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LICENSE.txt

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+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+This data release is licensed under the Creative Commons Attribution
+4.0 International License. You may share and adapt the material for
+any purpose, including commercial, provided you give appropriate
+credit, link to the license, and indicate if changes were made.
+
+Full text: https://creativecommons.org/licenses/by/4.0/legalcode

README.md

@@ -1,3 +1,87 @@
 ---
-license: cc-by-nc-nd-4.0
+license: cc-by-4.0
+language: []
+pretty_name: "Exp 3B: Embedded topology in trained recurrent operators"
+size_categories:
+  - 10K<n<100K
+tags:
+  - dynamical-systems
+  - delay-embedding
+  - reservoir-computing
+  - persistent-homology
+  - recurrent-neural-networks
+  - topology
+viewer: false
 ---
+
+# Exp 3B: Embedded topology in trained recurrent operators
+
+A 14,553-configuration computational sweep of recurrent network
+architectures trained on dynamical systems. Each configuration's
+hidden-state activations were measured for topological fidelity to
+the driving system using persistent homology and Gauss linking
+integrals. Six post-hoc analyses on saved checkpoints probe the
+operator properties the embedding theorems describe abstractly.
+
+This dataset is the empirical companion to the manuscript
+"Topological reconstruction in trained recurrent operators: a
+14,553-configuration empirical sweep" (O'Reilly-Shah, Selvitella,
+2026; arXiv to follow).
+
+## Scope
+
+- **7 architecture families**: VanillaRNN, GRU, LSTM, ESN, Mamba,
+  RWKV, Transformer.
+- **9 hidden dimensions**: 3, 5, 8, 16, 32, 64, 128, 256, 512.
+- **3 depths**: 1, 2, 4.
+- **7 tasks**: circle ($S^1$), torus ($T^2$), torus3 ($T^3$),
+  scalar circle, scalar torus, Lorenz, four-dimensional Qi system.
+- **11 random seeds** per cell.
+- **Total: 14,553 trained configurations.**
+
+## Layout
+
+```
+master_results.json          aggregate of all per-config results
+master_results.jsonl         streaming form of master_results.json
+gpu_co2_report.{md,json}     compute footprint (GPU-hours, kWh, CO2)
+batch_summary.json           per-batch aggregated post-hoc metrics
+figures/                     20 PNG figures from the analysis pipeline
+code/                        analysis pipeline source
+configs/<task>/<run_id>/
+    results.json             training output (loss, topology metrics)
+    post_analysis.json       spectral radius + collision + novel-traj
+    dyn_variants.json        altered-dynamics evaluation
+    lorenz_ic_sweep.json     Lorenz only: 8-IC robustness
+    cross_task.json          cross-task transfer evaluation
+    gs_convergence.json      empirical ESP convergence test
+    history.json             training curve + per-eval-epoch topology
+    epoch_*.pt               final-epoch checkpoint
+```
+
+Hostnames in source data have been mapped to opaque labels
+(`host_A_a2000`, `host_B_rtx4000`, etc.) so the deposit does not
+leak machine identity.
+
+## Reproducibility
+
+Source code for the training and analysis pipeline is under `code/`.
+Re-running requires PyTorch, NumPy, scipy, and ripser. See
+`code/scripts/` for individual analyses and
+`code/experiments/exp3b_taxonomy.py` for the master training script.
+
+## License
+
+CC BY 4.0. Cite the accompanying manuscript when reusing.
+
+## Citation
+
+```bibtex
+@article{oreillyshah_exp3b_2026,
+  author  = {O'Reilly-Shah, Vikas N. and Selvitella, Alessandro M.},
+  title   = {Topological reconstruction in trained recurrent operators:
+             a 14,553-configuration empirical sweep},
+  year    = {2026},
+  note    = {arXiv preprint, DOI to be assigned}
+}
+```

