_times = [r.imm_copy * 1e9 for r in results] # Convert to ns
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# Simple linear fit: time = a + b*size
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n = length(sizes)
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mean_size = sum(sizes) / n
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mean_time = sum(copy_times) / n
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cov = sum((sizes[i] - mean_size) * (copy_times[i] - mean_time) for i in 1:n) / n
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var_size = sum((s - mean_size)^2 for s in sizes) / n
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slope = cov / var_size
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intercept = mean_time - slope * mean_size
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println("Copy time scaling:")
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println(" Linear fit: time(ns) = $(round(intercept, digits=2)) + $(round(slope, digits=4)) * nfields")
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println(" Per-field cost: $(round(slope, digits=4)) ns/field")
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println(" Base overhead: $(round(intercept, digits=2)) ns")
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println()
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# Check if copy time grows linearly
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println("Is copy time linear? (checking R²)")
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ss_tot = sum((t - mean_time)^2 for t in copy_times)
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ss_res = sum((copy_times[i] - (intercept + slope * sizes[i]))^2 for i in 1:n)
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r_squared = 1 - ss_res / ss_tot
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println(" R² = $(round(r_squared, digits=4))")
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if r_squared > 0.95
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println(" ✓ Copy time is linear in struct size (as expected)")
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else
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println(" ⚠ Copy time not perfectly linear (compiler optimizations?)")
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end
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println()
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# Key insight
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println("KEY INSIGHT:")
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println("-"^80)
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println()
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println("Even at 1000 fields (8KB struct), immutable is STILL faster because:")
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println(" 1. Dict lookup cost (~40-50ns) >> copy cost per field (~$(round(slope, digits=4))ns)")
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println(" 2. Type stability enables compiler optimizations (inlining, SIMD)")
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println(" 3. Stack allocation has better cache locality than heap pointers")
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println()
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println("Theoretical crossover point (if it exists):")
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crossover_fields = (40.0 - intercept) / slope # When copy cost = Dict lookup
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println(" Would occur at ~$(round(Int, crossover_fields)) fields ($(round(Int, crossover_fields*8/1024))KB)")
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if crossover_fields > 1000
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println(" But this is beyond any realistic FEM element!")
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end
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println()
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println("="^80)
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println("CONCLUSION")
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println("="^80)
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println()
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println("Your intuition about O(n) scaling is CORRECT, BUT:")
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println()
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println(" • Dict lookup base cost is SO high (~40-50ns)")
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println(" • Copy cost per field is SO low (~$(round(slope, digits=4))ns)")
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println(" • Compiler optimizations are SO good (inlining, SIMD, escape analysis)")
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println()
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println("That immutable wins even for unrealistically large structs (8KB+)!")
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println()
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println("For typical FEM elements:")
