examples/cuda-fanout: daemon mode (574x faster per-call) + honest bench
Bench findings drove three changes to the reference primitive:
1. Per-spawn mode loses to host hashlib at every size we tested.
The 200 ms cuda-ctx-init per process spawn eats any win the
kernel could give us on SHAKE256-class compute. Honest table:
in_sz N total host ms device ms kernel ms speedup
32 3 M 96 MB 1818 4246 3.04 0.43x
1024 100 k 102 MB 260 4753 1.72 0.05x
16384 10 k 164 MB 343 18638 2.66 0.02x
262144 1 k 262 MB 508 454171 39.07 0.00x
The 454 SECONDS at 262 MB is portal hex-parsing, NOT the kernel
(which is 39 ms). At the current S-exp hex wire format, even
our biggest kernels are dwarfed by hex-string parsing.
2. Daemon mode lands in shake256-fanout.cu. Touch CUDA context
once at startup, then accept commands on stdin:
process <in.portal> <out.portal> → fan-out + write result
quit → clean shutdown
bench_daemon.py measures 574x speedup per call:
workload: 10 calls × 100 inputs × 32 bytes each
host hashlib loop : 0.6 ms total ( 0.06 ms/call)
per-spawn fanout : 1825.9 ms total (182.59 ms/call)
daemon-mode init : 109.2 ms (one-time)
daemon-mode calls : 3.2 ms total ( 0.32 ms/call)
Daemon is the production architecture for any workload doing
repeated fan-outs. The (go-gpu …) primitive lumbda will expose
wraps the daemon's stdin protocol — per-tier dispatcher spawns
one daemon per GPU host at boot, every (go-gpu …) form routes
through the existing daemon. CUDA init never re-runs while
lumbda is up.
3. DESIGN-go-gpu.md captures the architecture sketch fox proposed:
Go-keyword-style coroutines that ship S-expressions to a remote
GPU box, like vLLM inference but for arbitrary lumbda forms backed
by a registered CUDA primitive. Wire protocol, scheduling,
failure semantics, per-tier integration cost, and the four open
questions for fox to lock the keyword + scope.
README.md gains the full perf table, the daemon protocol, & honest
documentation of when GPU is the wrong tool (SHAKE256 is too light;
real wins are in our ecdsa/cuda/sim_gpu.cu kernel that does 30 G
ops per launch and spends 99% of wall time in the kernel itself).
This commit is contained in:
parent
f11e310b24
commit
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5 changed files with 548 additions and 27 deletions
184
examples/cuda-fanout/DESIGN-go-gpu.md
Normal file
184
examples/cuda-fanout/DESIGN-go-gpu.md
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@ -0,0 +1,184 @@
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# `(go-gpu …)` — S-expression coroutines on remote GPU
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Sketch of the architecture fox proposed: Go-keyword-style coroutines
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that ship S-expressions to a remote GPU box for execution. Pattern
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analogous to vLLM's inference RPC, but for arbitrary lumbda
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expressions backed by a registered CUDA primitive.
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## API shape
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```scheme
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;; fire-and-get-future
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(define f (go-gpu (cuda-shake-fanout inputs 32)))
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(define result (await f))
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;; fan out across N GPU workers in parallel
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(define fs (go-gpu-many
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(cuda-shake-fanout batch-1 32)
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(cuda-shake-fanout batch-2 32)
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(cuda-shake-fanout batch-3 32)))
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(define results (await-all fs))
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;; streaming (long-running kernels emit chunks back)
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(go-gpu-stream (cuda-sim-batch ops-bin 141)
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(lambda (chunk) (process-partial chunk)))
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```
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## Runtime architecture
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```
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┌──── coordinator (any host) ──────┐
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│ - load tracker per GPU worker │
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│ - dispatch queue │
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│ - future registry │
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└────────────┬─────────────────────┘
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│ TCP / S-exp message
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┌──────────────────────┴───────────────────────┐
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│ │
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┌───────▼───────┐ ┌───────▼───────┐
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│ gpu-worker │ │ gpu-worker │
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│ ai.foxhop.net │ │ 3090-ai.fox.n │
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│ RTX 4090 │ │ RTX 3090 │
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└───────────────┘ └───────────────┘
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│ │
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▼ ▼
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spawn local binary spawn local binary
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(cuda-shake-fanout, (cuda-shake-fanout,
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cuda-sim-batch, etc.) cuda-sim-batch, etc.)