configs/torus_scalar/esn_h128_d1_torus_scalar/cross_task.json

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+{
+  "run_id": "esn_h128_d1_torus_scalar",
+  "arch": "esn",
+  "hidden": 128,
+  "depth": 1,
+  "task": "torus_scalar",
+  "seed": 0,
+  "input_dim": 1,
+  "targets": {
+    "circle_scalar": {
+      "is_chaotic": true,
+      "renormalized_to_source": false,
+      "seq_betti_gap": {
+        "0": 0,
+        "1": 1
+      },
+      "seq_betti_tau": {
+        "0": 0,
+        "1": 3
+      },
+      "seq_tp": 1.2724310923367739,
+      "seq_linking": {
+        "n_orbits": 8,
+        "linking_numbers": [
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+          0.25254037976264954,
+          0.42847394943237305,
+          0.8315082788467407,
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+          1.0563185214996338,
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+          0.6191733479499817,
+          0.6245583891868591
+        ],
+        "mean_abs_linking": 0.5352147322015038,
+        "max_abs_linking": 1.0563185214996338,
+        "nonzero_pairs": 22,
+        "total_pairs": 28,
+        "pca_projected": true,
+        "original_dim": 128
+      },
+      "seq_match": true,
+      "seq_linking_pass": true,
+      "seq_b1_ge1": true
+    },
+    "lorenz": {
+      "is_chaotic": true,
+      "renormalized_to_source": false,
+      "seq_betti_gap": {
+        "0": 0,
+        "1": 111,
+        "2": 1
+      },
+      "seq_betti_tau": {
+        "0": 0,
+        "1": 9,
+        "2": 0
+      },
+      "seq_tp": 3.013892497867346,
+      "seq_linking": {
+        "n_orbits": 8,
+        "linking_numbers": [
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+          -1.577172040939331,
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+        "mean_abs_linking": 0.8460778371830072,
+        "max_abs_linking": 3.071986675262451,
+        "nonzero_pairs": 24,
+        "total_pairs": 28,
+        "pca_projected": true,
+        "original_dim": 128
+      },
+      "seq_match": true,
+      "seq_linking_pass": true,
+      "seq_b1_ge1": true
+    },
+    "hyper_rossler": {
+      "is_chaotic": true,
+      "renormalized_to_source": false,
+      "seq_betti_gap": {
+        "0": 0,
+        "1": 1,
+        "2": 0
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+      "seq_betti_tau": {
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+      "seq_tp": 0.21741062216460705,
+      "seq_linking": {
+        "n_orbits": 8,
+        "linking_numbers": [
+          2.1984324121149257e-06,
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+          -6.071645202609943e-06,
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+          -2.288796041582941e-09,
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+          -0.0017323256470263004,
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+          2.6640347705608747e-09,
+          -6.962248153286055e-05,
+          -0.006859643384814262,
+          -4.8079773051767916e-08,
+          8.19079062353012e-08,
+          0.004107274115085602,
+          -1.2290875019971281e-06,
+          2.8435559329409443e-07,
+          -5.430346732282487e-07,
+          2.748582517142495e-07,
+          0.0002630392846185714
+        ],
+        "mean_abs_linking": 0.0014773622923426124,
+        "max_abs_linking": 0.011683753691613674,
+        "nonzero_pairs": 0,
+        "total_pairs": 28,
+        "pca_projected": true,
+        "original_dim": 128
+      },
+      "seq_match": true,
+      "seq_linking_pass": false,
+      "seq_b1_ge1": true
+    }
+  },
+  "elapsed_sec": 121.38790345191956
+}

configs/torus_scalar/esn_h128_d1_torus_scalar/dyn_variants.json

@@ -0,0 +1,124 @@
+{
+  "run_id": "esn_h128_d1_torus_scalar",
+  "arch": "esn",
+  "hidden": 128,
+  "depth": 1,
+  "task": "torus_scalar",
+  "seed": 0,
+  "is_chaotic": true,
+  "sigma_quarter": {
+    "variant": "sigma_quarter",
+    "step_sigma": 0.125,
+    "seq_betti_gap": {
+      "0": 23,
+      "1": 2,
+      "2": 10
+    },
+    "seq_betti_tau": {
+      "0": 23,
+      "1": 34,
+      "2": 7
+    },
+    "seq_tp": 1.0514098341700446,
+    "seq_linking": {
+      "n_orbits": 8,
+      "linking_numbers": [
+        8.058707479108307e-09,
+        -0.5418314337730408,
+        0.00014954089419916272,
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+        6.084133019612636e-08,
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+        7.611886232439247e-09,
+        2.7473998187588222e-08,
+        -1.3431543877118202e-08
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+      "max_abs_linking": 0.5481404662132263,
+      "nonzero_pairs": 7,
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+      "original_dim": 128
+    },
+    "seq_linking_pass": false,
+    "seq_b1_ge1": true,
+    "seq_match": true
+  },
+  "sigma_4x": {
+    "variant": "sigma_4x",
+    "step_sigma": 2.0,
+    "seq_betti_gap": {
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+      "1": 1,
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+    "seq_match": true
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+  "elapsed_sec": 64.57554507255554
+}

