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bend catalog Wave 3 — 15 surveyed forms (2026-06-05)

Browse source 
Adds 15 new GPU primitive forms across domains not covered by
Waves 1-2: ZK/SNARK provers, pairing crypto, tensor network
contraction, sparse LA, CV primitives, numerical solvers,
generic belief propagation, MD/CFD, convex opt, DSP beyond cuFFT,
DB aggregations, graph theory beyond triangle/PageRank.

Top hits by relevance to our missions:

  * cuda-mfcc-spectral (97x) — punters-cc, BT-DISC forensics, real-time CC pipeline. unsandbox HIGH.
  * cuda-batched-lp-simplex (95x over CPLEX) — Prime Mission workstation-to-queue balancing. unsandbox HIGH.
  * cuda-betweenness-centrality-weighted (30-150x) — workaholic-node detection on dependency DAG, MOAD-0001 model. undefect HIGH.
  * cuda-hungarian-lap (400M-var LAP ~13s) — workstation balancing. unsandbox HIGH.
  * cuda-msm-bls12-381 (27.86x) — Pippenger shares branchless modmul shape with our reversible secp256k1 inner loop. ECDSA HIGH.
  * cuda-tensornet-contract (tensor QR 100x vs Xeon 8480+) — alt to stabilizer/kickmix sim path; MPS/PEPS evaluates reversible secp256k1 circuits beyond Clifford. ECDSA HIGH.
  * cuda-loopy-bp-mrf (45x) — LBP substrate for FuzzingBrain probabilistic analysis. undefect HIGH.
  * cuda-ega-gpu-aggregation (6-29x) — defect-corpus aggregation at planetary scale; telemetry queue aggregation. undefect HIGH + unsandbox HIGH.

Plus highlight numbers: FluidX3D LBM 100-200x vs ANSYS Fluent,
CUDASW++4.0 5.71 TCUPS on H100 for sequence alignment, ICICLE-Snark
the fastest Groth16 today.

Wave 3 filter-outs noted: AMGX, GROMACS, NVOFA, junction-tree BP,
batched L-BFGS — all below 10x or insufficiently general.

CATALOG.md mirrors the table for in-repo reading; bend.html
renders the public version with reference URLs.
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@ -141,6 +141,39 @@ run on our fleet yet.
- cuda-batched-mcts — 25-40x on Go-style rollouts; wrong shape for our search
- cuda-faiss-ann — 5-12x; no embedding workload today
## Wave 3 — surveyed 2026-06-05
15 additional forms covering ZK/SNARK provers, pairing crypto,
tensor network contraction, sparse LA, CV primitives, numerical
solvers, generic BP, MD/CFD, convex opt, DSP beyond cuFFT, DB
aggregations, graph theory beyond triangle/PageRank.
| form | speedup / throughput | hardware | relevance |
|-----------------------------------|------------------------------------------------|---------------------|-----------|
| cuda-fluidx3d-lbm | 100-200x vs ANSYS Fluent; 8799 MLUPS A100 | A100 | unsandbox MEDIUM (HPC reproducibility) |
| cuda-mfcc-spectral | ~97x CPU MFCC; STFT ~75x via cuSignal | GTX 580+ | unsandbox HIGH (punters-cc, BT-DISC forensics) |
| cuda-batched-lp-simplex | 95x over CPLEX; 5x over GLPK on 100K LPs | GTX 980-class | unsandbox HIGH (resource scheduling, Prime Mission) |
| cuda-betweenness-centrality-weighted | 30-150x warp-centric weighted BC | GTX+ | undefect HIGH (workaholic-node detection, MOAD-0001) |
| cuda-cudasift-orb-ransac | ~60x SIFT; 1.2ms on GTX 1060; ORB 11.3x | GTX 1060+ | unsandbox MEDIUM (visual evidence) |
| cuda-cudasw-gasal2 | CUDASW++4.0 16.2x; 5.71 TCUPS H100; GASAL2 750x | H100 | undefect MEDIUM (binary-diff patch-similarity) |
| cuda-loopy-bp-mrf | 45x over CPU LBP for stereo MRF | GTX 280+ | undefect HIGH (FuzzingBrain probabilistic analysis) |
| cuda-sgm-stereo | 42 fps @ 640x480, 128 disparities Tegra X1 | Tegra X1+ | unsandbox MEDIUM (edge node profile) |
| cuda-hungarian-lap | 400M-var LAP in ~13s; 10-50x class | NVIDIA | unsandbox HIGH (workstation-to-queue balancing); undefect MEDIUM |
| cuda-msm-bls12-381 | 27.86x over Pippenger RELIC AVX; 60% of Groth16 | A100 / 4090 | ECDSA HIGH (Pippenger shares branchless modmul shape) |
| cuda-pdwt-lifting | 15.9x over best CPU DWT | GTX / Tesla | unsandbox MEDIUM (audio-IPC analysis) |
| cuda-tensornet-contract | 8-20x vs CuPy; tensor QR ~100x vs Xeon 8480+ | A100 | ECDSA HIGH (alt to stabilizer/kickmix) |
| cuda-ega-gpu-aggregation | 6.45-29.12x multi-pass; group-by hash 19.4x | NVIDIA | undefect HIGH (corpus aggregation); unsandbox HIGH (telemetry) |
| cuda-bicgstab-ilu-spmv | SpTRSV 10.7x; ILU0 BiCGSTAB 3.2x; GMRES 6.9x | V100 / MI210 | ECDSA MEDIUM (sparse LA over GF(p)); undefect MEDIUM |
| cuda-icicle-snark-groth16 | Fastest Groth16 today; Mina 3x libsnark | RTX 4090 / A100 | ECDSA MEDIUM; undefect MEDIUM (zk-prover defect scanning) |
### Wave 3 filtered out (below threshold)
- AMGX algebraic multigrid: 2-5x — below 10x cutoff
- GROMACS GPU: 2-3x speedup vs CPU — below threshold
- NVIDIA Optical Flow SDK: 7-10x borderline, dedicated hardware unit
- Junction-tree BP per-message: 0.68-9.18x — below threshold
- Batched L-BFGS: 134x reported but single mesh-size case, not generalized
## Build order — recommended 3-form runway
1. **A: `cuda-secp256k1-batched-mul`** — biggest immediate win. Drop VanitySearch's CUDA secp256k1 kernel as a BSHK worker; lumbda gets a 6.5 Gkeys/s point-mul oracle. Every candidate validation rides this form. Hardware already owned (3090 + 4090).