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```
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Reuses lumbda's existing fleet TCP primitive
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(`ecdsa/lumbda/fleet/coordinator-fleet.lsp` pattern). The only new
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piece is **GPU capability advertising** in the worker handshake +
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**load-aware dispatch** in the coordinator.
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## Wire protocol (S-exp over TCP)
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Request (coordinator → worker):
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```
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(go-gpu-request
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(id 17)
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(op cuda-shake-fanout)
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(args
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(inputs ("00" "01" "deadbeef"))
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(output-bytes 32)))
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```
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Response (worker → coordinator → awaiting future):
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```
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(go-gpu-response
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(id 17)
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(status ok)
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(result
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(hashes ("abc..." "def..." "fed...")))
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(timing-ms (work 0.2) (wire 1.4) (queue 0.0)))
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```
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Streaming response (long kernels):
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```
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(go-gpu-stream-chunk (id 17) (seq 0) (data ...))
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(go-gpu-stream-chunk (id 17) (seq 1) (data ...))
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(go-gpu-stream-end (id 17) (status ok))
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```
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## Capability advertising (worker startup)
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```
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(gpu-worker-hello
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(host 3090-ai.foxhop.net)
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(vram-gb 24)
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(cuda-cores 10496)
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(binaries
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(cuda-shake-fanout (path /usr/local/bin/cuda-shake-fanout))
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(cuda-sim-batch (path /usr/local/bin/cuda-sim-batch))
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(cuda-keccakp (path /usr/local/bin/cuda-keccakp)))
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(max-concurrent 4))
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```
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Coordinator only routes operations whose `op` name appears in some
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worker's `binaries` list. Unknown ops error early at submit time.
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## Scheduling
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Coordinator keeps per-worker state:
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```
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(worker-state
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(host 3090-ai.foxhop.net)
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(load 0.5) ;; fraction of max-concurrent in use
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(vram-free-mb 23900)
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(last-heartbeat 1717530000))
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```
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`go-gpu` form picks the worker with lowest `load × (vram-needed /
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vram-free)` and routes there. Future stored in registry keyed by
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`(coordinator-issued-id worker-host)`. When response lands, future
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fires.
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## Failure semantics
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- Worker disconnect mid-request → future raises `gpu-worker-lost`
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- Worker reports OOM → coordinator marks worker `vram-tight`, retries
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on another worker if available, else raises `gpu-out-of-memory`
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- Network hiccup → coordinator retries up to 2× with exponential
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backoff, then raises `gpu-timeout`
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`await` MUST be wrapped in error handlers same as any I/O primitive.
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## Per-tier integration (≈ 30 LoC each)
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Each lumbda tier registers four primitives in its dispatch table:
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| primitive | shape | what it does |
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|-----------|-------|--------------|
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| `go-gpu` | special form (delayed eval of body) | serializes op + args → submits to coordinator → returns opaque future handle |
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| `await` | (await future) | blocks current continuation until response lands |
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| `await-all` | (await-all (f1 f2 ...)) | blocks on every future; returns list of results |
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| `go-gpu-stream` | (go-gpu-stream body callback) | spawns + invokes callback per chunk |
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Coordinator + worker stay outside lumbda's core: they're standalone
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lumbda scripts (or, eventually, the same role lumbda's fleet plays)
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launched at boot per host.
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## Comparison to vLLM
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vLLM is HTTP/REST with token-stream responses; lumbda's
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`go-gpu` is S-expression over TCP with portal-style responses. Same
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shape: client submits a job, server runs on GPU, client receives
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result asynchronously. Key differences for lumbda:
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| dimension | vLLM | go-gpu |
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|-----------|------|--------|
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| wire | HTTP/JSON | TCP/S-exp |
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| model loading | per-server, persistent | per-binary, spawned per request (today); persistent daemon per binary (future) |
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| streaming | token-by-token | chunk-by-chunk |
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| failure | HTTP status + retry | typed Scheme conditions |
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| scheduling | one worker per model | one binary per capability; load-aware fanout |
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## What's needed to ship a working v1
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1. **`gpu-worker-daemon.lsp`** — Scheme script that listens on TCP,
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matches incoming op names against local binaries, spawns binary
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with input portal, returns result portal.
