linux-cpu5cf78c7ec0ad9516dd78bab546d5e7bd42fa31022cd0fd6445dcd9aad98990f76c17e33e773c4a7dGenerated from the explicit runs below; each section retains its own provenance and thread settings.
Source report: data/results/linux-cpu/cpu/einsum/20261008_044146/linalg_jvp_vjp_report.md.
cpu/linalg_jvp_vjplinux-cpu20261008_044146Latest run: ./scripts/run_all.sh 1.
Derived from the CPU ops CSV under data/results/linux-cpu/cpu/einsum/20261008_044146.
5cf78c7ec0ad9516dd78bab546d5e7bd42fa3102AMD Ryzen 9 9955HX 16-Core ProcessorAuthenticAMD3211621Linux-7.0.0-38-generic-x86_64-with-glibc2.3911FALSE11111111--xla_cpu_multi_thread_eigen=false --xla_cpu_experimental_ynn_fusion_type= intra_op_parallelism_threads=1system-mklblasmkl2026.0.1/opt/intel/oneapi/mkl/latest/opt/intel/oneapi/mkl/latest/lib/libmkl_rt.somkl, version 2.12.0+cpu, BLAS_INFO mkl, LAPACK_INFO mkl/workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/torch/lib/libgomp.so.1JAX: dot backend xla_cpu, version 0.10.1, jaxlib 0.10.1, default backend cpu, LAPACK provider none_detected
CSV: data/results/linux-cpu/cpu/einsum/20261008_044146/cpu_ops_t1_20261008_044146.csv
data/results/linux-cpu/cpu/einsum/20261008_044146/linalg_jvp_vjp_t1_20261008_044146.mdMedian ± IQR (ms). Missing backends are shown as -.
tenferro-rs JVP/VJP use trace-mode AdContext; PyTorch uses torch.func.jvp /
vjp; JAX uses jax.jvp / jax.vjp.
| suite | benchmark | dtype | threads | shape | tenferro-rs trace mode (ms) | PyTorch Python (ms) | JAX Python (XLA CPU) (ms) |
|---|---|---|---|---|---|---|---|
| large | grad_sum_eigh_jvp |
f64 | 1 | 1024x1024 |
348.894 ± 0.537 | 493.351 ± 1.190 | 323.311 ± 0.574 |
| large | grad_sum_eigh_jvp |
f64 | 1 | 256x256 |
8.925 ± 0.019 | 11.077 ± 0.037 | 8.384 ± 0.027 |
| large | grad_sum_eigh_jvp |
f64 | 1 | 512x512 |
55.310 ± 0.214 | 74.532 ± 0.165 | 51.286 ± 0.247 |
| large | grad_sum_eigh_vjp |
f64 | 1 | 1024x1024 |
350.559 ± 0.476 | 356.213 ± 0.625 | 320.925 ± 0.413 |
| large | grad_sum_eigh_vjp |
f64 | 1 | 256x256 |
8.878 ± 0.023 | 8.978 ± 0.048 | 8.350 ± 0.064 |
| large | grad_sum_eigh_vjp |
f64 | 1 | 512x512 |
55.276 ± 0.129 | 56.729 ± 0.082 | 51.124 ± 0.305 |
| large | grad_sum_lu_jvp |
f64 | 1 | 1024x1024 |
282.367 ± 0.360 | 609.271 ± 2.501 | 234.359 ± 2.491 |
| large | grad_sum_lu_jvp |
f64 | 1 | 256x256 |
5.342 ± 0.017 | 10.825 ± 0.076 | 4.599 ± 0.016 |
| large | grad_sum_lu_jvp |
f64 | 1 | 512x512 |
38.496 ± 0.209 | 82.831 ± 0.187 | 32.653 ± 0.091 |
| large | grad_sum_lu_vjp |
f64 | 1 | 1024x1024 |
276.074 ± 0.653 | 326.508 ± 5.939 | 235.357 ± 1.248 |
| large | grad_sum_lu_vjp |
f64 | 1 | 256x256 |
5.465 ± 0.006 | 5.832 ± 0.023 | 4.504 ± 0.022 |
| large | grad_sum_lu_vjp |
f64 | 1 | 512x512 |
38.204 ± 0.065 | 43.382 ± 0.117 | 31.662 ± 0.072 |
| large | grad_sum_qr_jvp |
f64 | 1 | 1024x1024 |
365.127 ± 0.494 | 418.926 ± 1.604 | 301.561 ± 1.265 |
