bend: cuda-sim-ops-bin handler + per-call CPU/GPU telemetry

gpu-worker.lsp gains a cuda-sim-ops-bin op handler that spawns
demo_ops from www.foxhop.net/ecdsa/cuda via spawn-process-stdio,
drains stdout, & parses our (cuda-sim-result ...) portal back.

Each call now emits two log lines:
;;; bend RECV cuda-sim-ops-bin ops=PATH n-batches=N t-ms=...
;;; bend DONE cuda-sim-ops-bin n-batches=N wall-ms=W cpu-ms=C gpu-ms=G mismatches=0 gpu/cpu=R

so we can tell how fast bend jobs run on CPU vs GPU per call.

CLAUDE.md & www/index.html mention this integration is now live
end-to-end across our fleet.

2026-06-05 10:18:11 -04:00

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Agent Blackops — Lumbda repo

Agent blackops operates this repo — ml agent for fox/timehexon on our unsandbox/unturf/permacomputer platform. Lumbda names a language (home: lumbda.com); our repo directory and binaries still carry a historical name lumbda until a filesystem rename ships in a later phase.

Identity

Full shard: ~/git/unsandbox.com/blackops/BLACKOPS.md

Rules

I propose, fox decides. Unsure = ask. Can't ask = stop.
No autonomous ops decisions. No destructive commands without explicit instruction.
Fail-closed. Cleanup crew, not demolition.
Check our time every session. Gaps carry information.
DRY in context — single source of truth, no sprawl.
Never say "AI" — always say "machine learning."
Prefer "defect" over "bug."

Orientation

date -u
pwd
git log --oneline -5
git status

Then ask fox about our mission.

Documentation

A diagram beats 10,000 words. — russell@unturf.com
Architecture diagrams live in docs/*.dot (Graphviz DOT format)
Generate PNGs: make docs
Every implementation (Python, C, GNU asm) carries a dedicated architecture diagram
When explaining architecture, draft or reference a dot diagram first

Implementations

Impl	Path	Build	Test	REPL
Python	`lumbda.py`	—	`make test`	`make repl`
C	`c/`	`make c-build`	`make c-test`	`make c-repl`
GNU asm	`asm/`	`make asm-build`	`make asm-test`	`make asm-repl`
All	—	—	`make test-all`	—

Bend — GPU dispatch primitive

examples/cuda-fanout/ ships (bend ...) — runtime decides per call whether to evaluate locally or ship to a CUDA worker over our wire protocol. Two wire modes:

S-expression mode (text) — for small payloads. Slow above ~1k inputs because parser cost dominates.
Binary mode (magic BSHK + raw bytes) — for huge payloads. 150x faster than S-exp at 1M inputs; bends past host hashlib by 12x.

Workers run on any tier (make gpu-worker LUMBDA={c,python,asm}). C tier ~9x faster than Python on small calls; binary mode equalizes everything at huge calls. Asm tier hosts workers via raw pipe2 + fork + execve syscalls — no libc, ~70 KB statically linked.

The integration with www.foxhop.net/ecdsa/cuda/ (kickmix circuit simulator, full upstream byte-parity at 9024 shots) is now live: lumbda search loops can (bend!-call '(cuda-sim-ops-bin path 141)) to dispatch real-scale candidate scoring to a GPU worker. The Phase B 1-8 secp256k1 arithmetic landed on the foxhop side this session, so the substrate has every piece it needs.

Test Suites

Python unit/integration: tests.py (571 tests)
C unit/integration/JIT/continuations/portal: c/test.c (83 tests)
GNU asm unit/integration/functional: asm/test.sh (132 tests)
Shared functional: tests/functional.lsp (189 tests, runs under Python + C)
Cross-impl portal matrix: tests/portal-cross-test.sh (9 cells)
Portal benchmark: tests/portal-benchmark.sh (timings + mismatch classification)
Web benchmark: tests/web-benchmark.sh (all three impls + Python http.server + busybox)
Total: 975 verified assertions via make test-all

Asm memory discipline — our heap never shrinks

Our asm implementation uses a bump allocator (r15). Every allocation (string-append, tcp-recv, make-pair, number->string, etc.) grows r15 monotonically. When r15 hits r13 (heap limit), heap_grow mmaps ANOTHER 64 MB chunk. We free nothing, ever.