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@ -198,6 +198,38 @@ make gpu-worker LUMBDA=asm # smallest footprint</code></pre>
</tbody>
</table>
<h3>Wave 3 &mdash; surveyed 2026-06-05</h3>
<p>15 additional forms spanning ZK / SNARK provers, pairing crypto, tensor network contraction, sparse linear algebra, CV primitives, numerical solvers, generic belief propagation, MD/CFD kernels, convex optimization, DSP beyond cuFFT, DB aggregations, graph theory beyond triangle/PageRank. Sorted by reported speedup or absolute throughput descending.</p>
<table>
<thead>
<tr>
<th>form</th>
<th>speedup / throughput</th>
<th>hardware</th>
<th>relevance</th>
<th>ref</th>
</tr>
</thead>
<tbody>
<tr><td><code>cuda-fluidx3d-lbm</code></td><td><strong>100&ndash;200&times;</strong> vs ANSYS Fluent / OpenFOAM; 8,799 MLUPS single A100</td><td>A100</td><td>unsandbox MEDIUM (HPC reproducibility, OpenCL backend matches our fleet)</td><td><a href="https://github.com/ProjectPhysX/FluidX3D">FluidX3D</a></td></tr>
<tr><td><code>cuda-mfcc-spectral</code></td><td><strong>~97&times;</strong> CPU MFCC; STFT ~75&times; via cuSignal vs SciPy</td><td>GTX 580 / RTX 30-series</td><td><strong>unsandbox HIGH</strong> &mdash; punters-cc, BT-DISC forensics, real-time CC pipeline</td><td><a href="https://developer.nvidia.com/blog/accelerated-signal-processing-with-cusignal/">cuSignal</a></td></tr>
<tr><td><code>cuda-batched-lp-simplex</code></td><td><strong>95&times;</strong> over CPLEX; 5&times; over GLPK on a batch of 100K LPs</td><td>GTX 980-class</td><td><strong>unsandbox HIGH</strong> &mdash; resource scheduling, Prime Mission workstation balancing</td><td><a href="https://arxiv.org/pdf/1802.08557">arXiv 1802.08557</a></td></tr>
<tr><td><code>cuda-betweenness-centrality-weighted</code></td><td><strong>30&ndash;150&times;</strong> warp-centric weighted BC</td><td>GTX onwards</td><td><strong>undefect HIGH</strong> &mdash; workaholic-node detection on dependency DAG, directly matches MOAD-0001 model</td><td><a href="https://arxiv.org/pdf/1701.05975">arXiv 1701.05975</a></td></tr>
<tr><td><code>cuda-cudasift-orb-ransac</code></td><td>~60&times; SIFT CPU&rarr;GPU (11 fps 1920&times;1440); 1.2 ms on GTX 1060; ORB 11.3&times;</td><td>GTX 1060+</td><td>unsandbox MEDIUM (visual evidence pipeline for incident reports)</td><td><a href="https://github.com/Celebrandil/CudaSift">CudaSift</a></td></tr>
<tr><td><code>cuda-cudasw-gasal2</code></td><td>CUDASW++4.0 <strong>16.2&times;</strong> over v3.0; 134&times; over ADEPT; <strong>5.71 TCUPS on H100</strong>; GASAL2 packing 750&times; vs NVBio</td><td>H100 (TCUPS)</td><td>undefect MEDIUM (binary-diff &amp; patch-similarity at scale: SW reduces to opcode-sequence diff)</td><td><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05965-6">CUDASW++4.0</a></td></tr>
<tr><td><code>cuda-loopy-bp-mrf</code></td><td><strong>45&times;</strong> over CPU LBP for stereo MRF inference</td><td>GTX 280-class+</td><td><strong>undefect HIGH</strong> &mdash; LBP substrate for FuzzingBrain-style probabilistic program analysis</td><td><a href="https://arxiv.org/pdf/2509.22337">arXiv 2509.22337</a></td></tr>
<tr><td><code>cuda-sgm-stereo</code></td><td><strong>42 fps</strong> at 640&times;480 with 128 disparities on Tegra X1; 46 fps on discrete GPUs</td><td>Tegra X1 / discrete</td><td>unsandbox MEDIUM (embedded ARM+CUDA matches our edge node profile)</td><td><a href="https://arxiv.org/abs/1610.04121">arXiv 1610.04121</a></td></tr>