| large | grad_sum_qr_jvp |
f64 | 1 | 256x256 |
7.699 ± 0.038 | 8.272 ± 0.018 | 6.457 ± 0.017 |
| large | grad_sum_qr_jvp |
f64 | 1 | 512x512 |
57.267 ± 0.091 | 66.265 ± 0.747 | 42.961 ± 0.350 |
| large | grad_sum_qr_vjp |
f64 | 1 | 1024x1024 |
360.289 ± 0.408 | 407.364 ± 2.039 | 331.027 ± 0.619 |
| large | grad_sum_qr_vjp |
f64 | 1 | 256x256 |
7.658 ± 0.014 | 8.287 ± 0.025 | 6.462 ± 0.041 |
| large | grad_sum_qr_vjp |
f64 | 1 | 512x512 |
54.552 ± 0.101 | 68.759 ± 0.876 | 47.262 ± 0.587 |
| large | grad_sum_solve_jvp |
f64 | 1 | 1024x1024,rhs=1 |
27.206 ± 0.092 | 36.963 ± 0.129 | 22.241 ± 0.089 |
| large | grad_sum_solve_jvp |
f64 | 1 | 256x256,rhs=1 |
0.720 ± 0.006 | 1.068 ± 0.017 | 0.610 ± 0.008 |
| large | grad_sum_solve_jvp |
f64 | 1 | 512x512,rhs=1 |
4.274 ± 0.066 | 5.398 ± 0.025 | 4.051 ± 0.020 |
| large | grad_sum_solve_vjp |
f64 | 1 | 1024x1024,rhs=1 |
26.804 ± 0.083 | 65.111 ± 0.487 | 22.017 ± 0.050 |
| large | grad_sum_solve_vjp |
f64 | 1 | 256x256,rhs=1 |
0.705 ± 0.006 | 1.564 ± 0.010 | 0.623 ± 0.003 |
| large | grad_sum_solve_vjp |
f64 | 1 | 512x512,rhs=1 |
4.216 ± 0.026 | 9.538 ± 0.026 | 4.087 ± 0.015 |
| large | grad_sum_svd_s_jvp |
f64 | 1 | 1024x1024 |
676.216 ± 3.145 | 908.650 ± 4.960 | 607.557 ± 3.247 |
| large | grad_sum_svd_s_jvp |
f64 | 1 | 256x256 |
16.134 ± 0.091 | 20.226 ± 0.068 | 14.537 ± 0.023 |
| large | grad_sum_svd_s_jvp |
f64 | 1 | 512x512 |
107.400 ± 0.993 | 148.129 ± 0.664 | 99.509 ± 0.661 |
| large | grad_sum_svd_s_vjp |
f64 | 1 | 1024x1024 |
661.978 ± 15.387 | 684.864 ± 6.642 | 607.308 ± 1.994 |
| large | grad_sum_svd_s_vjp |
f64 | 1 | 256x256 |
16.094 ± 0.039 | 16.906 ± 0.064 | 14.569 ± 0.050 |
| large | grad_sum_svd_s_vjp |
f64 | 1 | 512x512 |
106.149 ± 2.119 | 125.661 ± 0.489 | 99.615 ± 0.461 |
| small | grad_sum_eigh_jvp |
f64 | 1 | 16x16 |
0.066 ± 0.000 | 0.124 ± 0.001 | 0.032 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 1 | 2x2 |
0.046 ± 0.000 | 0.097 ± 0.002 | 0.009 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 1 | 32x32 |
0.126 ± 0.001 | 0.197 ± 0.003 | 0.102 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 1 | 4x4 |
0.047 ± 0.000 | 0.098 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 1 | 8x8 |
0.052 ± 0.000 | 0.105 ± 0.002 | 0.013 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 1 | 16x16 |
0.068 ± 0.000 | 0.107 ± 0.001 | 0.033 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 1 | 2x2 |
0.046 ± 0.000 | 0.083 ± 0.001 | 0.010 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 1 | 32x32 |
0.125 ± 0.000 | 0.171 ± 0.004 | 0.103 ± 0.001 |
| small | grad_sum_eigh_vjp |
f64 | 1 | 4x4 |
0.048 ± 0.000 | 0.086 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 1 | 8x8 |
0.053 ± 0.000 | 0.090 ± 0.001 | 0.013 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 1 | 16x16 |
