216 lines
8.4 KiB
Julia
216 lines
8.4 KiB
Julia
# test/benchmark_v3.jl
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# ===================================================================
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# High-Precision Performance Benchmark Suite (v3)
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# ===================================================================
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# This script integrates the robust statistical sampling of `BenchmarkTools`
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# with the visual comparative mechanics against the legacy library.
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# It captures the paradigm shift from "Time per slice" to "Pipeline Throughput".
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# ===================================================================
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using Pkg
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Pkg.activate(joinpath(@__DIR__, ".."))
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using BenchmarkTools
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using DataFrames
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using CSV
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using CairoMakie
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using Statistics
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using MSI_src
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using julia_mzML_imzML
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struct BenchmarkCase
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filepath::String
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mz_value::Float64
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mz_tolerance::Float64
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name::String
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end
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const RESULTS_DIR = joinpath(@__DIR__, "results")
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# List to benchmark
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const BENCHMARK_CASES = [
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/Chilli/ltpmsi-chilli.imzML", 420.0, 0.1, "Chilli Pepper"),
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/imzML_DESI/40TopL,10TopR,30BottomL,20BottomR/40TopL,10TopR,30BottomL,20BottomR-centroid.imzML", 885.5, 0.1, "Colon Cancer Human"),
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/imzML_AP_SMALDI/HR2MSImouseurinarybladderS096.imzML", 716.053, 0.1, "Mouse Urinary Bladder"),
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/Leafs/CE1_Leaf_R3.imzML", 306.1, 0.1, "Leaf"),
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/Liv2_imzML_TIMSConvert-selected/Liv2.imzML", 796.18, 0.1, "Liver Cut"),
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BenchmarkCase("/home/pixel/Documents/Cinvestav_2025/Analisis/salida_Tims/Stomach_DHB.imzML", 804.3, 0.1, "Mouse Stomach 4GB")
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]
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function get_total_file_size_mb(filepath::String)
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imzml_size = isfile(filepath) ? filesize(filepath) : 0
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ibd_path = replace(filepath, r"\.(imzML|imzml)$" => ".ibd")
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ibd_size = isfile(ibd_path) ? filesize(ibd_path) : 0
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return round((imzml_size + ibd_size) / 1024^2, digits=2)
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end
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function flush_memory()
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GC.gc(true)
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if Sys.islinux()
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# Force the OS to reclaim memory from the glibc allocator
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ccall(:malloc_trim, Int32, (Int32,), 0)
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end
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end
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function run_v3_benchmarks()
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mkpath(RESULTS_DIR)
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results = DataFrame()
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println("="^60)
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println("STARTING ENTERPRISE BENCHMARK SUITE (v3)")
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println("="^60)
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for case in BENCHMARK_CASES
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if !isfile(case.filepath)
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@warn "File not found: $(case.filepath). Skipping."
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continue
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end
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println("\n--- Target: $(case.name) ---")
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file_size = get_total_file_size_mb(case.filepath)
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# ------------------------------------------------------------
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# 1. JuliaMSI (New Architecture)
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# ------------------------------------------------------------
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println("[JuliaMSI - New Engine]")
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flush_memory()
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# Load Phase (Metadata Only / Memory Mapping)
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load_stats_new = @timed OpenMSIData(case.filepath)
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msi_data = load_stats_new.value
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load_time_new_s = load_stats_new.time
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mem_load_new_mb = load_stats_new.bytes / 1024^2
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# Slicing Phase (High Precision)
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b_slice_new = @benchmark get_mz_slice($msi_data, $(case.mz_value), $(case.mz_tolerance)) samples=10 seconds=5
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mean_time_new_s = mean(b_slice_new.times) / 1e9
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# Calculate Amortized Throughput (10 slices)
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# Includes the 'Loading Wall' penalty
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total_time_10_new = load_time_new_s + (10 * mean_time_new_s)
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amortized_sps_new = 10.0 / total_time_10_new
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close(msi_data)
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flush_memory()
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# ------------------------------------------------------------
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# 2. julia_mzML_imzML (Legacy Architecture)
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# ------------------------------------------------------------
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println("[julia_mzML_imzML - Legacy]")
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load_stats_old = try
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@timed LoadImzml(case.filepath)
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catch e
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@warn "Legacy load failed: $e"
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(time=Inf, bytes=Inf, value=nothing)
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end