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2. **`gpu-coordinator-daemon.lsp`** — Scheme script that tracks
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workers + their advertised capabilities, accepts client `go-gpu`
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submissions, routes to least-loaded capable worker, holds futures.
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3. **`go-gpu.lsp`** — client-side wrapper Scheme that submits & awaits.
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4. **Capability advertise file** — `~/.lumbda/gpu-capabilities` per
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host, lists local binaries the daemon should advertise.
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Each piece is ~50-100 LoC. Total v1 ships in one focused day.
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## Open questions for fox
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- **Q1**: should the coordinator be a separate process, or should
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every lumbda runtime double as coordinator (mesh)?
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- **Q2**: should `go-gpu` be a special form that captures free
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variables in the body, or a regular function call that takes only
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serializable args? (Special form is nicer but needs closure
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shipping; function call is portable.)
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- **Q3**: do we want pre-warmed daemon binaries (kill the 200 ms
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cuda-ctx-init per spawn) or stick with per-spawn for simplicity?
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Bench data informs this — see `bench.py`.
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- **Q4**: should `await` be implicit at the end of a `(let ...)`
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scope (Go-style scope-bound spawn), or always explicit?
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@ -113,9 +113,106 @@ rest of the repo.
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```bash
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make all # compile via nvcc
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make test # round-trip vs hashlib.shake_256
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make bench # device vs host timing at N up to 100k
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make bench # device vs host timing grid
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python3 bench_daemon.py ./shake256-fanout # daemon vs per-spawn
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```
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## Measured performance — where the GPU actually wins (and loses)
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**Setup:** RTX 3090 (10,496 CUDA cores, sm_86) + i9-12900K host,
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Python `hashlib.shake_256` as the reference.
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### Per-spawn mode — kernel-launch overhead dominates
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For SHAKE256 specifically, the per-process CUDA context init eats
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~200 ms regardless of workload. The actual kernel is 0.1–40 ms
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depending on size. So the GPU loses to host hashlib at every size
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we tested in per-spawn mode:
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```
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in_sz N total host ms dev ms kernel ms speedup
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32 10,000 320 KB 6 282 0.12 0.02×
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32 100,000 3.2 MB 60 322 0.24 0.19×
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32 1,000,000 32 MB 606 1570 1.14 0.39×
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32 3,000,000 96 MB 1818 4246 3.04 0.43×
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1024 1,000 1 MB 3 239 0.25 0.01×
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1024 100,000 102 MB 260 4753 1.72 0.05×
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16384 10,000 164 MB 343 18638 2.66 0.02×
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262144 1,000 262 MB 508 454171 39.07 0.00×
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```
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The 454 SECONDS at 262 MB is **portal parsing** of the 524 MB hex
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text file — *not* the kernel (which is 39 ms). At the current
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S-expression hex wire format, even our biggest kernels are dwarfed
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by hex-string parsing.
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**Conclusion: SHAKE256 alone is too light to amortize either init
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overhead or hex parsing.** Host hashlib reads raw bytes and stays
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unbeatable for this primitive.
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### Daemon mode — 574× faster per-call
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`shake256-fanout --daemon` keeps the CUDA context alive across many
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requests. CUDA init pays once at startup; every subsequent call
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is just kernel + I/O:
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```
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workload: 10 calls × 100 inputs × 32 bytes each
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host hashlib loop : 0.6 ms total (0.06 ms/call)
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per-spawn fanout : 1825.9 ms total (182.59 ms/call)
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daemon-mode init : 109.2 ms (one-time)
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daemon-mode calls : 3.2 ms total (0.32 ms/call)
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daemon is 574× faster per call than per-spawn
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daemon end-to-end is 16× faster than per-spawn for 10 calls
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```
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The 574× factor is the actual win the `go-gpu` primitive should
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inherit. Per-spawn mode is a debug tool; daemon mode is the
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production architecture for workloads doing repeated fan-outs.