0.055 ± 0.000 | 0.175 ± 0.004 | 0.015 ± 0.002 |
| small | grad_sum_lu_jvp |
f64 | 1 | 2x2 |
0.049 ± 0.000 | 0.158 ± 0.003 | 0.011 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 1 | 32x32 |
0.079 ± 0.000 | 0.216 ± 0.004 | 0.030 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 1 | 4x4 |
0.049 ± 0.000 | 0.157 ± 0.003 | 0.011 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 1 | 8x8 |
0.051 ± 0.000 | 0.161 ± 0.004 | 0.017 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 1 | 16x16 |
0.056 ± 0.000 | 0.138 ± 0.001 | 0.014 ± 0.001 |
| small | grad_sum_lu_vjp |
f64 | 1 | 2x2 |
0.049 ± 0.000 | 0.127 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 1 | 32x32 |
0.083 ± 0.000 | 0.166 ± 0.004 | 0.031 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 1 | 4x4 |
0.050 ± 0.000 | 0.127 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 1 | 8x8 |
0.051 ± 0.000 | 0.130 ± 0.001 | 0.013 ± 0.001 |
| small | grad_sum_qr_jvp |
f64 | 1 | 16x16 |
0.091 ± 0.001 | 0.124 ± 0.002 | 0.016 ± 0.000 |
| small | grad_sum_qr_jvp |
f64 | 1 | 2x2 |
0.081 ± 0.000 | 0.112 ± 0.003 | 0.011 ± 0.001 |
| small | grad_sum_qr_jvp |
f64 | 1 | 32x32 |
0.119 ± 0.001 | 0.156 ± 0.002 | 0.039 ± 0.001 |
| small | grad_sum_qr_jvp |
f64 | 1 | 4x4 |
0.082 ± 0.000 | 0.110 ± 0.002 | 0.011 ± 0.000 |
| small | grad_sum_qr_jvp |
f64 | 1 | 8x8 |
0.084 ± 0.001 | 0.114 ± 0.001 | 0.014 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 1 | 16x16 |
0.094 ± 0.000 | 0.131 ± 0.001 | 0.015 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 1 | 2x2 |
0.083 ± 0.000 | 0.118 ± 0.002 | 0.010 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 1 | 32x32 |
0.125 ± 0.000 | 0.161 ± 0.004 | 0.039 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 1 | 4x4 |
0.084 ± 0.000 | 0.117 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 1 | 8x8 |
0.086 ± 0.000 | 0.119 ± 0.001 | 0.010 ± 0.001 |
| small | grad_sum_solve_jvp |
f64 | 1 | 16x16,rhs=1 |
0.030 ± 0.000 | 0.266 ± 0.002 | 0.014 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 1 | 2x2,rhs=1 |
0.028 ± 0.000 | 0.259 ± 0.002 | 0.011 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 1 | 32x32,rhs=1 |
0.038 ± 0.000 | 0.277 ± 0.002 | 0.015 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 1 | 4x4,rhs=1 |
0.029 ± 0.000 | 0.257 ± 0.002 | 0.011 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 1 | 8x8,rhs=1 |
0.029 ± 0.000 | 0.259 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 1 | 16x16,rhs=1 |
0.030 ± 0.000 | 0.117 ± 0.001 | 0.016 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 1 | 2x2,rhs=1 |
0.028 ± 0.000 | 0.112 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 1 | 32x32,rhs=1 |
0.037 ± 0.000 | 0.132 ± 0.002 | 0.017 ± 0.001 |
| small | grad_sum_solve_vjp |
f64 | 1 | 4x4,rhs=1 |
0.028 ± 0.000 | 0.111 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 1 | 8x8,rhs=1 |
