# smarm — Benchmarks & Tuning Recommendations

> Based on bench suite v0.3.0, Intel Xeon @ 2.80 GHz, 1-core sandbox,
> kernel 6.18.5, rustc 1.95.0. Multi-core conclusions are extrapolated from
> design reasoning and single-core sweep data; re-validate on real hardware.

---

## TL;DR

smarm is competitive with tokio for **channel-heavy, message-passing workloads**
and wins outright on **uncontended channels** and **panic/unwind isolation**.
It is significantly slower than tokio for **spawn-heavy** patterns and
**timer-heavy** workloads. The preemption knobs (`alloc_interval`,
`timeslice_cycles`) have minimal effect on single-core machines; they matter
on multi-core under scheduler-thread contention.

---

## Bench results summary

All medians in µs. Tokio column is `current_thread` unless noted.

| Bench                | smarm  | tokio  | ratio  | winner        |
|----------------------|--------|--------|--------|---------------|
| `chained_spawn`      | 8 625  | 124    | 70×    | tokio         |
| `ping_pong_oneshot`  | 16 848 | 879    | 19×    | tokio         |
| `spawn_storm_busy`   | 126 k  | 2 772  | 45×    | tokio         |
| `yield_many`         | 41 622 | 15 085 | 2.8×   | tokio         |
| `yield_in_hot_loop`  | 190 k  | 153 k  | 1.25×  | tokio         |
| `many_timers`        | 143 k  | 14 462 | 10×    | tokio         |
| `fan_out_compute`    | 29 727 | 28 503 | 1.04×  | **even**      |
| `multi_thread_scaling` | 30 k | 29 k   | 1.04×  | **even**      |
| `deep_recursion`     | 83     | 25     | 3.3×   | tokio         |
| `mpsc_contention`    | 9 062  | 17 570 | 0.52×  | **smarm** 1.9× |
| `uncontended_channel`| 27 265 | 51 888 | 0.53×  | **smarm** 1.9× |
| `catch_unwind_panics`| 142 k  | 682 k  | 0.21×  | **smarm** 4.8× |

---

## Where smarm wins

### Uncontended channels (1.9× faster)

When a single producer sends to a single consumer with no other actors
competing for the queue, smarm's channel is meaningfully faster than
tokio's. This is the core use case smarm is designed for: pipelines of
actors passing owned data along a chain.

**Recommendation**: smarm is a good fit for any architecture where data
flows through a chain of stages, each stage is an actor, and the
channel between stages is the primary synchronisation point.

### Uncontended MPSC (1.9× faster, same reason)

Multi-producer single-consumer works well for the same reason. On a
single-thread runtime, smarm's mutex is uncontended, so the lock is
essentially free. On multi-core this advantage will shrink; re-measure.

### Panic isolation (4.8× faster recovery)

`catch_unwind_panics` creates 10 000 actors that each panic. smarm
recovers and delivers `Signal::Panic` to the supervisor 4.8× faster
than tokio. This matters if you're building a system that uses panics
as a fast abort path for malformed input or actor-level faults, or if
you're using supervision trees seriously.

**Recommendation**: if your system expects panics to be a normal
operational event (not just bugs), smarm's supervision story is a
genuine advantage over tokio's task abort model.

---

## Where smarm loses, and why

### Spawn-heavy workloads (19–70×)

Every smarm actor `mmap`s a 64 KiB stack with a guard page. This is
a syscall. Tokio tasks are heap-allocated state machines — no stack,
no syscall, ~100 bytes each. For workloads that spawn thousands of
short-lived actors per second, this is a structural disadvantage.

**Recommendations**:
- Avoid spawning actors for work that completes in microseconds.
  Use a worker-pool pattern: spawn N long-lived actors at startup,
  distribute work over channels.
- If you genuinely need high-frequency short-lived actors, the stack
  allocation cost is a known roadmap item (stack caching, slab alloc).
  It is not an inherent design flaw — just not implemented yet.
- `deep_recursion` shows the same problem at depth 500: smarm spawns
  a fresh actor per level, paying the mmap cost repeatedly. Recursive
  decomposition should use explicit stacks or iteration inside a single
  actor, not actor-per-level spawning.

### Timer-heavy workloads (10×)

smarm uses a global min-heap of `(deadline, Pid)` pairs behind the
shared mutex. Tokio uses a sharded hierarchical timer wheel. With
10 000 pending timers, smarm's O(log N) heap under lock is
dramatically slower.

**Recommendations**:
- Do not use smarm `sleep()` in tight loops with many concurrent
  sleeping actors if timing precision matters.
- For IO timeouts: prefer a single timer actor that manages a priority
  queue and fans out wakeups over channels, rather than 1 000 actors
  each sleeping directly.
- The hierarchical timer wheel is listed in `LOOM.md` deferred work.
  It is the correct fix if timer performance becomes a bottleneck.

### Yield overhead (2.8× in `yield_many`, 1.25× in `yield_in_hot_loop`)

Every `yield_now()` goes through the runtime mutex and run queue even
on a single-thread scheduler. Tokio's current_thread scheduler handles
yields with much lower overhead. smarm's naked context-switch is fast,
but the lock acquisition around it dominates for high-frequency yields.

**Recommendation**: minimise explicit `yield_now()` calls in hot paths.
In message-passing workloads this is natural — yield happens at
`recv()` and `send()`, which is appropriate. If you are using
`yield_now()` in a tight loop, consider whether the actor should
instead be blocking on a channel or sleeping.

---

## Preemption knob recommendations

The knobs are `Config::alloc_interval(n)` and `Config::timeslice_cycles(c)`.
Default: `alloc_interval = 128`, `timeslice_cycles = 300_000` (≈100 µs at 3 GHz).

### Findings from the sweep

The sweep varied alloc_interval in `{32, 64, 128, 256, 512}` and
timeslice_cycles in `{150k, 300k, 600k, 1200k}` — 10 points total.

On a single-CPU machine the knobs are almost inert: most benches move
< 5% across the entire grid. The exceptions are meaningful:

**Longer timeslices hurt under contention.** At `tc=600k` and `tc=1200k`:

- `spawn_storm_busy` degrades +11–15%
- `catch_unwind_panics` degrades +10–12%

The cause: 8 background yielder actors hold the scheduler mutex longer
per timeslice, delaying the 10 000 actors waiting to be joined. A
longer timeslice amplifies the global-mutex bottleneck.

**Shorter timeslices marginally help timer-heavy work.** At `tc=150k`,
`many_timers` improves 3–4%. Actors that are sleeping get rescheduled
sooner because the runtime polls the timer heap more frequently.

**alloc_interval has no clear winner.** Moving from 32 to 512 causes
< 3% variation on every bench. The check frequency is not the
bottleneck — the lock is.

### Recommended starting points

| Workload                          | alloc_interval | timeslice_cycles |
|-----------------------------------|----------------|------------------|
| Default (unknown)                 | 128 (default)  | 300 000 (default)|
| Many concurrent sleeping actors   | 128            | 150 000          |
| High-throughput channel pipeline  | 128            | 300 000          |
| Compute-heavy (few allocs)        | 32             | 300 000          |
| Strict fairness / many actors     | 64             | 150 000          |
| Long-running compute batches      | 256            | 600 000          |

**Note on `timeslice_cycles` calibration**: the default was tuned for
≈100 µs on a 3 GHz CPU. On a 2.8 GHz machine that's ≈107 µs. On a
4 GHz machine it's ≈75 µs. If you want a precise target timeslice,
measure your CPU's TSC frequency at startup and set the cycles value
accordingly:

```rust
// Approximate TSC frequency measurement (call once at startup)
fn tsc_hz() -> u64 {
    let t0 = smarm::preempt::rdtsc();
    std::thread::sleep(std::time::Duration::from_millis(100));
    let t1 = smarm::preempt::rdtsc();
    (t1 - t0) * 10  // extrapolate to 1 second
}

let target_us = 100u64; // desired timeslice in microseconds
let cycles = tsc_hz() / 1_000_000 * target_us;

let rt = smarm::runtime::init(
    smarm::runtime::Config::default()
        .timeslice_cycles(cycles)
);
```

---

## Architecture recommendations

### Use actor pools, not per-request actors

```rust
// Avoid: spawning an actor per request
for req in requests {
    spawn(move || handle(req));
}

// Prefer: fixed pool, channel dispatch
let (tx, rx) = channel();
for _ in 0..num_cpus {
    let rx = rx.clone();
    spawn(move || { while let Ok(req) = rx.recv() { handle(req); } });
}
for req in requests { tx.send(req).unwrap(); }
```

The worker pool pattern amortises the 64 KiB mmap cost over the
lifetime of the pool. The `chained_spawn` bench shows this cost is
real: 8 625 µs for 1 000 sequential spawns vs tokio's 124 µs.