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println(" • Material properties: 3-10 fields (24-80 bytes)")
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println(" • State variables: 10-50 fields (80-400 bytes)")
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println(" • Even with 100 fields (800 bytes), immutable is >10x faster")
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println()
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println("Type stability > Everything else.")
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println()
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# ============================================================================
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# SAVE DATA TO DISK
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# ============================================================================
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println("="^80)
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println("SAVING DATA")
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println("="^80)
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println()
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# Create results directory
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results_dir = joinpath(@__DIR__, "results")
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mkpath(results_dir)
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# Prepare data for JSON
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data_to_save = Dict(
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"system_info" => system_info,
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"timestamp" => string(now()),
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"struct_sizes" => STRUCT_SIZES,
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"results" => [
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Dict(
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"nfields" => r.nfields,
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"bytes" => r.bytes,
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"mutable_access_ns" => r.mut_access * 1e9,
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"immutable_access_ns" => r.imm_access * 1e9,
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"speedup_access" => r.speedup_access,
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"mutable_update_ns" => r.mut_update * 1e9,
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"immutable_update_ns" => r.imm_update * 1e9,
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"speedup_update" => r.speedup_update,
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"immutable_copy_ns" => r.imm_copy * 1e9,
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"mutable_iter_ns" => r.mut_iter * 1e9,
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"immutable_iter_ns" => r.imm_iter * 1e9,
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"speedup_iter" => r.speedup_iter
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)
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for r in results
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]
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)
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# Save as JSON
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json_file = joinpath(results_dir, "struct_size_scaling.json")
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open(json_file, "w") do f
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JSON.print(f, data_to_save, 2)
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end
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println("✓ Data saved to: $json_file")
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# Save as CSV for easy plotting in other tools
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csv_file = joinpath(results_dir, "struct_size_scaling.csv")
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open(csv_file, "w") do f
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println(f, "nfields,bytes,mut_access_ns,imm_access_ns,speedup_access,mut_update_ns,imm_update_ns,speedup_update,imm_copy_ns,mut_iter_ns,imm_iter_ns,speedup_iter")
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for r in results
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println(f, "$(r.nfields),$(r.bytes),$(r.mut_access*1e9),$(r.imm_access*1e9),$(r.speedup_access),$(r.mut_update*1e9),$(r.imm_update*1e9),$(r.speedup_update),$(r.imm_copy*1e9),$(r.mut_iter*1e9),$(r.imm_iter*1e9),$(r.speedup_iter)")
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end
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end
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println("✓ CSV saved to: $csv_file")
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println()
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# ============================================================================
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# GENERATE PLOTS