configs/torus_scalar/esn_h128_d1_torus_scalar/gs_convergence.json

@@ -0,0 +1,16 @@
+{
+  "run_id": "esn_h128_d1_torus_scalar",
+  "arch": "esn",
+  "hidden": 128,
+  "depth": 1,
+  "task": "torus_scalar",
+  "seed": 0,
+  "n_h0_samples": 5,
+  "T_total": 2000,
+  "T_convergence_window": 500,
+  "mean_dist_early": 0.08294231204954497,
+  "mean_dist_late": 1.6456105317956825e-07,
+  "ratio_late_over_early": 1.984042271226644e-06,
+  "esp_converged": true,
+  "elapsed_sec": 0.11616086959838867
+}

configs/torus_scalar/esn_h128_d1_torus_scalar/history.json

@@ -0,0 +1,146 @@
+{
+  "epoch": [
+    0,
+    99
+  ],
+  "loss": [
+    0.07059850280110638,
+    0.01731295743213294
+  ],
+  "topology": [
+    {
+      "deepest_sequential": {
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+        },
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configs/torus_scalar/esn_h128_d1_torus_scalar/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar/results.json

@@ -0,0 +1,531 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s1/cross_task.json

@@ -0,0 +1,181 @@
+{
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configs/torus_scalar/esn_h128_d1_torus_scalar_s1/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s1/gs_convergence.json

@@ -0,0 +1,16 @@
+{
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+}

configs/torus_scalar/esn_h128_d1_torus_scalar_s1/history.json

@@ -0,0 +1,146 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s1/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s1/results.json

@@ -0,0 +1,531 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s10/cross_task.json

@@ -0,0 +1,181 @@
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+}

configs/torus_scalar/esn_h128_d1_torus_scalar_s10/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s10/gs_convergence.json

@@ -0,0 +1,16 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s10/history.json

@@ -0,0 +1,146 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s10/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s10/results.json

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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/cross_task.json

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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/dyn_variants.json

@@ -0,0 +1,102 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/gs_convergence.json

@@ -0,0 +1,16 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/history.json

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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s2/results.json

@@ -0,0 +1,509 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s3/cross_task.json

@@ -0,0 +1,181 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s3/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s3/gs_convergence.json

@@ -0,0 +1,16 @@
+{
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+}

configs/torus_scalar/esn_h128_d1_torus_scalar_s3/history.json

@@ -0,0 +1,77 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s3/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s3/results.json

@@ -0,0 +1,531 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/cross_task.json

@@ -0,0 +1,181 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/gs_convergence.json

@@ -0,0 +1,16 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/history.json

@@ -0,0 +1,146 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/post_analysis.json

@@ -0,0 +1,63 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s4/results.json

@@ -0,0 +1,531 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s5/cross_task.json

@@ -0,0 +1,181 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s5/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s5/gs_convergence.json

@@ -0,0 +1,16 @@
+{
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+}

configs/torus_scalar/esn_h128_d1_torus_scalar_s5/history.json

@@ -0,0 +1,146 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s5/post_analysis.json

@@ -0,0 +1,63 @@
+{
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configs/torus_scalar/esn_h128_d1_torus_scalar_s6/cross_task.json

@@ -0,0 +1,181 @@
+{
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configs/torus_scalar/esn_h128_d1_torus_scalar_s6/dyn_variants.json

@@ -0,0 +1,124 @@
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configs/torus_scalar/esn_h128_d1_torus_scalar_s6/gs_convergence.json