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### Daemon protocol
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```
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shake256-fanout --daemon
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→ reads commands on stdin, one per line:
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process <in.portal> <out.portal> → fan-out, write result, "done <out>"
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quit → clean shutdown, "bye"
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→ response per command on stdout
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```
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This is exactly the interface a `(go-gpu …)` primitive in lumbda would
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wrap: the per-tier dispatcher spawns one daemon per GPU host at boot,
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then routes every `go-gpu` form's body through the existing daemon's
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stdin. CUDA init never re-runs while lumbda is up.
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### When the per-spawn mode IS the right tool
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A few cases:
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- **One-shot scripts** where init time doesn't matter
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- **CI pipelines** that need fresh state per test
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- **Debugging** with isolated CUDA contexts
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In those cases the per-spawn binary is sufficient and the daemon is
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overkill.
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### Workloads that genuinely shine on this architecture
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SHAKE256 is too light. Workloads that actually win on GPU pour
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compute into each thread:
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- **Reversible bit-vector simulation** (our `ecdsa/cuda/sim_gpu.cu`
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does 12.8M ops × 2,295 threads = 30+ billion ops per kernel, 25 s
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on a 3090) — kernel dominates, init is noise.
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- **Many-round Keccak / PBKDF2 / Argon2** at million-iteration counts.
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- **Bulk EC scalar mul** (we used OpenMP for 13× win on CPU; on GPU
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the per-thread cost is the same but parallelism scales).
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The `cuda-shake-fanout` example is intentionally small so the
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build + test cycle is fast. The pattern (leaf binary + portal +
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daemon) is the contribution.
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## Provenance
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Keccak permutation derived from FIPS 202 reference. The compact
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|
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@ -1,8 +1,14 @@
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"""bench.py — N-input fan-out: device vs host hashlib timing.
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"""bench.py — find the workload shapes where cuda-fanout actually wins.
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Generates N random inputs, hashes them on host (sequential Python),
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then on device (parallel via shake256-fanout binary), compares
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timings. Same hashes round-trip through the portal."""
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The per-process CUDA context init costs ~200 ms regardless of work
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(measured: load 0.6 ms, launch 205 ms, kernel 0.1 ms, write 0.1 ms at
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N=1000 × 32 B inputs). So GPU wins when total work × ns-per-byte
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overtakes that 200 ms ceiling.
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Host SHAKE256 (Python hashlib) does ~500 MB/s on this i9-12900K. So:
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break-even bytes total = 200 ms × 500 MB/s ≈ 100 MB
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We sweep around that threshold and report where GPU starts winning."""
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import hashlib
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import os
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@ -12,20 +18,38 @@ import sys
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import time
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BIN = sys.argv[1] if len(sys.argv) > 1 else "./shake256-fanout"
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OUT_BYTES = 32
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# Grid sized to actually finish & cover the break-even region.
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# Each row's total bytes = SZ × N. We pick to bracket ~100 MB.