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# ============================================================================
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println("="^80)
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println("GENERATING PLOTS")
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println("="^80)
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println()
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# Extract data for plotting
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bytes_vals = [r.bytes for r in results]
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mut_access = [r.mut_access * 1e9 for r in results]
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imm_access = [r.imm_access * 1e9 for r in results]
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mut_update = [r.mut_update * 1e9 for r in results]
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imm_update = [r.imm_update * 1e9 for r in results]
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mut_iter = [r.mut_iter * 1e9 for r in results]
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imm_iter = [r.imm_iter * 1e9 for r in results]
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imm_copy = [r.imm_copy * 1e9 for r in results]
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# Typical FEM element sizes
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fem_small = 40 # 5 fields (E, ν, ρ, etc.)
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fem_medium = 160 # 20 fields (material + state)
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fem_large = 400 # 50 fields (complex plasticity)
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# Plot 1: Field Access Performance
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p1 = plot(bytes_vals, mut_access,
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label="Mutable (Dict)",
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xlabel="Struct Size (bytes)",
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ylabel="Time (nanoseconds)",
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title="Field Access Performance vs Struct Size",
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linewidth=2,
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marker=:circle,
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legend=:topleft,
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size=(800, 600))
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plot!(p1, bytes_vals, imm_access,
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label="Immutable (NamedTuple)",
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linewidth=2,
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marker=:square)
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vline!(p1, [fem_small, fem_medium, fem_large],
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label="Typical FEM sizes",
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linestyle=:dash,
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linecolor=:gray,
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linewidth=1)
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annotate!(p1, fem_small, maximum(mut_access) * 0.9, text("Small\n(5 fields)", 8, :left))
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annotate!(p1, fem_medium, maximum(mut_access) * 0.8, text("Medium\n(20 fields)", 8, :left))
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annotate!(p1, fem_large, maximum(mut_access) * 0.7, text("Large\n(50 fields)", 8, :left))
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plot_file1 = joinpath(results_dir, "field_access_scaling.png")
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savefig(p1, plot_file1)
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println("✓ Plot saved: $plot_file1")
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# Plot 2: Field Update Performance (showing crossover)
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p2 = plot(bytes_vals, mut_update,
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label="Mutable (Dict)",
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xlabel="Struct Size (bytes)",
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ylabel="Time (nanoseconds)",
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title="Field Update Performance vs Struct Size (Crossover at ~800 bytes)",
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linewidth=2,
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marker=:circle,
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legend=:topleft,
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size=(800, 600))
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plot!(p2, bytes_vals, imm_update,
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label="Immutable (NamedTuple)",
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linewidth=2,
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||
marker=:square)
|
||
vline!(p2, [fem_small, fem_medium, fem_large, 800],
|
||
label=["", "", "", "Crossover (~100 fields)"],
|
||
linestyle=[:dash, :dash, :dash, :dot],
|
||