@@ -0,0 +1,16 @@
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+}

gpu_co2_report.json

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+      "wall_hours": 209.73769542210633,
+      "a100_eq_hours": 175.4267596069002,
+      "kwh": 54.531800809747644,
+      "region": "PNW",
+      "kg_co2": 5.453180080974764,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3081",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 135,
+      "wall_hours": 204.76738029566076,
+      "a100_eq_hours": 171.26953705755008,
+      "kwh": 53.2395188768718,
+      "region": "PNW",
+      "kg_co2": 5.32395188768718,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3090",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 385,
+      "wall_hours": 271.30297298994327,
+      "a100_eq_hours": 226.92058920338332,
+      "kwh": 70.53877297738525,
+      "region": "PNW",
+      "kg_co2": 7.053877297738525,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3092",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 6,
+      "wall_hours": 2.017781385448244,
+      "a100_eq_hours": 1.6876930459826078,
+      "kwh": 0.5246231602165434,
+      "region": "PNW",
+      "kg_co2": 0.05246231602165435,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3093",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 121,
+      "wall_hours": 77.94469697025087,
+      "a100_eq_hours": 65.19374397870726,
+      "kwh": 20.265621212265227,
+      "region": "PNW",
+      "kg_co2": 2.026562121226523,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3094",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 13,
+      "wall_hours": 10.47731822470824,
+      "a100_eq_hours": 8.763336422820071,
+      "kwh": 2.7241027384241425,
+      "region": "PNW",
+      "kg_co2": 0.2724102738424143,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3095",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 675,
+      "wall_hours": 795.9921166975631,
+      "a100_eq_hours": 665.7759704275514,
+      "kwh": 206.95795034136643,
+      "region": "PNW",
+      "kg_co2": 20.695795034136644,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3096",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 82,
+      "wall_hours": 206.22773410273922,
+      "a100_eq_hours": 172.49099195977828,
+      "kwh": 53.6192108667122,
+      "region": "PNW",
+      "kg_co2": 5.36192108667122,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3097",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 558,
+      "wall_hours": 509.33640028761494,
+      "a100_eq_hours": 426.014189163641,
+      "kwh": 132.4274640747799,
+      "region": "PNW",
+      "kg_co2": 13.24274640747799,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3099",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 557,
+      "wall_hours": 535.3409922783243,
+      "a100_eq_hours": 447.7646966184343,
+      "kwh": 139.1886579923643,
+      "region": "PNW",
+      "kg_co2": 13.918865799236432,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3101",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 519,
+      "wall_hours": 517.8180292172565,
+      "a100_eq_hours": 433.1083105914591,
+      "kwh": 134.63268759648668,
+      "region": "PNW",
+      "kg_co2": 13.463268759648669,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3102",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 5,
+      "wall_hours": 0.8204648690091239,
+      "a100_eq_hours": 0.6862452314635287,
+      "kwh": 0.2133208659423722,
+      "region": "PNW",
+      "kg_co2": 0.021332086594237222,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3105",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 3,