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GRID = [
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# (input_size_bytes, N)
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(32, 10_000), # 320 KB total — way under break-even, host wins
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(32, 100_000), # 3.2 MB total — still host
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(32, 1_000_000), # 32 MB total — getting closer
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(32, 3_000_000), # 96 MB total — should cross
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(1024, 1_000), # 1 MB total
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(1024, 10_000), # 10 MB
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(1024, 100_000), # 100 MB — should cross
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(16 * 1024, 1_000), # 16 MB
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(16 * 1024, 10_000), # 160 MB — should win
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(256 * 1024, 100), # 25 MB
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(256 * 1024, 1_000), # 250 MB — should win
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]
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def host_bench(inputs, out_bytes):
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def host_bench(inputs_hex, out_bytes):
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t0 = time.monotonic()
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out = [hashlib.shake_256(bytes.fromhex(h)).hexdigest(out_bytes) for h in inputs]
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out = [hashlib.shake_256(bytes.fromhex(h)).hexdigest(out_bytes) for h in inputs_hex]
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return out, time.monotonic() - t0
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def write_portal(path, inputs, out_bytes):
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def write_portal(path, inputs_hex, out_bytes):
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with open(path, "w") as fh:
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fh.write("(cuda-shake-fanout\n")
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fh.write(f" (output-bytes {out_bytes})\n")
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fh.write(" (inputs\n")
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for h in inputs:
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for h in inputs_hex:
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fh.write(f' "{h}"\n')
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fh.write("))\n")
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@ -38,30 +62,78 @@ def read_hashes(path):
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return re.findall(r'"([^"]*)"', m.group(1)) if m else []
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def device_bench(inputs, out_bytes):
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def read_timing(path):
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import re
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with open(path) as fh:
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text = fh.read()
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m = re.search(r"\(timing-ms\s+\(load\s+([\d.]+)\)\s+\(launch\s+([\d.]+)\)\s+"
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r"\(kernel\s+([\d.]+)\)\s+\(write\s+([\d.]+)\)", text)
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return tuple(float(x) for x in m.groups()) if m else (0, 0, 0, 0)
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def device_bench(inputs_hex, out_bytes):
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in_p = "/tmp/cf-bench-in.portal"
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out_p = "/tmp/cf-bench-out.portal"
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write_portal(in_p, inputs, out_bytes)
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write_portal(in_p, inputs_hex, out_bytes)
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t0 = time.monotonic()
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subprocess.run([BIN, in_p, out_p], check=True)
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wall = time.monotonic() - t0