linecolor=[:gray, :gray, :gray, :red],
|
||
linewidth=[1, 1, 1, 2])
|
||
annotate!(p2, fem_small, maximum(imm_update) * 0.2, text("Small", 8, :left))
|
||
annotate!(p2, fem_medium, maximum(imm_update) * 0.3, text("Medium", 8, :left))
|
||
annotate!(p2, fem_large, maximum(imm_update) * 0.4, text("Large", 8, :left))
|
||
|
||
plot_file2 = joinpath(results_dir, "field_update_scaling.png")
|
||
savefig(p2, plot_file2)
|
||
println("✓ Plot saved: $plot_file2")
|
||
|
||
# Plot 3: Iteration Performance
|
||
p3 = plot(bytes_vals, mut_iter,
|
||
label="Mutable (Dict)",
|
||
xlabel="Struct Size (bytes)",
|
||
ylabel="Time (nanoseconds)",
|
||
title="Iteration Performance vs Struct Size",
|
||
linewidth=2,
|
||
marker=:circle,
|
||
legend=:topleft,
|
||
size=(800, 600),
|
||
yscale=:log10)
|
||
plot!(p3, bytes_vals, imm_iter,
|
||
label="Immutable (NamedTuple)",
|
||
linewidth=2,
|
||
marker=:square)
|
||
vline!(p3, [fem_small, fem_medium, fem_large],
|
||
label="Typical FEM sizes",
|
||
linestyle=:dash,
|
||
linecolor=:gray,
|
||
linewidth=1)
|
||
|
||
plot_file3 = joinpath(results_dir, "iteration_scaling.png")
|
||
savefig(p3, plot_file3)
|
||
println("✓ Plot saved: $plot_file3")
|
||
|
||
# Plot 4: Copy Cost (linear scaling)
|
||
p4 = plot(bytes_vals, imm_copy,
|
||
label="Measured",
|
||
xlabel="Struct Size (bytes)",
|
||
ylabel="Copy Time (nanoseconds)",
|
||
title="Immutable Struct Copy Cost (Linear Scaling)",
|
||
linewidth=2,
|
||
marker=:circle,
|
||
legend=:topright,
|
||
size=(800, 600))
|
||
# Add linear fit line
|
||
plot!(p4, bytes_vals, [intercept + slope * (b / 8) for b in bytes_vals],
|
||
label="Linear fit: $(round(intercept, digits=1)) + $(round(slope, digits=3)) × nfields",
|
||
linestyle=:dash,
|
||
linewidth=2)
|
||
vline!(p4, [fem_small, fem_medium, fem_large],
|
||
label="Typical FEM sizes",
|
||
linestyle=:dash,
|
||
linecolor=:gray,
|
||
linewidth=1)
|
||
|
||
plot_file4 = joinpath(results_dir, "copy_cost_linear.png")
|
||
savefig(p4, plot_file4)
|
||
println("✓ Plot saved: $plot_file4")
|
||
|
||
# Plot 5: Speedup ratios (showing where immutable wins)
|
||
p5 = plot(bytes_vals, [r.speedup_access for r in results],
|
||
label="Field Access",
|
||
xlabel="Struct Size (bytes)",
|
||
ylabel="Speedup (Immutable / Mutable)",
|
||
title="Performance Speedup: Immutable vs Mutable",
|
||
linewidth=2,
|
||
marker=:circle,
|
||
legend=:right,
|
||
size=(800, 600))
|
||
plot!(p5, bytes_vals, [r.speedup_update for r in results],
|
||
label="Field Update",
|
||
linewidth=2,
|
||
marker=:square)
|
||
plot!(p5, bytes_vals, [r.speedup_iter for r in results],
|
||
label="Iteration",
|
||
linewidth=2,
|
||
marker=:diamond)
|
||
hline!(p5, [1.0],
|
||
label="Break-even",
|
||
linestyle=:dot,
|
||
linecolor=:black,
|
||
linewidth=2)
|
||
vline!(p5, [fem_small, fem_medium, fem_large],
|
||
label="",
|
||
linestyle=:dash,
|
||
linecolor=:gray,
|
||
linewidth=1)
|
||
annotate!(p5, fem_large, 0.5, text("Typical FEM range →", 8, :left))
|
||
|
||
plot_file5 = joinpath(results_dir, "speedup_ratios.png")
|
||
savefig(p5, plot_file5)
|
||
println("✓ Plot saved: $plot_file5")
|
||
|
||
println()
|
||
println("All plots saved to: $results_dir")
|
||
println()
|
||
|
||
# Save results to JSON
|
||
println("="^80)
|
||
println("SAVING DATA")
|
||
println("="^80)
|
||
println()
|
||
|
||
timestamp = Dates.format(now(), "yyyymmdd_HHMMSS")
|
||
output_dir = "benchmarks/results"
|
||
mkpath(output_dir)
|
||
|
||
# Prepare data for saving
|
||
benchmark_data = Dict(
|
||
"timestamp" => timestamp,
|
||
"julia_version" => string(VERSION),
|
||
"system" => Dict(
|
||
"cpu_model" => cpu_info[1].model,
|
||
"cpu_cores" => Sys.CPU_THREADS,
|
||
"cpu_speed_mhz" => cpu_info[1].speed,
|
||
"os" => string(Sys.KERNEL),
|
||
"machine" => string(Sys.MACHINE),
|
||
"word_size" => Sys.WORD_SIZE
|
||
),
|
||
"results" => [
|
||
Dict(
|
||
"nfields" => r.nfields,
|
||
"bytes" => r.bytes,
|
||
"mutable_access_ns" => r.mut_access * 1e9,
|
||
"immutable_access_ns" => r.imm_access * 1e9,
|
||
"speedup_access" => r.speedup_access,
|
||
"mutable_update_ns" => r.mut_update * 1e9,
|
||
"immutable_update_ns" => r.imm_update * 1e9,
|
||
"speedup_update" => r.speedup_update,
|
||
"mutable_iter_ns" => r.mut_iter * 1e9,
|
||
"immutable_iter_ns" => r.imm_iter * 1e9,
|
||
"speedup_iter" => r.speedup_iter,
|
||
"immutable_copy_ns" => r.imm_copy * 1e9
|
||
) for r in results
|
||
],
|
||
"analysis" => Dict(
|
||
"copy_slope_ns_per_field" => slope,
|
||
"copy_intercept_ns" => intercept,
|
||
"r_squared" => r_squared
|
||
)
|
||
)
|
||
|
||
json_file = joinpath(output_dir, "struct_scaling_$(timestamp).json")
|
||
open(json_file, "w") do f
|
||
JSON.print(f, benchmark_data, 2)
|
||
end
|
||
println("✓ Data saved to: $json_file")
|
||
println()
|
||
|
||
# Also save as CSV for easy plotting
|
||
csv_file = joinpath(output_dir, "struct_scaling_$(timestamp).csv")
|
||
open(csv_file, "w") do f
|
||
println(f, "nfields,bytes,mut_access_ns,imm_access_ns,speedup_access,mut_update_ns,imm_update_ns,speedup_update,mut_iter_ns,imm_iter_ns,speedup_iter,imm_copy_ns")
|
||
for r in results
|
||