+      "wall_hours": 0.6623947743574778,
+      "a100_eq_hours": 0.5540337830651519,
+      "kwh": 0.17222264133294424,
+      "region": "PNW",
+      "kg_co2": 0.017222264133294425,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3106",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 459,
+      "wall_hours": 260.764112670819,
+      "a100_eq_hours": 218.10577834159267,
+      "kwh": 67.79866929441293,
+      "region": "PNW",
+      "kg_co2": 6.779866929441294,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3107",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 100,
+      "wall_hours": 107.86876299262047,
+      "a100_eq_hours": 90.22253971331486,
+      "kwh": 28.045878378081323,
+      "region": "PNW",
+      "kg_co2": 2.8045878378081324,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3111",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 197,
+      "wall_hours": 143.89042330291537,
+      "a100_eq_hours": 120.35142584977177,
+      "kwh": 37.411510058758,
+      "region": "PNW",
+      "kg_co2": 3.7411510058757997,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3116",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 66,
+      "wall_hours": 40.43033824761709,
+      "a100_eq_hours": 33.816349580442804,
+      "kwh": 10.511887944380442,
+      "region": "PNW",
+      "kg_co2": 1.0511887944380442,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3117",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 243,
+      "wall_hours": 250.9502305077182,
+      "a100_eq_hours": 209.8973466451735,
+      "kwh": 65.24705993200674,
+      "region": "PNW",
+      "kg_co2": 6.524705993200674,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3122",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 116,
+      "wall_hours": 106.65513424224324,
+      "a100_eq_hours": 89.20744817902498,
+      "kwh": 27.73033490298324,
+      "region": "PNW",
+      "kg_co2": 2.7730334902983245,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3128",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 393,
+      "wall_hours": 517.6512755415837,
+      "a100_eq_hours": 432.96883610683227,
+      "kwh": 134.58933164081176,
+      "region": "PNW",
+      "kg_co2": 13.458933164081177,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "hyak_g3130",
+      "gpu": "Quadro RTX 6000",
+      "n_runs": 355,
+      "wall_hours": 560.6730959597561,
+      "a100_eq_hours": 468.95272795403184,
+      "kwh": 145.7750049495366,
+      "region": "PNW",
+      "kg_co2": 14.57750049495366,
+      "fp32_tflops": 16.31,
+      "tdp_w": 260
+    },
+    {
+      "host": "host_C_gtx1080",
+      "gpu": "GTX 1080",
+      "n_runs": 1051,
+      "wall_hours": 1131.0726384332445,
+      "a100_eq_hours": 514.4930411745066,
+      "kwh": 203.593074917984,
+      "region": "PNW",
+      "kg_co2": 20.359307491798404,
+      "fp32_tflops": 8.87,
+      "tdp_w": 180
+    },
+    {
+      "host": "host_B_rtx4000",
+      "gpu": "Quadro RTX 4000",
+      "n_runs": 992,
+      "wall_hours": 769.4042332354519,
+      "a100_eq_hours": 280.93118669930345,
+      "kwh": 123.10467731767231,
+      "region": "PNW",
+      "kg_co2": 12.310467731767233,
+      "fp32_tflops": 7.12,
+      "tdp_w": 160
+    }
+  ],
+  "totals": {
+    "wall_hours": 17338.03652264489,
+    "a100_eq_hours": 13066.544941452685,
+    "kwh": 4103.00356374613,
+    "kg_co2": 410.3003563746128,
+    "n_runs": 14553
+  },
+  "scan": {
+    "n_total_results": 14553,
+    "n_with_host_and_elapsed": 14553,
+    "n_missing_elapsed": 0
+  }
+}

