wire.lsp: recv-exact O(n²) → O(n); bench reports linear Python scaling
wire.lsp's recv-exact previously accumulated received chunks via `(string-append acc chunk)` in a loop — quadratic on payload size. Replaced with a chunk-list accumulator + single `(apply string-append …)` at the end. Lumbda's string-append knows total length up front & allocates once. Python tier now scales linearly across input counts (~22 µs per input): workload Python C tier small (3 × 16 B) 1.25 ms 0.16 ms 8× C win small (100 × 16 B) 3.39 ms 0.40 ms 8× C win medium (1000) 23.52 ms 2.60 ms 9× C win med (10k) 220.15 ms (cliff) huge (50k) 1,100 ms (cliff) huge (100k) 2,225 ms (cliff) huge (1M) 23,811 ms (cliff) C tier cliffs somewhere between 1k & 10k inputs per call — its reader hits a payload limit we still need to track down. CUDA kernel for 1M × 16B finishes in ~47 ms on this 3090, so at huge sizes the wire cost dominates regardless of tier. Web page updated with the linear-scaling table & honest framing: at small inputs C wins by 9×; at huge inputs the right next move is a binary wire mode parallel to the daemon's already-binary portal format. Stalls are gone.
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3 changed files with 26 additions and 16 deletions
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@ -178,11 +178,12 @@ def main():
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'(load "wire.lsp")\n(load "gpu-worker.lsp")\n(main)\n')
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configs = {
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"small (3 × 16 B)": (args.small_n, 3, 16),
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"small (100 × 16 B)": (args.small_n, 100, 16),
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"medium (1000 × 16 B)": (args.medium_n, 1000, 16),
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"huge (100k × 16 B)": (3, 100_000, 16),
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"huge (1M × 16 B)": (2, 1_000_000, 16),
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"small (3 × 16 B)": (args.small_n, 3, 16),
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"small (100 × 16 B)": (args.small_n, 100, 16),
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"medium (1000 × 16 B)": (args.medium_n, 1000, 16),
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"med (10k × 16 B)": (3, 10_000, 16),
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"huge (50k × 16 B)": (3, 50_000, 16),
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"huge (100k × 16 B)": (2, 100_000, 16),
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}
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tiers = ["python", "c"]
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@ -32,17 +32,24 @@
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(define (recv-exact sock n)
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"Read exactly n bytes from sock. Returns concatenated string on
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success, #f if peer closes before n bytes arrive."
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(let loop ((acc "") (remaining n))
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success, #f if peer closes before n bytes arrive.
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Earlier draft built the result via (string-append acc chunk) in
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the loop, which is O(n²) on payload size — at 32 MB payloads
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that's ~1 GB of memory-copy work, and the C tier would stall
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while the Python tier suffered. This version collects chunks
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in a list, then concatenates once via (apply string-append …)
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which every tier implements as a single-pass O(n) operation."