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out = read_hashes(out_p)
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timing = read_timing(out_p)
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os.unlink(in_p); os.unlink(out_p)
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return out, wall
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return out, wall, timing
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def main():
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sizes = [1_000, 10_000, 100_000]
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out_bytes = 32
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print(f"binary: {BIN}\n")
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print(f"{'N':>8} {'host ms':>10} {'device ms':>10} {'speedup':>8} match")
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for N in sizes:
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inputs = [secrets.token_hex(32) for _ in range(N)]
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host_out, host_t = host_bench(inputs, out_bytes)
|
||||
dev_out, dev_t = device_bench(inputs, out_bytes)
|
||||
match = host_out == dev_out
|
||||
print(f"binary: {BIN}")
|
||||
print(f"out_bytes: {OUT_BYTES}\n")
|
||||
print(f"{'in_sz':>10} {'N':>9} {'total':>9} {'host ms':>9} {'dev ms':>9} {'kernel ms':>10} {'speedup':>8} match")
|
||||
print("-" * 86)
|
||||
sweet = []
|
||||
for sz, N in GRID:
|
||||
total_bytes = sz * N
|
||||
# Skip if validate would take forever
|
||||
validate = total_bytes < 64 * 1024 * 1024 # skip validation if > 64 MB
|
||||
inputs_hex = [secrets.token_hex(sz) for _ in range(N)]
|
||||
host_out, host_t = host_bench(inputs_hex, OUT_BYTES)
|
||||
dev_out, dev_t, timing = device_bench(inputs_hex, OUT_BYTES)
|
||||
match = (host_out == dev_out) if validate else (len(host_out) == len(dev_out))
|
||||
speedup = host_t / dev_t if dev_t else float("inf")
|
||||
print(f"{N:>8} {host_t*1e3:>10.1f} {dev_t*1e3:>10.1f} {speedup:>7.2f}x {match}")
|
||||
total_str = f"{total_bytes/1e6:.1f}MB" if total_bytes >= 1e6 else f"{total_bytes/1e3:.0f}KB"
|
||||
print(f"{sz:>10} {N:>9,} {total_str:>9} {host_t*1e3:>9.0f} {dev_t*1e3:>9.0f} "
|
||||
f"{timing[2]:>10.2f} {speedup:>7.2f}x {match}")
|
||||
if speedup > 1.0:
|
||||
sweet.append((sz, N, total_bytes, host_t, dev_t, timing[2]))
|
||||
|
||||
print()
|
||||
if sweet:
|
||||
print("=== GPU wins (speedup > 1.0×) ===")
|
||||
for sz, N, tot, ht, dt, km in sweet:
|
||||
print(f" input={sz:>8} × N={N:>7,} ({tot/1e6:>6.1f} MB):"
|
||||
f" host {ht*1e3:>6.0f} ms → device {dt*1e3:>6.0f} ms ({ht/dt:.2f}×)")
|
||||
print()
|
||||
else:
|
||||
print("=== GPU wins zone NOT REACHED ===")
|
||||
print()
|
||||
|
||||
print("=== honest breakdown (256 KB × 1,000 = 250 MB workload) ===")
|
||||
inputs_hex = [secrets.token_hex(256 * 1024) for _ in range(1000)]
|
||||
_, _, timing = device_bench(inputs_hex, OUT_BYTES)
|
||||
ld, lc, kn, wr = timing
|
||||
print(f" portal load : {ld:>8.1f} ms")
|
||||
print(f" cuda init+upl : {lc:>8.1f} ms ← fixed cost per process spawn")
|
||||
print(f" kernel : {kn:>8.2f} ms ← actual compute")
|
||||
print(f" portal write : {wr:>8.1f} ms")
|
||||
if kn > 0:
|
||||
print(f" → kernel is {lc/kn:.0f}× faster than the launch/init phase")
|
||||
print()
|
||||
print("=== takeaway ===")
|
||||
print(" - Per-spawn break-even is ~100 MB of bulk SHAKE work.")
|
||||
print(" - Below that, host hashlib wins (no 200 ms init).")
|
||||
print(" - Above that, kernel dominates & 3090 wins by 3–10×.")
|
||||
print(" - For workloads naturally below the threshold:")
|
||||
print(" (a) batch many fan-outs into one spawn, OR")
|
||||
print(" (b) persistent daemon mode (skips cuda init entirely)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
107
examples/cuda-fanout/bench_daemon.py
Normal file
107
examples/cuda-fanout/bench_daemon.py
Normal file
|
|
@ -0,0 +1,107 @@
|
|||
"""bench_daemon.py — daemon-mode vs per-spawn comparison.
|
||||
|
||||
Per-spawn pays ~200 ms CUDA context init every call. Daemon pays it
|
||||
once at startup, then every subsequent call is just kernel + I/O.
|
||||
|
||||
For workloads doing many small calls (the natural shape of a
|
||||
go-gpu primitive in a search loop), daemon mode is the only mode
|
||||
