Establishes the integration pattern for lumbda's future cuda primitive
across Python / C / asm tiers without dragging the CUDA toolchain into
lumbda's core build.
Shape: leaf binary that every tier spawns via its existing process-
spawn primitive & talks to through S-expression input + output
portals. Asm tier inherits via fork + execve syscalls; no libcudart
linkage; no DKMS dependency at lumbda build time.
Files:
shake256-fanout.cu self-contained CUDA SHAKE256 fan-out, Keccak
permutation derived from FIPS 202 reference
(tiny-sha3 lineage, CC0 → re-licensed AGPLv3)
Makefile nvcc build + make test + make bench
test_roundtrip.py validates output byte-identical to
hashlib.shake_256
bench.py device vs host throughput at N = 1k / 10k / 100k
lumbda-call.lsp reference Scheme wrapper showing the
(cuda-shake-fanout inputs out-bytes) API shape
lumbda's core would dispatch to per-tier
README.md full integration story, wire contract, the
three changes each tier needs (~20 LoC each),
generalization path for other CUDA primitives
Tested on 3090-ai (RTX 3090):
make test → PASS — 4 / 4 hashes byte-identical to hashlib.shake_256
Honest bench (32-byte inputs):
N host (Python hashlib) device (kernel launch dominated)
1,000 0.6 ms 188.1 ms
10,000 5.9 ms 195.7 ms
100,000 58.7 ms 313.8 ms
Useful primitive when inputs are larger (KB+) or N reaches millions;
honest about the launch-overhead break-even point. This is the
reference, not the win — the win is locking the API shape so each
tier registers under one stable name.
Provenance: extracted as the generic pattern from
~/git/www.foxhop.net/ecdsa/cuda/sim_gpu.cu where on-device SHAKE
delivered 2.6× memory compression for batched reversible-circuit
simulation. Re-shipping the primitive back to the lumbda repo so the
ecosystem inherits the work.
russell@unturf.com
f11e310b24