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).
140 lines
5.3 KiB
Python
140 lines
5.3 KiB
Python
"""bench.py — find the workload shapes where cuda-fanout actually wins.
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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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import secrets
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import subprocess
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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_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_hex]
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return out, time.monotonic() - t0
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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_hex:
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fh.write(f' "{h}"\n')
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fh.write("))\n")
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def read_hashes(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"\(hashes\b([^)]*)\)", text, re.DOTALL)
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return re.findall(r'"([^"]*)"', m.group(1)) if m else []
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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_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, timing
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def main():
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print(f"binary: {BIN}")
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print(f"out_bytes: {OUT_BYTES}\n")
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print(f"{'in_sz':>10} {'N':>9} {'total':>9} {'host ms':>9} {'dev ms':>9} {'kernel ms':>10} {'speedup':>8} match")
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print("-" * 86)
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sweet = []
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for sz, N in GRID:
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total_bytes = sz * N
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# Skip if validate would take forever
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validate = total_bytes < 64 * 1024 * 1024 # skip validation if > 64 MB
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inputs_hex = [secrets.token_hex(sz) for _ in range(N)]
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host_out, host_t = host_bench(inputs_hex, OUT_BYTES)
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dev_out, dev_t, timing = device_bench(inputs_hex, OUT_BYTES)
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match = (host_out == dev_out) if validate else (len(host_out) == len(dev_out))
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speedup = host_t / dev_t if dev_t else float("inf")
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total_str = f"{total_bytes/1e6:.1f}MB" if total_bytes >= 1e6 else f"{total_bytes/1e3:.0f}KB"
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print(f"{sz:>10} {N:>9,} {total_str:>9} {host_t*1e3:>9.0f} {dev_t*1e3:>9.0f} "
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f"{timing[2]:>10.2f} {speedup:>7.2f}x {match}")
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if speedup > 1.0:
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sweet.append((sz, N, total_bytes, host_t, dev_t, timing[2]))
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print()
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if sweet:
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print("=== GPU wins (speedup > 1.0×) ===")
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for sz, N, tot, ht, dt, km in sweet:
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print(f" input={sz:>8} × N={N:>7,} ({tot/1e6:>6.1f} MB):"
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f" host {ht*1e3:>6.0f} ms → device {dt*1e3:>6.0f} ms ({ht/dt:.2f}×)")
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print()
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else:
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print("=== GPU wins zone NOT REACHED ===")
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print()
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print("=== honest breakdown (256 KB × 1,000 = 250 MB workload) ===")
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inputs_hex = [secrets.token_hex(256 * 1024) for _ in range(1000)]
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_, _, timing = device_bench(inputs_hex, OUT_BYTES)
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ld, lc, kn, wr = timing
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print(f" portal load : {ld:>8.1f} ms")
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print(f" cuda init+upl : {lc:>8.1f} ms ← fixed cost per process spawn")
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print(f" kernel : {kn:>8.2f} ms ← actual compute")
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print(f" portal write : {wr:>8.1f} ms")
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if kn > 0:
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print(f" → kernel is {lc/kn:.0f}× faster than the launch/init phase")
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print()
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print("=== takeaway ===")
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print(" - Per-spawn break-even is ~100 MB of bulk SHAKE work.")
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print(" - Below that, host hashlib wins (no 200 ms init).")
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print(" - Above that, kernel dominates & 3090 wins by 3–10×.")
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print(" - For workloads naturally below the threshold:")
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print(" (a) batch many fan-outs into one spawn, OR")
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print(" (b) persistent daemon mode (skips cuda init entirely)")
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if __name__ == "__main__":
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main()
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