feat(causal): native causal profiling behind smarm-causal (RFC 007 v1)
causal_site! scoped site guards per actor slot, progress! throughput points, and a Coz-style virtual-speedup engine hooked into maybe_preempt's amortized cold block: target-site samples grow a global delay ledger; bystanders spin-absorb their debt at the next causal check, with timeslice extension so injected delay is not charged against the slice. Resume credit (Coz's blocked-thread rule) is gated on a causal_parked slot bit set only by a real park: crediting on every resume made any yield-cadence actor delay-immune and every experiment inert (found live on a 24-core run — dead-flat deltas across all sites). Report normalization uses a measured TSC frequency (~50ms calibration on first use) instead of the crate-wide 3 GHz assumption, which uniformly inflated impact numbers on a 3.7 GHz box. impact_pct() is the machine-readable form of the summary for programmatic checks. examples/causal_pipeline.rs burns fixed *work* (calibrated LCG loop), not fixed wall time — a timed busy-wait absorbs injected delay into its own budget and reads as a no-op. Self-checking: exits nonzero if causal separation fails; skips the verdict below 4 cores. Validated on a 24-core box: reserve (true bottleneck) +29.3%@25/+83.5%@50; serialize and background-compaction ~0%. Known v1 gaps (jar): timer-heap deadlines unshifted, no-check!/no-alloc actors undelayable, multi-scheduler coherence best-effort Relaxed, off-CPU blame punted, Instant::now() uncorrected. Zero-cost with the feature off; clippy -D warnings clean both ways; full suite green with and without smarm-causal.
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Claude (sandbox)
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//! Causal-profiling demo (RFC 007): a pipeline where conventional profiling
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//! lies and causal profiling doesn't.
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//!
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//! producer --(serialize ~200µs/item)--> reserve --(~400µs/item)--> notify
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//! background: an actor burning CPU constantly, fully off the critical path
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//!
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//! `reserve` is the true bottleneck. `serialize` is hot but overlapped with
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//! `reserve`'s backlog, and `background` is the hottest code in the process
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//! while contributing nothing to throughput. A cycle profiler ranks them
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//! background > reserve ≈ 2×serialize; the causal report instead shows
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//! throughput responding to virtual speedups of `reserve` and (near-)ignoring
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//! `serialize` and `background`.
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//!
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//! Stage cost is fixed *work* (a calibrated arithmetic loop), not fixed wall
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//! time. This matters: a timed busy-wait absorbs injected causal delay into
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//! its own budget and finishes on schedule regardless, making every
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//! experiment read as a no-op (found live on a 24-core run: dead-flat
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//! deltas). Real workloads are work-shaped, so the demo must be too.
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//!
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//! Run:
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//! cargo run --release --example causal_pipeline --features smarm-causal
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//!
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//! Prints a summary, writes `profile.coz` (Coz plot-compatible), and — given
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//! enough cores for the pipeline to actually run in parallel — checks the
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//! expected separation and exits nonzero if it doesn't hold, so a CI box can
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//! run this as a smoke test.
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use std::sync::atomic::{AtomicBool, Ordering};
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use std::sync::Arc;
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use std::time::{Duration, Instant};
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/// LCG-mix `iters` times in dependent sequence (unvectorizable, un-elidable),
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/// staying preemptible — and causal-sampleable/delayable — via `check!()`.
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fn work_iters(iters: u64) {
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let mut acc = 0x2545_f491_4f6c_dd1du64;
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let mut i = 0u64;
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while i < iters {
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let chunk_end = (i + 256).min(iters);
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while i < chunk_end {
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acc = acc.wrapping_mul(6364136223846793005).wrapping_add(i);
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i += 1;
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}
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std::hint::black_box(acc);
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smarm::check!();
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}
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}
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/// Measure how many `work_iters` iterations fit in a microsecond on this
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/// machine, so stage costs below are meaningful in time while staying
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/// work-shaped.
