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.
+| form | +speedup / throughput | +hardware | +relevance | +ref | +
|---|---|---|---|---|
cuda-fluidx3d-lbm | 100–200× vs ANSYS Fluent / OpenFOAM; 8,799 MLUPS single A100 | A100 | unsandbox MEDIUM (HPC reproducibility, OpenCL backend matches our fleet) | FluidX3D |
cuda-mfcc-spectral | ~97× CPU MFCC; STFT ~75× via cuSignal vs SciPy | GTX 580 / RTX 30-series | unsandbox HIGH — punters-cc, BT-DISC forensics, real-time CC pipeline | cuSignal |
cuda-batched-lp-simplex | 95× over CPLEX; 5× over GLPK on a batch of 100K LPs | GTX 980-class | unsandbox HIGH — resource scheduling, Prime Mission workstation balancing | arXiv 1802.08557 |
cuda-betweenness-centrality-weighted | 30–150× warp-centric weighted BC | GTX onwards | undefect HIGH — workaholic-node detection on dependency DAG, directly matches MOAD-0001 model | arXiv 1701.05975 |
cuda-cudasift-orb-ransac | ~60× SIFT CPU→GPU (11 fps 1920×1440); 1.2 ms on GTX 1060; ORB 11.3× | GTX 1060+ | unsandbox MEDIUM (visual evidence pipeline for incident reports) | CudaSift |
cuda-cudasw-gasal2 | CUDASW++4.0 16.2× over v3.0; 134× over ADEPT; 5.71 TCUPS on H100; GASAL2 packing 750× vs NVBio | H100 (TCUPS) | undefect MEDIUM (binary-diff & patch-similarity at scale: SW reduces to opcode-sequence diff) | CUDASW++4.0 |
cuda-loopy-bp-mrf | 45× over CPU LBP for stereo MRF inference | GTX 280-class+ | undefect HIGH — LBP substrate for FuzzingBrain-style probabilistic program analysis | arXiv 2509.22337 |
cuda-sgm-stereo | 42 fps at 640×480 with 128 disparities on Tegra X1; 46 fps on discrete GPUs | Tegra X1 / discrete | unsandbox MEDIUM (embedded ARM+CUDA matches our edge node profile) | arXiv 1610.04121 |
cuda-hungarian-lap | 10–50× class; 400 M-variable LAP in ~13 s | NVIDIA GPU | unsandbox HIGH — workstation-to-queue balancing per Prime Mission; defect-cluster ↔ patch-bundle assignment for undefect | ScienceDirect |
cuda-msm-bls12-381 | 27.86× over Pippenger (RELIC) AVX baseline; 60% of Groth16 prover time on single GPU | A100 / RTX 4090 | ECDSA HIGH — Pippenger bucket sort + multi-G1 arithmetic shares branchless modmul shape with our reversible secp256k1 inner loop | SimdMSM TCHES |
cuda-pdwt-lifting | 15.9× over best optimized CPU DWT (lifting scheme) | GTX / Tesla | unsandbox MEDIUM (audio-IPC payload analysis, BT signal denoising) | PDWT |
cuda-tensornet-contract | 8–20× vs CuPy on contraction; tensor QR ~100× vs Xeon 8480+; tensor SVD ~10× | A100 | ECDSA HIGH — alternative to stabilizer/kickmix sim path; MPS/PEPS evaluates reversible secp256k1 circuits beyond Clifford | cuTensorNet |
cuda-ega-gpu-aggregation | 6.45–29.12× over CPU multi-pass EGA; group-by hash 19.4× | NVIDIA GPU | undefect HIGH — defect-corpus aggregation at planetary scale; unsandbox HIGH — telemetry queue aggregation | VLDB Top-k EGA |
cuda-bicgstab-ilu-spmv | SpTRSV 10.7×; ILU0 BiCGSTAB 3.2× vs cuSPARSE on MI210; GMRES(30) block-ISAI 1.4–6.9× | V100 / MI210 | ECDSA MEDIUM (sparse LA over GF(p) underpins lattice / index-calc); undefect MEDIUM (spectral analysis on DAG) | arXiv 2508.04917 |
cuda-icicle-snark-groth16 | ICICLE-Snark fastest Groth16 today; Mina GPU 3× over libsnark; NTT 91% of prover at large sizes | RTX 4090 / A100 | ECDSA MEDIUM (zk + MSM stack shares finite-field discipline); undefect MEDIUM (zk-prover defect scanning) | ICICLE-Snark |
Wave 3 filter-outs: AMGX algebraic multigrid (2–5×), GROMACS GPU (2–3×), NVOFA optical flow (7–10× borderline, dedicated hardware unit), Junction-tree BP per-message (0.68–9.18×), batched L-BFGS (134× single-case, not generalized). All below 10× or insufficiently general; revisit when shape changes.
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