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old_data = load_stats_old.value
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mem_load_old_mb = load_stats_old.bytes / 1024^2
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load_time_old_s = Inf
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mean_time_old_s = Inf
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amortized_sps_old = 0.0
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if old_data !== nothing
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b_slice_old = try
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# Legacy can be extremely slow, constrain it heavily
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@benchmark GetSlice($old_data, $(case.mz_value), $(case.mz_tolerance)) samples=3 seconds=10
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catch e
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@warn "Legacy slice failed: $e"
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nothing
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end
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if b_slice_old !== nothing
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mean_time_old_s = mean(b_slice_old.times) / 1e9
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# Accurately reflect the massive load time in the throughput
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load_time_old_s = load_stats_old.time
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total_time_10_old = load_time_old_s + (10 * mean_time_old_s)
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amortized_sps_old = 10.0 / total_time_10_old
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end
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end
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flush_memory()
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# ------------------------------------------------------------
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# Record
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# ------------------------------------------------------------
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push!(results, (
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Dataset = case.name,
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FileSize_MB = file_size,
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# Legacy Metrics
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Legacy_LoadMem_MB = mem_load_old_mb,
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Legacy_LoadTime_s = load_stats_old.time,
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Legacy_Amortized_SPS_10 = amortized_sps_old,
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# New Metrics
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New_LoadMem_MB = mem_load_new_mb,
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New_LoadTime_s = load_time_new_s,
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New_Amortized_SPS_10 = amortized_sps_new,
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# Competitive Deltas
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RAM_Reduction_Pct = isfinite(mem_load_old_mb) ? ((mem_load_old_mb - mem_load_new_mb) / mem_load_old_mb)*100 : NaN,
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UX_Speedup_Factor = isfinite(amortized_sps_old) ? (amortized_sps_new / amortized_sps_old) : NaN
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))
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println(" > RAM Reduction: $(round(results[end, :RAM_Reduction_Pct], digits=2))%")
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println(" > Amortized Throughput (10 Slices): $(round(amortized_sps_old, digits=2)) -> $(round(amortized_sps_new, digits=2)) slices/sec")
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end
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csv_path = joinpath(RESULTS_DIR, "v3_enterprise_benchmarks.csv")
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CSV.write(csv_path, results)
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println("\nData saved to $csv_path")
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plot_v3_results(results)
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return results
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end
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function plot_v3_results(df::DataFrame)
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if isempty(df) return end
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fig = Figure(size=(1800, 1200), fontsize=24)
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x_pos = 1:nrow(df)
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labels = df.Dataset
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# ---------- Plot 1: The RAM Revolution ----------
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ax1 = Axis(fig[1, 1],
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title="Initial RAM Cost (Loading & Mmap)",
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ylabel="Memory (MB)",
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xticks=(x_pos, labels), xticklabelrotation=π/8)
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barplot!(ax1, x_pos .- 0.2, df.New_LoadMem_MB, color="#10b981", width=0.4, label="JuliaMSI (Mmap Lazy)")
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barplot!(ax1, x_pos .+ 0.2, df.Legacy_LoadMem_MB, color="#ef4444", width=0.4, label="Legacy (Dense Matrix)")
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axislegend(ax1, position=:lt)
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# ---------- Plot 2: Throughput Leap ----------
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ax2 = Axis(fig[1, 2],
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title="Amortized Throughput (10 Slices - Higher is Better)",
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ylabel="Slices / Second (Inc. Load Time)",
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xticks=(x_pos, labels), xticklabelrotation=π/8)
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barplot!(ax2, x_pos .- 0.2, df.New_Amortized_SPS_10, color="#10b981", width=0.4, label="JuliaMSI")
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barplot!(ax2, x_pos .+ 0.2, df.Legacy_Amortized_SPS_10, color="#ef4444", width=0.4, label="Legacy")
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axislegend(ax2, position=:lt)
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# ---------- Plot 3: The Scaling Wall (File Size vs Throughput) ----------
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ax3 = Axis(fig[2, 1:2],
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title="Performance Scaling: UX Throughput vs Dataset Size",
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xlabel="Dataset File Size (MB)",
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ylabel="Amortized Throughput (Slices/sec)")
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scatterlines!(ax3, df.FileSize_MB, df.New_Amortized_SPS_10, color="#10b981", markersize=15, linewidth=4, label="JuliaMSI Engine")
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scatterlines!(ax3, df.FileSize_MB, df.Legacy_Amortized_SPS_10, color="#ef4444", markersize=15, linewidth=4, label="Legacy Engine")
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axislegend(ax3, position=:rt)
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save(joinpath(RESULTS_DIR, "v3_enterprise_dashboard.png"), fig)
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println("\nDashboards saved successfully.")
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end
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# Ensure we process if run as the main script
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if abspath(PROGRAM_FILE) == @__FILE__
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run_v3_benchmarks()
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end
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