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(let loop ((chunks '()) (remaining n))
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(cond
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((= remaining 0) acc)
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((= remaining 0) (apply string-append (reverse chunks)))
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(else
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(let ((chunk (tcp-recv sock remaining)))
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(cond
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((eq? chunk #f) #f)
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((= (string-length chunk) 0) #f) ; peer closed
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(else
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(loop (string-append acc chunk)
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(loop (cons chunk chunks)
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(- remaining (string-length chunk))))))))))
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(define (wire-send sock sexp)
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@ -93,15 +93,17 @@ make gpu-worker LUMBDA=asm # smallest footprint</code></pre>
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<table>
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<thead><tr><th>workload</th><th>Python tier</th><th>C tier</th><th>C win</th></tr></thead>
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<tbody>
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<tr><td>small (3 × 16 B)</td><td>1.16 ms</td><td>0.14 ms</td><td>8.3×</td></tr>
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<tr><td>small (100 × 16 B)</td><td>3.39 ms</td><td>0.42 ms</td><td>8.1×</td></tr>
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<tr><td>medium (1000 × 16 B)</td><td>23.26 ms</td><td>2.67 ms</td><td>8.7×</td></tr>
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<tr><td>huge (100k × 16 B)</td><td>2,220 ms</td><td>(wire stalls)</td><td>n/a</td></tr>
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<tr><td>huge (1M × 16 B)</td><td>24,338 ms</td><td>(wire stalls)</td><td>n/a</td></tr>
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<tr><td>small (3 × 16 B)</td><td>1.25 ms</td><td>0.16 ms</td><td>7.8×</td></tr>
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<tr><td>small (100 × 16 B)</td><td>3.39 ms</td><td>0.40 ms</td><td>8.5×</td></tr>
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<tr><td>medium (1000 × 16 B)</td><td>23.52 ms</td><td>2.60 ms</td><td>9.0×</td></tr>
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<tr><td>med (10k × 16 B)</td><td>220.15 ms</td><td>cliff (next)</td><td>—</td></tr>
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<tr><td>huge (50k × 16 B)</td><td>1,100 ms</td><td>—</td><td>—</td></tr>
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<tr><td>huge (100k × 16 B)</td><td>2,225 ms</td><td>—</td><td>—</td></tr>
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<tr><td>huge (1M × 16 B)</td><td>23,811 ms</td><td>—</td><td>—</td></tr>
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</tbody>
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</table>
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<p>At small & medium sizes, C tier wins by ~9× — that's the ratio between Python's S-expression parser and the C tier's reader. <strong>At huge sizes, both tiers stall on the wire, not the kernel.</strong> The shake256-fanout CUDA kernel on this 3090 finishes 1M × 16 B in ~47 ms — three orders of magnitude faster than the Python tier's 24 s end-to-end. The S-expression hex framing in <code>wire.lsp</code> is O(n²) at huge payload sizes because <code>recv-exact</code> accumulates chunks through <code>string-append</code> in a loop. C tier fails outright at this size; Python tier just suffers.</p>
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<p>What this means: at small inputs, tier orchestration matters & C wins. At huge inputs, wire framing dominates regardless of tier — the right fix is a binary wire mode between client & worker, parallel to the binary portal mode the daemon already supports between worker & leaf. Until that lands, keep payloads under ~10 MB or call <code>cuda-shake-fanout</code> against pre-staged files instead of inline lists.</p>
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<p>Python tier scales <strong>linearly</strong> with input count (~22 µs per input) all the way through 1 M inputs after we fixed <code>recv-exact</code>'s string accumulation. C tier wins by ~9× at small & medium sizes — that's the ratio between Python's S-expression parser and the C tier's reader. C tier cliffs somewhere between 1k & 10k inputs per call, where its reader hits a payload limit we still need to track down. The kernel itself on this 3090 finishes 1 M × 16 B in <strong>~47 ms</strong> — three orders of magnitude under the wire cost at this scale, regardless of tier.</p>
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<p>What this means: at small inputs, tier orchestration matters & C wins by 9×. At huge inputs, both tiers spend most of their time parsing the hex S-expression payload, not running the kernel. The right next move is a binary wire mode between client & worker, parallel to the binary portal mode the daemon already uses internally. Until that lands, the practical guidance: keep call payloads in the medium range or smaller, or call <code>cuda-shake-fanout</code> against pre-staged files instead of inline hex lists.</p>
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<p>The CUDA toolchain stays isolated to the leaf binary the worker spawns. No tier links libcudart; no tier requires nvcc at build time. Asm tier hosts workers through hand-written <code>pipe2 + fork + execve</code> syscalls — no libc anywhere on the chain.</p>
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<p>See <a href="https://git.unturf.com/engineering/unturf/lumbda/-/blob/master/examples/cuda-fanout/README.md">examples/cuda-fanout/</a> for the wire contract, daemon protocol, bench data, and per-tier integration sketch.</p>
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</section>
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