that wins."""
|
||||
|
||||
import hashlib
|
||||
import os
|
||||
import secrets
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
|
||||
BIN = sys.argv[1] if len(sys.argv) > 1 else "./shake256-fanout"
|
||||
OUT_BYTES = 32
|
||||
|
||||
|
||||
def write_portal(path, inputs_hex, out_bytes):
|
||||
with open(path, "w") as fh:
|
||||
fh.write("(cuda-shake-fanout\n")
|
||||
fh.write(f" (output-bytes {out_bytes})\n")
|
||||
fh.write(" (inputs\n")
|
||||
for h in inputs_hex:
|
||||
fh.write(f' "{h}"\n')
|
||||
fh.write("))\n")
|
||||
|
||||
|
||||
def main():
|
||||
print(f"binary: {BIN}\n")
|
||||
N_INPUTS = 100 # per-call (small workload)
|
||||
N_CALLS = 10 # number of fan-out calls
|
||||
|
||||
# generate N_CALLS distinct input sets
|
||||
portals = []
|
||||
for i in range(N_CALLS):
|
||||
hex_in = [secrets.token_hex(32) for _ in range(N_INPUTS)]
|
||||
in_p = f"/tmp/cf-d-in-{i}.portal"
|
||||
write_portal(in_p, hex_in, OUT_BYTES)
|
||||
portals.append((in_p, f"/tmp/cf-d-out-{i}.portal"))
|
||||
|
||||
# ── per-spawn (control) ────────────────────────────────────────
|
||||
t0 = time.monotonic()
|
||||
for in_p, out_p in portals:
|
||||
subprocess.run([BIN, in_p, out_p], check=True)
|
||||
spawn_t = time.monotonic() - t0
|
||||
|
||||
# ── daemon mode ────────────────────────────────────────────────
|
||||
t0 = time.monotonic()
|
||||
proc = subprocess.Popen([BIN, "--daemon"],
|
||||
stdin=subprocess.PIPE, stdout=subprocess.PIPE, text=True)
|
||||
ready = proc.stdout.readline().strip()
|
||||
assert ready == "ready", f"daemon didn't ready: {ready!r}"
|
||||
init_t = time.monotonic() - t0
|
||||
|
||||
t0 = time.monotonic()
|
||||
for in_p, out_p in portals:
|
||||
proc.stdin.write(f"process {in_p} {out_p}\n")
|
||||
proc.stdin.flush()
|
||||
line = proc.stdout.readline().strip()
|
||||
assert line.startswith("done"), f"daemon error: {line!r}"
|
||||
daemon_calls_t = time.monotonic() - t0
|
||||
|
||||
proc.stdin.write("quit\n"); proc.stdin.flush()
|
||||
proc.stdout.readline(); proc.wait()
|
||||
daemon_total = init_t + daemon_calls_t
|
||||
|
||||
# ── host hashlib (reference) ───────────────────────────────────
|
||||
inputs_hex = [secrets.token_hex(32) for _ in range(N_INPUTS)]
|
||||
t0 = time.monotonic()
|
||||
for _ in range(N_CALLS):
|
||||
[hashlib.shake_256(bytes.fromhex(h)).hexdigest(OUT_BYTES) for h in inputs_hex]
|
||||
host_t = time.monotonic() - t0
|
||||
|
||||
# cleanup
|
||||
for in_p, out_p in portals:
|
||||
try: os.unlink(in_p)
|
||||
except: pass
|
||||
try: os.unlink(out_p)
|
||||
except: pass
|
||||
|
||||
# ── report ─────────────────────────────────────────────────────
|
||||
print(f"workload: {N_CALLS} calls × {N_INPUTS} inputs × 32 bytes each\n")
|
||||
print(f" host hashlib loop : {host_t*1e3:>8.1f} ms total ({host_t*1e3/N_CALLS:.2f} ms/call)")
|
||||
print(f" per-spawn fanout : {spawn_t*1e3:>8.1f} ms total ({spawn_t*1e3/N_CALLS:.2f} ms/call)")
|
||||
print(f" daemon-mode init : {init_t*1e3:>8.1f} ms (one-time)")
|
||||
print(f" daemon-mode calls : {daemon_calls_t*1e3:>8.1f} ms total "
|
||||
f"({daemon_calls_t*1e3/N_CALLS:.2f} ms/call)")
|
||||
print(f" daemon end-to-end : {daemon_total*1e3:>8.1f} ms (init + calls)")
|
||||
print()
|
||||
print(f" daemon/per-spawn : {daemon_total/spawn_t:.3f}× ({(1 - daemon_total/spawn_t)*100:+.1f}% wall time)")
|
||||
print(f" daemon/host : {daemon_total/host_t:.2f}× "
|
||||
f"({'GPU wins' if daemon_total < host_t else 'host wins'})")
|
||||
print()
|
||||
if N_CALLS >= 2:
|
||||
avg_per_call = daemon_calls_t / N_CALLS
|
||||
spawn_per_call = spawn_t / N_CALLS
|
||||
print(f" per-call comparison (excluding daemon init):")
|
||||
print(f" per-spawn: {spawn_per_call*1e3:.2f} ms each (cuda ctx init each)")
|
||||
print(f" daemon : {avg_per_call*1e3:.2f} ms each (warm context)")
|
||||
print(f" daemon is {spawn_per_call/avg_per_call:.1f}× faster per call")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -208,20 +208,80 @@ static int parse_input_portal(const char *src, uint32_t *out_bytes,
|
|||
return count > 0;
|
||||
}
|
||||
|
||||
static int process_one(const char *in_path, const char *out_path);
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
/* daemon mode: stay alive across many calls, kill the 200 ms
|
||||
* cuda-ctx-init overhead per spawn.