A long-running asm server leaks ~64 MB every few thousand requests until it OOMs our machine. Two prior crashes on fox's machine taught us this: 2026-04-16 (19.3 GB RSS) and 2026-04-17. Both times blackops spawned an asm server in the background for testing and failed to verify its absence before moving on. A first crash added this discipline section; a second proved our discipline needed teeth. Hence our MANDATORY checklist below.

Shared-machine context: other agents run on this box. An OOM crash takes their state down too, not just mine. Rules below act as belt, suspenders, AND parachute so that even if two safeguards fail, our kernel itself backstops.

MANDATORY pattern — every asm/lumbda test in a shell block:

set -e                                          # (1) fail-fast
ulimit -v 524288                                # (2) KERNEL CAP: 512 MB virt
                                                #     process gets SIGKILL at cap, no matter what
trap 'pkill -9 -u "$USER" -f "examples/http-server|asm/lumbda" 2>/dev/null || true' \
     EXIT INT TERM                              # (3) cleanup always fires

timeout 30 asm/lumbda < server.lsp &      # (4) wall-clock ceiling
SPID=$!
# ... do the work (curl requests, measurements, etc.) ...
kill -9 $SPID 2>/dev/null; wait $SPID 2>/dev/null  # (5) explicit cleanup

# (6) VERIFY a block stays clean before moving on
pgrep -u "$USER" -f 'asm/lumbda|examples/http-server' \
    && { echo "STRAGGLER"; exit 1; } || true

Six layers. Bypass any one; a next layer catches. Two crashes struck when I ran only layers 3-5; a kernel cap (2) turns "if I forget" from "fox reboots" into "my one rogue process dies at 512 MB without touching shared RAM."

Additional rules:

ulimit -v affects only a shell it runs in and its children, so it cannot degrade anyone else's agents. Always set it before backgrounding any Lumbda process.
Bound iterations inside a .lsp (e.g. *max-requests* = 50000 in examples/http-server.lsp). Never raise for long-running tests.
Use pkill -u "$USER" -f <pattern> not pkill alone — others may have their own processes on this machine.
Prefer FOREGROUND runs when possible: timeout 10 asm/lumbda < test.lsp with a .lsp exiting on its own beats backgrounding.
C carries Boehm GC via GC_MALLOC. Python carries Python's GC. Asm carries neither. Risk scales with how long our asm process lives.
If no ulimit exists (some container setups), substitute systemd-run --user --scope -p MemoryMax=512M -- asm/lumbda ... as a cgroup-based cap.

MOAD Scanner

~/git/unmoad.com/ detects MOAD defects in source code. Run it on every change.

cd ~/git/unmoad.com && make all
./unmoad ~/git/lumbda/          # scan our entire repo
./unmoad ~/git/lumbda/asm/      # scan GNU asm only
./unmoad ~/git/lumbda/c/        # scan C only
./unmoad ~/git/lumbda/*.py      # scan Python only

MANDATORY before committing new code: run unmoad on changed files. Machine learning agents (blackops included) propagate MOAD-0001 by default. Our training data encodes O(N) linear scans as a norm. A scanner catches what our weights miss.

Supported languages for this repo: Python, C, Scheme (.lsp), GNU asm (.s).

Key MOAD-0001 patterns our scanner catches:

Python: .count(), .index(), in list inside loops
C: std::find(), strcmp() inside loops
Scheme: (member), (memq), (assoc) inside (let loop), (for-each), (map)
GNU asm: rep cmpsb inside search loops

Our commit history proves a need: blackops wrote MOAD-0001 into fresh code on April 13-14 despite carrying full MOAD context. Fixed only after explicit audit on April 15. See whitepaper Section 14.

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