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fn calibrate_iters_per_us() -> u64 {
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let n = 8_000_000u64;
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let t = Instant::now();
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work_iters(n);
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(n / (t.elapsed().as_micros().max(1) as u64)).max(1)
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}
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fn main() {
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let per_us = calibrate_iters_per_us();
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println!("calibration: {per_us} work iters/µs");
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let work_us = move |us: u64| work_iters(us * per_us);
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let mut failures: Vec<String> = Vec::new();
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smarm::init(smarm::Config::default()).run(move || {
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let stop = Arc::new(AtomicBool::new(false));
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let (tx_ab, rx_ab) = smarm::channel::<u64>();
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let (tx_bc, rx_bc) = smarm::channel::<u64>();
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// Producer: hot serialization, but upstream of the bottleneck.
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let stop_p = stop.clone();
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let producer = smarm::spawn(move || {
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let mut i = 0u64;
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while !stop_p.load(Ordering::Relaxed) {
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{
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let _g = smarm::causal_site!("serialize");
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work_us(200);
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}
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if tx_ab.send(i).is_err() {
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break;
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}
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i += 1;
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}
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// tx_ab drops here; downstream drains and exits.
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});
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// Reserve: the true bottleneck (~400µs of work per item).
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let reserve = smarm::spawn(move || {
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while let Ok(item) = rx_ab.recv() {
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{
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let _g = smarm::causal_site!("reserve");
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work_us(400);
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}
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if tx_bc.send(item).is_err() {
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break;
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}
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}
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});
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// Notify: light tail stage; marks the unit of useful work.
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let notify = smarm::spawn(move || {
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while rx_bc.recv().is_ok() {
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{
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let _g = smarm::causal_site!("notify");
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work_us(50);
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}
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smarm::progress!("orders-processed");
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}
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});
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// Background: hottest code in the process, zero throughput relevance.
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let stop_bg = stop.clone();
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let background = smarm::spawn(move || {
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while !stop_bg.load(Ordering::Relaxed) {
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let _g = smarm::causal_site!("background-compaction");
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work_us(500);
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}
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});
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// Warm up so queues reach steady state before measuring.
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smarm::sleep(Duration::from_millis(300));
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let results = smarm::causal::run_experiments(&smarm::causal::ExperimentPlan {
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speedups_pct: vec![0, 25, 50],
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experiment: Duration::from_millis(700),
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cooldown: Duration::from_millis(150),
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});
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stop.store(true, Ordering::Relaxed);
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producer.join().unwrap();
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reserve.join().unwrap();
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notify.join().unwrap();
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background.join().unwrap();
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print!("{}", smarm::causal::render_summary(&results));
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let coz = smarm::causal::render_coz(&results);
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match std::fs::write("profile.coz", coz) {
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Ok(()) => println!("\nwrote profile.coz"),
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Err(e) => eprintln!("\nfailed to write profile.coz: {e}"),
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}
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// Verdict. The separation only exists when the four pipeline actors
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// actually run in parallel; on a small box, report and skip.
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let cores = std::thread::available_parallelism().map(|n| n.get()).unwrap_or(1);
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if cores < 4 {
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println!("verdict: SKIPPED ({cores} cores; separation needs the stages in parallel)");
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return;
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}
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let impact = |site: &str| {
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smarm::causal::impact_pct(&results, site, 25, "orders-processed")
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};
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let mut expect = |site: &str, ok: &dyn Fn(f64) -> bool, want: &str| match impact(site) {
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Some(p) => {
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let verdict = if ok(p) { "ok" } else { "FAIL" };
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println!("verdict: {site} @25% -> {p:+.1}% (want {want}) {verdict}");
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if !ok(p) {
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failures.push(format!("{site}: {p:+.1}% (want {want})"));
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}
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}
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None => {
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println!("verdict: {site} @25% -> missing cell FAIL");
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failures.push(format!("{site}: missing cell"));
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}
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};
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expect("reserve", &|p| p > 15.0, "> +15%");
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expect("serialize", &|p| p < 10.0, "< +10%");
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expect("background-compaction", &|p| p < 10.0, "< +10%");
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if failures.is_empty() {
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println!("verdict: PASS — causal separation holds");
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} else {
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println!("verdict: FAIL — {}", failures.join("; "));
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std::process::exit(1);
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}
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});
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}
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