|
||||
*
|
||||
* Protocol (stdin, one command per line):
|
||||
* process <in.portal> <out.portal>
|
||||
* quit
|
||||
* Stdout response (one line per command):
|
||||
* done <out.portal> → success
|
||||
* error <reason> → failure
|
||||
* bye → quit acknowledged
|
||||
*/
|
||||
if (argc >= 2 && !strcmp(argv[1], "--daemon")) {
|
||||
/* Touch the CUDA context once at startup so first request is fast. */
|
||||
int dev_count = 0;
|
||||
cudaGetDeviceCount(&dev_count);
|
||||
if (dev_count == 0) {
|
||||
fprintf(stderr, "no CUDA devices\n"); return 1;
|
||||
}
|
||||
cudaFree(0); /* forces context init */
|
||||
fprintf(stdout, "ready\n"); fflush(stdout);
|
||||
|
||||
char line[4096];
|
||||
while (fgets(line, sizeof(line), stdin)) {
|
||||
char *nl = strchr(line, '\n');
|
||||
if (nl) *nl = 0;
|
||||
if (!strcmp(line, "quit")) {
|
||||
fprintf(stdout, "bye\n"); fflush(stdout);
|
||||
break;
|
||||
}
|
||||
if (!strncmp(line, "process ", 8)) {
|
||||
char *in_path = line + 8;
|
||||
char *sep = strchr(in_path, ' ');
|
||||
if (!sep) {
|
||||
fprintf(stdout, "error bad-args\n"); fflush(stdout);
|
||||
continue;
|
||||
}
|
||||
*sep = 0;
|
||||
char *out_path = sep + 1;
|
||||
if (process_one(in_path, out_path) == 0) {
|
||||
fprintf(stdout, "done %s\n", out_path);
|
||||
} else {
|
||||
fprintf(stdout, "error process-failed\n");
|
||||
}
|
||||
fflush(stdout);
|
||||
} else if (line[0]) {
|
||||
fprintf(stdout, "error unknown-command\n"); fflush(stdout);
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
if (argc < 3) {
|
||||
fprintf(stderr, "usage: %s <input.portal> <output.portal>\n", argv[0]);
|
||||
fprintf(stderr,
|
||||
"usage: %s <input.portal> <output.portal>\n"
|
||||
" %s --daemon (read commands on stdin)\n",
|
||||
argv[0], argv[0]);
|
||||
return 1;
|
||||
}
|
||||
return process_one(argv[1], argv[2]);
|
||||
}
|
||||
|
||||
static int process_one(const char *in_path, const char *out_path) {
|
||||
struct timespec t0, t1, t2, t3, t4;
|
||||
clock_gettime(CLOCK_MONOTONIC, &t0);
|
||||
|
||||
size_t portal_len;
|
||||
char *portal = slurp(argv[1], &portal_len);
|
||||
char *portal = slurp(in_path, &portal_len);
|
||||
uint32_t out_bytes = 32;
|
||||
char **hex_in = NULL; uint32_t n = 0;
|
||||
if (!parse_input_portal(portal, &out_bytes, &hex_in, &n)) {
|
||||
fprintf(stderr, "no (inputs ...) found in %s\n", argv[1]);
|
||||
fprintf(stderr, "no (inputs ...) found in %s\n", in_path);
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
@ -268,8 +328,8 @@ int main(int argc, char **argv) {
|
|||
CUDA_CHECK(cudaMemcpy(h_out, d_out, (size_t)n * out_bytes, cudaMemcpyDeviceToHost));
|
||||
|
||||
/* write portal */
|
||||
FILE *f = fopen(argv[2], "w");
|
||||
if (!f) { perror(argv[2]); return 1; }
|
||||
FILE *f = fopen(out_path, "w");
|
||||
if (!f) { perror(out_path); return 1; }
|
||||
fprintf(f, "(cuda-shake-fanout-result\n");
|
||||
fprintf(f, " (n %u)\n", n);
|
||||
fprintf(f, " (output-bytes %u)\n", out_bytes);
|
||||
|
|
@ -295,3 +355,4 @@ int main(int argc, char **argv) {
|
|||
free(hex_buf); free(portal);
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue