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m3ultra	27564e80eb	perfcheck: nightly fleet GPU canary + the contention lessons that shaped it BENCHMARKS.md says what a model cost the day it was measured; nothing noticed if a macOS/torch update or a thermal fault halved a node. perfcheck runs the whole fleet in ~35s off godcheck's 03:30 cron on the m4mini and reports drift into GODCHECK_LATEST.md. Probes matmul (fp16/fp32), memory bandwidth, and SDPA at head_dim 64 vs 56 — the latter turning CorridorKey's fast-path cliff into a permanent canary: it confirms the padding win fleet-wide (1.96x-5.26x) and tells us if a future torch closes it. Runs on venvs/rmbg/bin/python, already identical fleet-wide, so nothing new is installed (nothing lands on the disk-tight m1max). First cross-machine capability table for all 6 nodes. The M3 Ultra is ~2.2x the M1 Ultra on fp16 matmul, but they share ~625 GB/s — so bandwidth-bound stages run alike while compute-bound ones scale. Both Ultras reach only ~78% of spec bandwidth on a single kernel; the smaller Macs hit ~88%. Measuring a fleet that is doing real work is the whole problem, and naive benchmarking here is off by 11x: - min, not median: a concurrent trellis_mac job dragged a median-of-5 matmul from ~24500 to ~2150 GFLOP/s, which reads exactly like a catastrophic regression. - n=4096 not 2048: 2048 is dispatch-bound and swung 48% run-to-run; 4096 reproduces to 0.1% even while contended. - sdpa 16x2048 not 8x1024: sub-ms probes are dispatch noise — 8x1024 gave ratios of 0.79/3.95/2.35 on three runs of one machine, the first "proving" 56 is faster. - busy nodes are excluded, not blamed: GPU contention is invisible to load average (M3 Ultra read load 2.45 with its GPU pinned), so bench.py samples ioreg GPU% before it touches the GPU — our own matmul pins the device, so ordering is the trick. - baselines are the median of recent history, not a saved best: the M4 Pro also serves Ollama and is bimodal (~3200 vs ~5500 fp32), so a best-observed baseline pins to a lucky outlier and alerts forever. - a regression must repeat before it is believed ([~] watching -> [!] CONFIRMED). Validated by re-running the fleet against its own baselines: zero false alarms, including a sweep where the M3 Ultra read 43 GB/s under load and was correctly marked BUSY rather than reported as a 93% regression. Also found: the m4mini is the only node with Tailscale SSH (RunSSH: true) and it does NOT propagate remote exit codes — `ssh m4mini "exit 7"` returns 0, so any `if ssh m4mini ...` test silently always passes. Test on output instead, which is what godcheck already does (and why it is unaffected). It also cannot ssh to itself, so run_fleet detects its own tailnet IP and benches the local node via the shell. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-17 18:03:03 +10:00

Author

SHA1

Message

Date

m3ultra

27564e80eb

perfcheck: nightly fleet GPU canary + the contention lessons that shaped it

BENCHMARKS.md says what a model cost the day it was measured; nothing noticed if a
macOS/torch update or a thermal fault halved a node. perfcheck runs the whole fleet
in ~35s off godcheck's 03:30 cron on the m4mini and reports drift into
GODCHECK_LATEST.md.

Probes matmul (fp16/fp32), memory bandwidth, and SDPA at head_dim 64 vs 56 — the
latter turning CorridorKey's fast-path cliff into a permanent canary: it confirms
the padding win fleet-wide (1.96x-5.26x) and tells us if a future torch closes it.
Runs on venvs/rmbg/bin/python, already identical fleet-wide, so nothing new is
installed (nothing lands on the disk-tight m1max).

First cross-machine capability table for all 6 nodes. The M3 Ultra is ~2.2x the M1
Ultra on fp16 matmul, but they share ~625 GB/s — so bandwidth-bound stages run alike
while compute-bound ones scale. Both Ultras reach only ~78% of spec bandwidth on a
single kernel; the smaller Macs hit ~88%.

Measuring a fleet that is doing real work is the whole problem, and naive
benchmarking here is off by 11x:

- min, not median: a concurrent trellis_mac job dragged a median-of-5 matmul from
  ~24500 to ~2150 GFLOP/s, which reads exactly like a catastrophic regression.
- n=4096 not 2048: 2048 is dispatch-bound and swung 48% run-to-run; 4096 reproduces
  to 0.1% even while contended.
- sdpa 16x2048 not 8x1024: sub-ms probes are dispatch noise — 8x1024 gave ratios of
  0.79/3.95/2.35 on three runs of one machine, the first "proving" 56 is faster.
- busy nodes are excluded, not blamed: GPU contention is invisible to load average
  (M3 Ultra read load 2.45 with its GPU pinned), so bench.py samples ioreg GPU% before
  it touches the GPU — our own matmul pins the device, so ordering is the trick.
- baselines are the median of recent history, not a saved best: the M4 Pro also serves
  Ollama and is bimodal (~3200 vs ~5500 fp32), so a best-observed baseline pins to a
  lucky outlier and alerts forever.
- a regression must repeat before it is believed ([~] watching -> [!] CONFIRMED).

Validated by re-running the fleet against its own baselines: zero false alarms,
including a sweep where the M3 Ultra read 43 GB/s under load and was correctly
marked BUSY rather than reported as a 93% regression.

Also found: the m4mini is the only node with Tailscale SSH (RunSSH: true) and it does
NOT propagate remote exit codes — `ssh m4mini "exit 7"` returns 0, so any
`if ssh m4mini ...` test silently always passes. Test on output instead, which is
what godcheck already does (and why it is unaffected). It also cannot ssh to itself,
so run_fleet detects its own tailnet IP and benches the local node via the shell.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-07-17 18:03:03 +10:00

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