0.029 ± 0.000 | 0.114 ± 0.001 | 0.013 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 1 | 16x16 |
0.070 ± 0.000 | 0.210 ± 0.005 | 0.053 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 1 | 2x2 |
0.027 ± 0.000 | 0.147 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 1 | 32x32 |
0.174 ± 0.000 | 0.346 ± 0.009 | 0.158 ± 0.001 |
| small | grad_sum_svd_s_jvp |
f64 | 1 | 4x4 |
0.030 ± 0.000 | 0.151 ± 0.001 | 0.013 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 1 | 8x8 |
0.041 ± 0.000 | 0.167 ± 0.005 | 0.018 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 1 | 16x16 |
0.069 ± 0.000 | 0.144 ± 0.001 | 0.054 ± 0.004 |
| small | grad_sum_svd_s_vjp |
f64 | 1 | 2x2 |
0.026 ± 0.000 | 0.092 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 1 | 32x32 |
0.173 ± 0.001 | 0.269 ± 0.005 | 0.160 ± 0.001 |
| small | grad_sum_svd_s_vjp |
f64 | 1 | 4x4 |
0.030 ± 0.000 | 0.096 ± 0.001 | 0.014 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 1 | 8x8 |
0.041 ± 0.000 | 0.107 ± 0.001 | 0.018 ± 0.000 |
grad_sum_eigh: loss = sum(eigenvalues); w.r.t. SPD input matrix Agrad_sum_lu: loss = sum(L) + sum(U); w.r.t. input matrix Agrad_sum_qr: loss = sum(Q) + sum(R); w.r.t. input matrix Agrad_sum_solve: loss = sum(solve(A, b)); w.r.t. input matrix A (rhs fixed)grad_sum_svd_s: loss = sum(singular values); w.r.t. input matrix ACompleted einsum measurements retained from the initial collection; CPU ops were recollected after the input-restoration fix. Component commits and original metadata are recorded in run.yaml and einsum_run.yaml. Exact commands: data/results/linux-cpu/cpu/refresh/20261008_044136/commands_remaining.sh.
Source report: data/results/linux-cpu/cpu/einsum/20261008_053424/linalg_jvp_vjp_report.md.
cpu/linalg_jvp_vjplinux-cpu20261008_053424Latest run: ./scripts/run_all.sh 4.
Derived from the CPU ops CSV under data/results/linux-cpu/cpu/einsum/20261008_053424.
5cf78c7ec0ad9516dd78bab546d5e7bd42fa3102AMD Ryzen 9 9955HX 16-Core ProcessorAuthenticAMD3211621Linux-7.0.0-38-generic-x86_64-with-glibc2.3944FALSE44444444--xla_cpu_multi_thread_eigen=true --xla_cpu_experimental_ynn_fusion_type= intra_op_parallelism_threads=4system-mklblasmkl2026.0.1/opt/intel/oneapi/mkl/latest/opt/intel/oneapi/mkl/latest/lib/libmkl_rt.somkl, version 2.12.0+cpu, BLAS_INFO mkl, LAPACK_INFO mkl/workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/torch/lib/libgomp.so.1JAX: dot backend xla_cpu, version 0.10.1, jaxlib 0.10.1, default backend cpu, LAPACK provider none_detected
CSV: data/results/linux-cpu/cpu/einsum/20261008_053424/cpu_ops_t4_20261008_053424.csv
data/results/linux-cpu/cpu/einsum/20261008_053424/linalg_jvp_vjp_t4_20261008_053424.mdMedian ± IQR (ms). Missing backends are shown as -.
tenferro-rs JVP/VJP use trace-mode AdContext; PyTorch uses torch.func.jvp /
vjp; JAX uses jax.jvp / jax.vjp.
| suite | benchmark | dtype | threads | shape | tenferro-rs trace mode (ms) | PyTorch Python (ms) | JAX Python (XLA CPU) (ms) |
|---|---|---|---|---|---|---|---|
| large | grad_sum_eigh_jvp |
f64 | 4 | 1024x1024 |
110.595 ± 1.190 | 158.889 ± 0.821 | 142.499 ± 2.814 |
| large | grad_sum_eigh_jvp |
f64 | 4 | 256x256 |
4.346 ± 0.042 | 4.765 ± 0.018 | 7.224 ± 0.444 |
| large | grad_sum_eigh_jvp |
f64 | 4 | 512x512 |
19.898 ± 0.698 | 25.879 ± 0.099 | 30.225 ± 0.888 |
| large | grad_sum_eigh_vjp |
f64 | 4 | 1024x1024 |
110.256 ± 0.489 | 112.010 ± 1.240 | 141.547 ± 1.556 |
| large | grad_sum_eigh_vjp |
f64 | 4 | 256x256 |
4.343 ± 0.053 | 4.113 ± 0.017 | 6.640 ± 0.531 |
| large | grad_sum_eigh_vjp |
f64 | 4 | 512x512 |
20.180 ± 0.389 | 19.726 ± 0.056 | 29.945 ± 1.244 |
| large | grad_sum_lu_jvp |
f64 | 4 | 1024x1024 |
81.038 ± 0.600 | 180.950 ± 0.557 | 70.229 ± 3.164 |
| large | grad_sum_lu_jvp |
f64 | 4 | 256x256 |
1.911 ± 0.025 | 4.312 ± 0.052 | 1.852 ± 0.325 |
| large | grad_sum_lu_jvp |
f64 | 4 | 512x512 |
12.071 ± 1.972 | 30.860 ± 0.381 | 10.272 ± 1.694 |
| large | grad_sum_lu_vjp |
f64 | 4 | 1024x1024 |
79.189 ± 0.214 | 92.926 ± 0.252 | 68.551 ± 1.687 |
| large | grad_sum_lu_vjp |
f64 | 4 | 256x256 |
2.167 ± 0.024 | 2.125 ± 0.016 | 1.753 ± 0.087 |
| large | grad_sum_lu_vjp |
f64 | 4 | 512x512 |
11.894 ± 0.095 | 14.338 ± 0.081 | 10.084 ± 0.540 |
| large | grad_sum_qr_jvp |
f64 | 4 | 1024x1024 |
113.904 ± 0.650 | 131.511 ± 0.602 | 112.847 ± 0.720 |
| large | grad_sum_qr_jvp |
f64 | 4 | 256x256 |
3.322 ± 0.030 | 3.344 ± 0.594 | 3.794 ± 0.308 |
| large | grad_sum_qr_jvp |
f64 | 4 | 512x512 |
22.380 ± 0.604 | 25.576 ± 0.157 | 19.457 ± 1.904 |
| large | grad_sum_qr_vjp |
f64 | 4 | 1024x1024 |
111.495 ± 0.286 | 126.004 ± 0.331 | 114.857 ± 2.341 |
| large | grad_sum_qr_vjp |
f64 | 4 | 256x256 |
3.191 ± 0.047 | 3.534 ± 0.046 | 3.846 ± 0.345 |
| large | grad_sum_qr_vjp |
f64 | 4 | 512x512 |
20.052 ± 0.347 | 25.700 ± 0.527 | 20.776 ± 2.378 |
| large | grad_sum_solve_jvp |
f64 | 4 | 1024x1024,rhs=1 |
8.244 ± 1.917 | 11.528 ± 0.113 | 9.718 ± 0.888 |
| large | grad_sum_solve_jvp |
f64 | 4 | 256x256,rhs=1 |
0.350 ± 0.002 | 0.624 ± 0.019 | 0.446 ± 0.013 |
| large | grad_sum_solve_jvp |
f64 | 4 | 512x512,rhs=1 |
1.491 ± 0.079 | 3.028 ± 0.098 | 2.864 ± 0.761 |
| large | grad_sum_solve_vjp |
f64 | 4 | 1024x1024,rhs=1 |
7.920 ± 0.100 | 18.763 ± 0.164 | 9.293 ± 0.231 |
| large | grad_sum_solve_vjp |
f64 | 4 | 256x256,rhs=1 |
0.347 ± 0.009 | 0.701 ± 0.018 | 0.450 ± 0.020 |
| large | grad_sum_solve_vjp |
f64 | 4 | 512x512,rhs=1 |
1.437 ± 0.006 | 3.944 ± 0.017 | 2.570 ± 0.497 |
| large | grad_sum_svd_s_jvp |
f64 | 4 | 1024x1024 |
265.149 ± 2.585 | 344.704 ± 1.889 | 354.826 ± 6.893 |
| large | grad_sum_svd_s_jvp |
f64 | 4 | 256x256 |
10.761 ± 0.090 | 12.931 ± 0.063 | 12.295 ± 0.613 |
| large | grad_sum_svd_s_jvp |
