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https://github.com/msberends/AMR.git
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d770469a63
Previous single-panel benchmark was misleading: the first sequential run
paid one-time cache-warm-up cost (skewing n=20), and only 6 columns were
used so only 6 cores were ever active on a 16-core machine.
New two-panel design:
Left – vary rows with 16 fixed AB columns (shows memory-bandwidth
saturation for large n)
Right – vary columns with fixed rows (shows the real speedup profile:
parallel wins when n_cols >> 1)
Also adds a warm-up pass before measurements to eliminate first-call bias.
https://claude.ai/code/session_012DXCXbZUC54Zij1z9bFiHR
116 lines
4.9 KiB
R
116 lines
4.9 KiB
R
# Benchmark: sequential vs parallel as.sir() across data-set shapes
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#
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# Run from the repo root:
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# Rscript tools/benchmark_parallel.R
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# or inside an R session:
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# source("tools/benchmark_parallel.R")
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#
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# Two panels:
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# Left – fixed columns (n_ab_fixed), varying rows.
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# Parallel wins at small n; sequential catches up at large n due to
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# memory-bandwidth saturation (all workers compete for the same
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# clinical_breakpoints lookup table in L3 cache / RAM).
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# Right – fixed rows (n_rows_fixed), varying column count.
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# This is the shape that actually benefits: each additional column
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# keeps another core busy. The "real world" gain for a 2854×65
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# dataset lives here.
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#
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# Requires ggplot2; uses devtools::load_all() so the package need not be
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# installed.
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devtools::load_all(".", quiet = TRUE)
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# ── configuration ─────────────────────────────────────────────────────────────
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row_sizes <- c(200, 1000, 5000, 20000)
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col_sizes <- c(4, 8, 16, 32, 48)
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n_rows_fixed <- 1000
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n_ab_fixed <- 16
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n_cores_avail <- AMR:::get_n_cores(Inf)
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all_abs <- c("AMC", "GEN", "CIP", "TZP", "IPM", "MEM",
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"AMP", "TMP", "SXT", "NIT", "FOX", "CRO",
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"FEP", "CAZ", "CTX", "TOB", "AMK", "ERY",
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"AZM", "CLI", "VAN", "TEC", "RIF", "MTR",
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"MFX", "LNZ", "TGC", "DOX", "FLC", "OXA",
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"PEN", "CXM", "CZO", "KAN", "COL", "FOS",
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"MUP", "TCY", "TEC", "IPM", "CHL", "FEP",
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"MEM", "TZP", "GEN", "AMC", "AMX", "AMP")
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all_abs <- unique(all_abs)
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mic_vals <- c("0.25", "0.5", "1", "2", "4", "8", "16", "32")
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make_df <- function(n_rows, n_ab) {
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set.seed(42)
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ab_sel <- all_abs[seq_len(min(n_ab, length(all_abs)))]
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mics <- lapply(ab_sel, function(a) as.mic(sample(mic_vals, n_rows, TRUE)))
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names(mics) <- ab_sel
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data.frame(mo = "B_ESCHR_COLI", mics, stringsAsFactors = FALSE)
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}
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time_both <- function(n_rows, n_ab, label) {
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df <- make_df(n_rows, n_ab)
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t_seq <- system.time(
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suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = FALSE))
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)[["elapsed"]]
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t_par <- system.time(
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suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = TRUE))
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)[["elapsed"]]
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message(sprintf("%-28s seq=%5.2fs par=%5.2fs speedup=%.1fx",
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label, t_seq, t_par, t_seq / t_par))
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data.frame(group = label, mode = c("sequential", "parallel"),
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seconds = c(t_seq, t_par), stringsAsFactors = FALSE)
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}
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# ── warm-up (avoid first-call overhead biasing results) ───────────────────────
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message("Warming up cache ...")
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invisible(suppressMessages(as.sir(make_df(100, 6), col_mo = "mo", info = FALSE)))
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invisible(suppressMessages(as.sir(make_df(100, 6), col_mo = "mo", info = FALSE, parallel = TRUE)))
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sir_interpretation_history(clean = TRUE)
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# ── panel 1: vary rows, fixed columns ─────────────────────────────────────────
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message(sprintf("\nPanel 1 – varying rows, %d fixed columns:", n_ab_fixed))
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res_rows <- do.call(rbind, lapply(row_sizes, function(n) {
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time_both(n, n_ab_fixed, sprintf("rows=%d", n))
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}))
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res_rows$x <- rep(row_sizes, each = 2)
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res_rows$panel <- "Vary rows (16 fixed AB columns)"
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# ── panel 2: vary columns, fixed rows ─────────────────────────────────────────
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message(sprintf("\nPanel 2 – varying columns, %d fixed rows:", n_rows_fixed))
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res_cols <- do.call(rbind, lapply(col_sizes, function(n_ab) {
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time_both(n_rows_fixed, n_ab, sprintf("cols=%d", n_ab))
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}))
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res_cols$x <- rep(col_sizes, each = 2)
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res_cols$panel <- sprintf("Vary columns (%d fixed rows)", n_rows_fixed)
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results <- rbind(res_rows, res_cols)
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if (requireNamespace("ggplot2", quietly = TRUE)) {
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p <- ggplot2::ggplot(
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results,
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ggplot2::aes(x = x, y = seconds, colour = mode, group = mode)
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) +
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ggplot2::geom_line(linewidth = 1) +
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ggplot2::geom_point(size = 2.5) +
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ggplot2::facet_wrap(~panel, scales = "free_x") +
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ggplot2::scale_colour_manual(
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values = c(sequential = "#E05C5C", parallel = "#2E86AB")
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) +
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ggplot2::labs(
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title = "as.sir() throughput: sequential vs parallel",
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subtitle = sprintf("E. coli, EUCAST 2026, %d cores available", n_cores_avail),
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x = "Dataset dimension (rows ·left· or columns ·right·)",
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y = "Wall-clock time (seconds)",
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colour = NULL
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) +
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ggplot2::theme_minimal(base_size = 12) +
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ggplot2::theme(legend.position = "top")
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out_file <- "tools/benchmark_parallel.png"
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ggplot2::ggsave(out_file, p, width = 10, height = 5, dpi = 150)
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message("\nPlot saved to ", out_file)
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} else {
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message("Install ggplot2 to get a plot; raw results:")
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print(results[, c("panel", "group", "mode", "seconds")])
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}
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