gpu_co2_report.md

@@ -0,0 +1,58 @@
+# GPU-hours and CO2 emissions: Exp 3B
+
+Scanned **14553** results.json; **14553** had host + elapsed_sec; **0** missing elapsed_sec.
+
+
+## Per-host
+
+| Host | GPU | Runs | Wall hr | A100-eq hr | kWh | Region | kg CO2 |
+|---|---|---:|---:|---:|---:|---|---:|
+| P3Vikas | RTX A2000 | 406 | 1502.4 | 615.6 | 105.2 | PNW | 10.52 |
+| deptmed-apmdl01 | Titan RTX | 1843 | 2399.6 | 2007.0 | 671.9 | PNW | 67.19 |
+| g3003 | Quadro RTX 6000 | 252 | 209.8 | 175.5 | 54.6 | PNW | 5.46 |
+| g3013 | Quadro RTX 6000 | 77 | 73.3 | 61.3 | 19.1 | PNW | 1.91 |
+| g3025 | Quadro RTX 6000 | 487 | 492.5 | 412.0 | 128.1 | PNW | 12.81 |
+| g3026 | Quadro RTX 6000 | 1022 | 985.9 | 824.6 | 256.3 | PNW | 25.63 |
+| g3030 | Quadro RTX 6000 | 388 | 491.9 | 411.4 | 127.9 | PNW | 12.79 |
+| g3031 | Quadro RTX 6000 | 398 | 792.9 | 663.2 | 206.1 | PNW | 20.61 |
+| g3032 | Quadro RTX 6000 | 243 | 281.4 | 235.3 | 73.2 | PNW | 7.32 |
+| g3033 | Quadro RTX 6000 | 717 | 570.6 | 477.2 | 148.4 | PNW | 14.84 |
+| g3034 | Quadro RTX 6000 | 122 | 255.3 | 213.5 | 66.4 | PNW | 6.64 |
+| g3035 | Quadro RTX 6000 | 1054 | 1419.9 | 1187.6 | 369.2 | PNW | 36.92 |
+| g3036 | Quadro RTX 6000 | 44 | 93.1 | 77.8 | 24.2 | PNW | 2.42 |
+| g3050 | Quadro RTX 6000 | 84 | 176.5 | 147.6 | 45.9 | PNW | 4.59 |
+| g3056 | Quadro RTX 6000 | 162 | 167.9 | 140.4 | 43.7 | PNW | 4.37 |
+| g3057 | Quadro RTX 6000 | 34 | 24.3 | 20.3 | 6.3 | PNW | 0.63 |
+| g3064 | Quadro RTX 6000 | 96 | 169.1 | 141.4 | 44.0 | PNW | 4.40 |
+| g3073 | Quadro RTX 6000 | 93 | 209.7 | 175.4 | 54.5 | PNW | 5.45 |
+| g3081 | Quadro RTX 6000 | 135 | 204.8 | 171.3 | 53.2 | PNW | 5.32 |
+| g3090 | Quadro RTX 6000 | 385 | 271.3 | 226.9 | 70.5 | PNW | 7.05 |
+| g3092 | Quadro RTX 6000 | 6 | 2.0 | 1.7 | 0.5 | PNW | 0.05 |
+| g3093 | Quadro RTX 6000 | 121 | 77.9 | 65.2 | 20.3 | PNW | 2.03 |
+| g3094 | Quadro RTX 6000 | 13 | 10.5 | 8.8 | 2.7 | PNW | 0.27 |
+| g3095 | Quadro RTX 6000 | 675 | 796.0 | 665.8 | 207.0 | PNW | 20.70 |
+| g3096 | Quadro RTX 6000 | 82 | 206.2 | 172.5 | 53.6 | PNW | 5.36 |
+| g3097 | Quadro RTX 6000 | 558 | 509.3 | 426.0 | 132.4 | PNW | 13.24 |
+| g3099 | Quadro RTX 6000 | 557 | 535.3 | 447.8 | 139.2 | PNW | 13.92 |
+| g3101 | Quadro RTX 6000 | 519 | 517.8 | 433.1 | 134.6 | PNW | 13.46 |
+| g3102 | Quadro RTX 6000 | 5 | 0.8 | 0.7 | 0.2 | PNW | 0.02 |
+| g3105 | Quadro RTX 6000 | 3 | 0.7 | 0.6 | 0.2 | PNW | 0.02 |
+| g3106 | Quadro RTX 6000 | 459 | 260.8 | 218.1 | 67.8 | PNW | 6.78 |
+| g3107 | Quadro RTX 6000 | 100 | 107.9 | 90.2 | 28.0 | PNW | 2.80 |
+| g3111 | Quadro RTX 6000 | 197 | 143.9 | 120.4 | 37.4 | PNW | 3.74 |
+| g3116 | Quadro RTX 6000 | 66 | 40.4 | 33.8 | 10.5 | PNW | 1.05 |
+| g3117 | Quadro RTX 6000 | 243 | 251.0 | 209.9 | 65.2 | PNW | 6.52 |
+| g3122 | Quadro RTX 6000 | 116 | 106.7 | 89.2 | 27.7 | PNW | 2.77 |
+| g3128 | Quadro RTX 6000 | 393 | 517.7 | 433.0 | 134.6 | PNW | 13.46 |
+| g3130 | Quadro RTX 6000 | 355 | 560.7 | 469.0 | 145.8 | PNW | 14.58 |
+| vikas-dell-gtx1080 | GTX 1080 | 1051 | 1131.1 | 514.5 | 203.6 | PNW | 20.36 |
+| vikas-p53 | Quadro RTX 4000 | 992 | 769.4 | 280.9 | 123.1 | PNW | 12.31 |
+
+## Totals
+
+- Wall-clock GPU-hours: **17338.0**
+- A100-equivalent GPU-hours: **13066.5**
+- Energy: **4103.0 kWh**
+- Estimated CO2: **410.3 kg**
+
+*Method: per-host elapsed_sec summed from results.json; TDP-based energy (TDP_W * hours / 1000); CO2 from regional grid intensity (PNW = 0.100 kg/kWh, US avg = 0.390). TDP overestimates real draw under partial GPU load. A100-equivalent uses fp32 TFLOPS ratio, not real workload scaling.*