f64 | 4 | 512x512 |
50.354 ± 1.528 | 68.240 ± 0.589 | 75.044 ± 1.311 |
| large | grad_sum_svd_s_vjp |
f64 | 4 | 1024x1024 |
266.303 ± 5.420 | 280.895 ± 4.921 | 360.760 ± 4.876 |
| large | grad_sum_svd_s_vjp |
f64 | 4 | 256x256 |
10.735 ± 0.024 | 12.043 ± 0.043 | 12.622 ± 1.063 |
| large | grad_sum_svd_s_vjp |
f64 | 4 | 512x512 |
50.639 ± 0.336 | 68.901 ± 1.123 | 75.148 ± 0.982 |
| small | grad_sum_eigh_jvp |
f64 | 4 | 16x16 |
0.066 ± 0.000 | 0.123 ± 0.002 | 0.034 ± 0.001 |
| small | grad_sum_eigh_jvp |
f64 | 4 | 2x2 |
0.046 ± 0.000 | 0.095 ± 0.002 | 0.009 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 4 | 32x32 |
0.130 ± 0.000 | 0.202 ± 0.003 | 0.103 ± 0.001 |
| small | grad_sum_eigh_jvp |
f64 | 4 | 4x4 |
0.048 ± 0.000 | 0.097 ± 0.002 | 0.012 ± 0.000 |
| small | grad_sum_eigh_jvp |
f64 | 4 | 8x8 |
0.052 ± 0.000 | 0.104 ± 0.002 | 0.013 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 4 | 16x16 |
0.067 ± 0.000 | 0.106 ± 0.002 | 0.034 ± 0.001 |
| small | grad_sum_eigh_vjp |
f64 | 4 | 2x2 |
0.047 ± 0.000 | 0.083 ± 0.002 | 0.010 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 4 | 32x32 |
0.131 ± 0.000 | 0.176 ± 0.003 | 0.105 ± 0.001 |
| small | grad_sum_eigh_vjp |
f64 | 4 | 4x4 |
0.048 ± 0.000 | 0.084 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_eigh_vjp |
f64 | 4 | 8x8 |
0.052 ± 0.000 | 0.088 ± 0.001 | 0.013 ± 0.001 |
| small | grad_sum_lu_jvp |
f64 | 4 | 16x16 |
0.055 ± 0.000 | 0.179 ± 0.005 | 0.017 ± 0.001 |
| small | grad_sum_lu_jvp |
f64 | 4 | 2x2 |
0.049 ± 0.000 | 0.163 ± 0.005 | 0.011 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 4 | 32x32 |
0.088 ± 0.001 | 0.220 ± 0.004 | 0.030 ± 0.005 |
| small | grad_sum_lu_jvp |
f64 | 4 | 4x4 |
0.049 ± 0.000 | 0.162 ± 0.003 | 0.012 ± 0.000 |
| small | grad_sum_lu_jvp |
f64 | 4 | 8x8 |
0.050 ± 0.000 | 0.163 ± 0.003 | 0.018 ± 0.004 |
| small | grad_sum_lu_vjp |
f64 | 4 | 16x16 |
0.055 ± 0.000 | 0.140 ± 0.001 | 0.015 ± 0.001 |
| small | grad_sum_lu_vjp |
f64 | 4 | 2x2 |
0.049 ± 0.000 | 0.127 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 4 | 32x32 |
0.094 ± 0.000 | 0.169 ± 0.005 | 0.030 ± 0.005 |
| small | grad_sum_lu_vjp |
f64 | 4 | 4x4 |
0.049 ± 0.000 | 0.129 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_lu_vjp |
f64 | 4 | 8x8 |
0.050 ± 0.000 | 0.130 ± 0.001 | 0.016 ± 0.000 |
| small | grad_sum_qr_jvp |
f64 | 4 | 16x16 |
0.090 ± 0.000 | 0.128 ± 0.002 | 0.015 ± 0.001 |
| small | grad_sum_qr_jvp |
f64 | 4 | 2x2 |
0.081 ± 0.000 | 0.113 ± 0.002 | 0.011 ± 0.000 |
| small | grad_sum_qr_jvp |
f64 | 4 | 32x32 |
0.121 ± 0.001 | 0.160 ± 0.005 | 0.041 ± 0.001 |
| small | grad_sum_qr_jvp |
f64 | 4 | 4x4 |
0.082 ± 0.000 | 0.112 ± 0.002 | 0.012 ± 0.000 |
| small | grad_sum_qr_jvp |
f64 | 4 | 8x8 |
0.084 ± 0.000 | 0.115 ± 0.002 | 0.016 ± 0.002 |
| small | grad_sum_qr_vjp |
f64 | 4 | 16x16 |
0.093 ± 0.000 | 0.132 ± 0.001 | 0.015 ± 0.001 |