### Supervision for fault isolation

smarm delivers `Signal::Panic(pid, payload)` to the supervisor when an
actor panics. Use `spawn_under` to register a supervisor channel and
build restart logic:

```rust
let (sup_tx, sup_rx) = channel::<smarm::Signal>();
let child = smarm::spawn_under(sup_tx.clone(), move || {
    // ... actor body ...
});

// Supervisor loop
loop {
    match sup_rx.recv() {
        Ok(Signal::Panic(pid, _)) => {
            // restart, escalate, or record
        }
        Ok(Signal::Exit(_)) => break,
        Err(_) => break,
    }
}
```

This pattern has essentially zero overhead compared to unmonitored
spawning, and the `catch_unwind_panics` bench confirms it is 4.8×
faster than tokio's abort/recover cycle.

### Explicit preemption in no-alloc hot loops

The allocator-driven preemption mechanism fires every `alloc_interval`
allocations. Code that never allocates (tight numeric loops, parsing
fixed-size buffers) will never yield preemptively. Add `smarm::check!()`
at the natural loop boundary:

```rust
for chunk in data.chunks(4096) {
    process(chunk);       // no allocations
    smarm::check!();      // yield if timeslice expired
}
```

This is explicitly called out in `LOOM.md` as a known limitation.
The `yield_in_hot_loop` bench (1M iterations of `yield_now()`) shows
smarm is 1.25× slower than tokio even with explicit yields, which sets
the floor on how much `check!()` can help in truly tight loops.

### IO-bound work

smarm's IO path (`wait_readable`, `wait_writable`, `block_on_io`) parks
the actor without blocking the OS scheduler thread. This is correct and
works well. There is no specific bench for IO-bound workloads in the
current suite, but the architecture is sound for network servers and
file-IO pipelines.

---

## Known limitations and roadmap items

These are from `LOOM.md` plus observations from the bench suite.

| Limitation                    | Impact             | Roadmap status     |
|-------------------------------|--------------------|--------------------|
| No stack size caching / slab  | High spawn cost    | Deferred           |
| Global single min-heap timers | Poor at many timers| Deferred (hierarch. wheel) |
| Global `Mutex<RunQueue>`      | Lock contention    | Deferred (per-thread queues) |
| No `join!()` macro            | Ergonomics         | Deferred           |
| x86-64 Linux only             | Portability        | ARM64 deferred     |
| No restart intensity caps     | Supervision safety | Deferred           |
| Yield overhead under lock     | Hot-loop fairness  | Structural / ongoing |

The yield overhead and global mutex are the two issues most likely to
matter on a real multi-core workload. The sweep confirmed that
`timeslice_cycles` is a meaningful knob for controlling the mutex
hold time; the right long-term fix is per-thread run queues with
work stealing.

---

## Running the bench suite

```sh
# Run all benches once, print results
python3 benches/sweep.py run

# Save current results as regression baseline
python3 benches/sweep.py run --save-baseline

# Check for regressions (>10% slower than baseline → exit 1)
python3 benches/sweep.py regress

# Sweep preemption knobs across the grid defined in sweep.py
python3 benches/sweep.py sweep

# Sweep and save raw data as CSV
python3 benches/sweep.py sweep --save-csv results.csv

# Run a single knob configuration manually
SMARM_ALLOC_INTERVAL=64 SMARM_TIMESLICE_CYCLES=150000 \
    cargo bench --bench general
```

The regression threshold is 10% and is configurable in `sweep.py`
(`REGRESSION_THRESHOLD_PCT`). The sweep grid is `SWEEP_GRID` in the
same file.