<tr><td><code>cuda-hungarian-lap</code></td><td>10&ndash;50&times; class; 400 M-variable LAP in <strong>~13 s</strong></td><td>NVIDIA GPU</td><td><strong>unsandbox HIGH</strong> &mdash; workstation-to-queue balancing per Prime Mission; defect-cluster ↔ patch-bundle assignment for undefect</td><td><a href="https://www.sciencedirect.com/science/article/abs/pii/S016781911630045X">ScienceDirect</a></td></tr>
<tr><td><code>cuda-msm-bls12-381</code></td><td><strong>27.86&times;</strong> over Pippenger (RELIC) AVX baseline; 60% of Groth16 prover time on single GPU</td><td>A100 / RTX 4090</td><td><strong>ECDSA HIGH</strong> &mdash; Pippenger bucket sort + multi-G1 arithmetic shares branchless modmul shape with our reversible secp256k1 inner loop</td><td><a href="https://tches.iacr.org/index.php/TCHES/article/download/12061/11906/13930">SimdMSM TCHES</a></td></tr>
<tr><td><code>cuda-pdwt-lifting</code></td><td><strong>15.9&times;</strong> over best optimized CPU DWT (lifting scheme)</td><td>GTX / Tesla</td><td>unsandbox MEDIUM (audio-IPC payload analysis, BT signal denoising)</td><td><a href="https://github.com/pierrepaleo/PDWT">PDWT</a></td></tr>
<tr><td><code>cuda-tensornet-contract</code></td><td>8&ndash;20&times; vs CuPy on contraction; tensor QR <strong>~100&times;</strong> vs Xeon 8480+; tensor SVD ~10&times;</td><td>A100</td><td><strong>ECDSA HIGH</strong> &mdash; alternative to stabilizer/kickmix sim path; MPS/PEPS evaluates reversible secp256k1 circuits beyond Clifford</td><td><a href="https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html">cuTensorNet</a></td></tr>
<tr><td><code>cuda-ega-gpu-aggregation</code></td><td><strong>6.45&ndash;29.12&times;</strong> over CPU multi-pass EGA; group-by hash 19.4&times;</td><td>NVIDIA GPU</td><td><strong>undefect HIGH</strong> &mdash; defect-corpus aggregation at planetary scale; unsandbox HIGH &mdash; telemetry queue aggregation</td><td><a href="https://www.vldb.org/pvldb/vol17/p644-siddiqui.pdf">VLDB Top-k EGA</a></td></tr>
<tr><td><code>cuda-bicgstab-ilu-spmv</code></td><td>SpTRSV 10.7&times;; ILU0 BiCGSTAB 3.2&times; vs cuSPARSE on MI210; GMRES(30) block-ISAI 1.4&ndash;6.9&times;</td><td>V100 / MI210</td><td>ECDSA MEDIUM (sparse LA over GF(p) underpins lattice / index-calc); undefect MEDIUM (spectral analysis on DAG)</td><td><a href="https://arxiv.org/pdf/2508.04917">arXiv 2508.04917</a></td></tr>
<tr><td><code>cuda-icicle-snark-groth16</code></td><td>ICICLE-Snark fastest Groth16 today; Mina GPU 3&times; over libsnark; NTT 91% of prover at large sizes</td><td>RTX 4090 / A100</td><td>ECDSA MEDIUM (zk + MSM stack shares finite-field discipline); undefect MEDIUM (zk-prover defect scanning)</td><td><a href="https://www.ingonyama.com/post/icicle-snark-the-fastest-groth16-implementation-in-the-world">ICICLE-Snark</a></td></tr>
</tbody>
</table>
<p><strong>Wave 3 filter-outs:</strong> AMGX algebraic multigrid (2&ndash;5&times;), GROMACS GPU (2&ndash;3&times;), NVOFA optical flow (7&ndash;10&times; borderline, dedicated hardware unit), Junction-tree BP per-message (0.68&ndash;9.18&times;), batched L-BFGS (134&times; single-case, not generalized). All below 10&times; or insufficiently general; revisit when shape changes.</p>
<h3>Skipped &mdash; revisit when shape changes</h3>
<ul>
<li><strong>Argon2 / scrypt</strong>: ~1000 H/s on Tesla K20X is the <em>whole point</em> of memory-hard KDFs. Not a speedup story; only worth listing in an attack-surface doc.</li>