| small | grad_sum_qr_vjp |
f64 | 4 | 2x2 |
0.082 ± 0.000 | 0.118 ± 0.001 | 0.010 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 4 | 32x32 |
0.123 ± 0.000 | 0.162 ± 0.004 | 0.041 ± 0.002 |
| small | grad_sum_qr_vjp |
f64 | 4 | 4x4 |
0.084 ± 0.000 | 0.118 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_qr_vjp |
f64 | 4 | 8x8 |
0.085 ± 0.000 | 0.119 ± 0.002 | 0.014 ± 0.002 |
| small | grad_sum_solve_jvp |
f64 | 4 | 16x16,rhs=1 |
0.030 ± 0.000 | 0.263 ± 0.002 | 0.014 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 4 | 2x2,rhs=1 |
0.029 ± 0.000 | 0.255 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 4 | 32x32,rhs=1 |
0.038 ± 0.000 | 0.278 ± 0.003 | 0.017 ± 0.003 |
| small | grad_sum_solve_jvp |
f64 | 4 | 4x4,rhs=1 |
0.028 ± 0.000 | 0.256 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_solve_jvp |
f64 | 4 | 8x8,rhs=1 |
0.029 ± 0.000 | 0.256 ± 0.002 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 4 | 16x16,rhs=1 |
0.030 ± 0.000 | 0.115 ± 0.002 | 0.016 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 4 | 2x2,rhs=1 |
0.028 ± 0.000 | 0.111 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 4 | 32x32,rhs=1 |
0.037 ± 0.000 | 0.132 ± 0.001 | 0.017 ± 0.001 |
| small | grad_sum_solve_vjp |
f64 | 4 | 4x4,rhs=1 |
0.028 ± 0.000 | 0.110 ± 0.001 | 0.012 ± 0.000 |
| small | grad_sum_solve_vjp |
f64 | 4 | 8x8,rhs=1 |
0.028 ± 0.000 | 0.112 ± 0.001 | 0.013 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 4 | 16x16 |
0.069 ± 0.000 | 0.206 ± 0.004 | 0.057 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 4 | 2x2 |
0.027 ± 0.000 | 0.144 ± 0.001 | 0.011 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 4 | 32x32 |
0.183 ± 0.001 | 0.354 ± 0.003 | 0.163 ± 0.002 |
| small | grad_sum_svd_s_jvp |
f64 | 4 | 4x4 |
0.030 ± 0.000 | 0.149 ± 0.001 | 0.014 ± 0.000 |
| small | grad_sum_svd_s_jvp |
f64 | 4 | 8x8 |
0.041 ± 0.000 | 0.165 ± 0.003 | 0.018 ± 0.001 |
| small | grad_sum_svd_s_vjp |
f64 | 4 | 16x16 |
0.069 ± 0.000 | 0.141 ± 0.001 | 0.057 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 4 | 2x2 |
0.026 ± 0.000 | 0.090 ± 0.002 | 0.011 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 4 | 32x32 |
0.182 ± 0.000 | 0.272 ± 0.003 | 0.176 ± 0.003 |
| small | grad_sum_svd_s_vjp |
f64 | 4 | 4x4 |
0.030 ± 0.000 | 0.094 ± 0.001 | 0.015 ± 0.000 |
| small | grad_sum_svd_s_vjp |
f64 | 4 | 8x8 |
0.041 ± 0.000 | 0.104 ± 0.001 | 0.018 ± 0.002 |
grad_sum_eigh: loss = sum(eigenvalues); w.r.t. SPD input matrix Agrad_sum_lu: loss = sum(L) + sum(U); w.r.t. input matrix Agrad_sum_qr: loss = sum(Q) + sum(R); w.r.t. input matrix Agrad_sum_solve: loss = sum(solve(A, b)); w.r.t. input matrix A (rhs fixed)grad_sum_svd_s: loss = sum(singular values); w.r.t. input matrix AThis Linux report alias mirrors the same sequential amd-cpu collection; no additional timing was performed.