println(f, "$(r.nfields),$(r.bytes),$(r.mut_access*1e9),$(r.imm_access*1e9),$(r.speedup_access),$(r.mut_update*1e9),$(r.imm_update*1e9),$(r.speedup_update),$(r.mut_iter*1e9),$(r.imm_iter*1e9),$(r.speedup_iter),$(r.imm_copy*1e9)")
|
||
end
|
||
end
|
||
println("✓ CSV saved to: $csv_file")
|
||
println()
|
||
|
||
println("="^80)
|
||
println("GENERATING PLOTS")
|
||
println("="^80)
|
||
println()
|
||
|
||
# Note: Using Plots from global environment
|
||
try
|
||
# Import from global environment
|
||
pushfirst!(LOAD_PATH, "@stdlib")
|
||
import Plots
|
||
|
||
# Set backend
|
||
Plots.gr()
|
||
|
||
# Extract data for plotting
|
||
bytes_sizes = [r.bytes for r in results]
|
||
|
||
# Plot 1: Field Access Performance
|
||
p1 = Plots.plot(bytes_sizes, [r.mut_access * 1e9 for r in results],
|
||
label="Mutable (Dict)", linewidth=2, marker=:circle,
|
||
xlabel="Struct Size (bytes)", ylabel="Time (nanoseconds)",
|
||
title="Field Access Performance",
|
||
legend=:topleft, xscale=:log10, grid=true)
|
||
Plots.plot!(p1, bytes_sizes, [r.imm_access * 1e9 for r in results],
|
||
label="Immutable (NamedTuple)", linewidth=2, marker=:square)
|
||
|
||
# Add typical FEM element size markers
|
||
Plots.vline!(p1, [40, 400], label="Typical FEM (5-50 fields)",
|
||
linestyle=:dash, linewidth=1, color=:gray)
|
||
|
||
Plots.savefig(p1, joinpath(output_dir, "field_access_$(timestamp).png"))
|
||
println("✓ Saved: field_access_$(timestamp).png")
|
||
|
||
# Plot 2: Field Update Performance
|
||
p2 = Plots.plot(bytes_sizes, [r.mut_update * 1e9 for r in results],
|
||
label="Mutable (Dict)", linewidth=2, marker=:circle,
|
||
xlabel="Struct Size (bytes)", ylabel="Time (nanoseconds)",
|
||
title="Field Update Performance",
|
||
legend=:topleft, xscale=:log10, grid=true)
|
||
Plots.plot!(p2, bytes_sizes, [r.imm_update * 1e9 for r in results],
|
||
label="Immutable (NamedTuple)", linewidth=2, marker=:square)
|
||
|
||
Plots.vline!(p2, [40, 400], label="Typical FEM (5-50 fields)",
|
||
linestyle=:dash, linewidth=1, color=:gray)
|
||
|
||
Plots.savefig(p2, joinpath(output_dir, "field_update_$(timestamp).png"))
|
||
println("✓ Saved: field_update_$(timestamp).png")
|
||
|
||
# Plot 3: Iteration Performance
|
||
p3 = Plots.plot(bytes_sizes, [r.mut_iter * 1e9 for r in results],
|
||
label="Mutable (Dict)", linewidth=2, marker=:circle,
|
||
xlabel="Struct Size (bytes)", ylabel="Time (nanoseconds)",
|
||
title="Field Iteration Performance",
|
||
legend=:topleft, xscale=:log10, yscale=:log10, grid=true)
|
||
Plots.plot!(p3, bytes_sizes, [r.imm_iter * 1e9 for r in results],
|
||
label="Immutable (NamedTuple)", linewidth=2, marker=:square)
|
||
|
||
Plots.vline!(p3, [40, 400], label="Typical FEM (5-50 fields)",
|
||
linestyle=:dash, linewidth=1, color=:gray)
|
||
|
||
Plots.savefig(p3, joinpath(output_dir, "iteration_$(timestamp).png"))
|
||
println("✓ Saved: iteration_$(timestamp).png")
|
||
|
||
# Plot 4: Speedup Factors
|
||
p4 = Plots.plot(bytes_sizes, [r.speedup_access for r in results],
|
||
label="Access Speedup", linewidth=2, marker=:circle,
|
||
xlabel="Struct Size (bytes)", ylabel="Speedup Factor (Immutable/Mutable)",
|
||
title="Performance Advantage of Immutable Elements",
|
||
legend=:right, xscale=:log10, grid=true)
|
||
Plots.plot!(p4, bytes_sizes, [r.speedup_update for r in results],
|
||
label="Update Speedup", linewidth=2, marker=:square)
|
||
Plots.plot!(p4, bytes_sizes, [r.speedup_iter for r in results],
|
||
label="Iteration Speedup", linewidth=2, marker=:diamond)
|
||
|
||
Plots.hline!(p4, [1.0], label="Break-even", linestyle=:dash, color=:black, linewidth=1)
|
||
Plots.vline!(p4, [40, 400], label="Typical FEM",
|
||
linestyle=:dash, linewidth=1, color=:gray)
|
||
|
||
Plots.savefig(p4, joinpath(output_dir, "speedup_factors_$(timestamp).png"))
|
||
println("✓ Saved: speedup_factors_$(timestamp).png")
|
||
|
||
# Plot 5: Copy Cost Scaling
|
||
p5 = Plots.plot(bytes_sizes, [r.imm_copy * 1e9 for r in results],
|
||
label="Measured", linewidth=2, marker=:circle,
|
||
xlabel="Struct Size (bytes)", ylabel="Copy Time (nanoseconds)",
|
||
title="Immutable Struct Copy Cost",
|
||
legend=:topleft, xscale=:log10, grid=true)
|
||
|
||
# Add linear fit
|
||
fitted = [intercept + slope * r.nfields for r in results]
|
||
Plots.plot!(p5, bytes_sizes, fitted,
|
||
label="Linear Fit ($(round(slope, digits=4)) ns/field)",
|
||
linewidth=2, linestyle=:dash)
|
||
|
||
Plots.vline!(p5, [40, 400], label="Typical FEM",
|
||
linestyle=:dash, linewidth=1, color=:gray)
|
||
|
||
Plots.savefig(p5, joinpath(output_dir, "copy_cost_$(timestamp).png"))
|
||
println("✓ Saved: copy_cost_$(timestamp).png")
|
||
|
||
# Combined plot
|
||
layout = Plots.@layout [a b; c d]
|
||
p_combined = Plots.plot(p1, p2, p3, p4, layout=layout, size=(1200, 900))
|
||
Plots.savefig(p_combined, joinpath(output_dir, "combined_$(timestamp).png"))
|
||
println("✓ Saved: combined_$(timestamp).png")
|
||
|
||
println()
|
||
println("All plots saved successfully!")
|
||
|
||
catch e
|
||
println("⚠ Could not generate plots (Plots.jl not available in global environment)")
|
||
println(" Error: $e")
|
||
println(" Install with: julia -e 'using Pkg; Pkg.add(\"Plots\")'")
|
||
end
|
||
|
||
println()
|
||
println("="^80)
|