{
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  "benchmark_id": "glim-complete-continuous-time-cuda-full",
  "title": "Public GLIM Livox complete continuous-time refinement",
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    "data_license": "CC-BY-4.0 (AIST Zenodo record 6864654)",
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      "status": "success",
      "metrics": {
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      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker baseline --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/baseline.repeat-01 --candidate-query-backend cuda --baseline-query-backend cuda",
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      "status": "success",
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      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker sparse --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/sparse.repeat-01 --candidate-query-backend cuda --baseline-query-backend cuda",
        "config_sha256": "1e84922f03bde8b97f598237d8cd53e7a716db84a5f1f5651aa45043953d0186",
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    },
    {
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      "split_id": "repeat-02",
      "status": "success",
      "metrics": {
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        "adaptive_query_seconds": 0.0,
        "complete_result_matches": 1.0,
        "interpolated_pose_calculations": 21858940.0,
        "pipeline_seconds": 54.99013479996938,
        "solver_seconds": 42.1142806999851,
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      "runtime_seconds": 58.798765500076115,
      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker sparse --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/sparse.repeat-02 --candidate-query-backend cuda --baseline-query-backend cuda",
        "config_sha256": "1e84922f03bde8b97f598237d8cd53e7a716db84a5f1f5651aa45043953d0186",
        "input_sha256": "107e3deb3aaeab52628a93d8c6eed80aa1663c3492f6a4d2e280d553e47cd27c",
        "output_sha256": "51874482f9cf2403c3abfd9b68237d8ed1f6532ebbcad640bd3223fd871bbfec",
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        "notes": [
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        ]
      }
    },
    {
      "method_id": "baseline",
      "split_id": "repeat-02",
      "status": "success",
      "metrics": {
        "adaptive_query_calls": 0.0,
        "adaptive_query_seconds": 0.0,
        "complete_result_matches": 1.0,
        "interpolated_pose_calculations": 21092943.0,
        "pipeline_seconds": 78.12040919996798,
        "solver_seconds": 62.42824700009078,
        "subprocess_wall_seconds": 81.84258860000409,
        "uniform_normal_calculations": 883.0,
        "worker_cpu_seconds": 105.421875,
        "worker_import_seconds": 2.6322401999495924
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      "runtime_seconds": 81.84258860000409,
      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker baseline --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/baseline.repeat-02 --candidate-query-backend cuda --baseline-query-backend cuda",
        "config_sha256": "1e84922f03bde8b97f598237d8cd53e7a716db84a5f1f5651aa45043953d0186",
        "input_sha256": "107e3deb3aaeab52628a93d8c6eed80aa1663c3492f6a4d2e280d553e47cd27c",
        "output_sha256": "11e8ea6d129100f1314c0a5c5ee2b6bbf7c6be5f61669d97eb56e92454e42e5d",
        "execution_host": "Windows-11-10.0.26300-SP0",
        "notes": [
          "{\"cuda_cache_after\": {\"9ed58aaf0a8ff1513d8b1ff1f380f41d5d36ce51.cubin\": \"439af15bcb3e76072e7edf15df7826ea82900d542fef8a25a06add2bb01f6da4\", \"f4331870c33b1b7c90f548c88d07b801503c89e5.cubin\": \"84d63457c31f3966a080e1ce0b4a3b07748a9ea2aa61ff30168c7ea31a327b15\"}, \"cuda_cache_before\": {\"9ed58aaf0a8ff1513d8b1ff1f380f41d5d36ce51.cubin\": \"439af15bcb3e76072e7edf15df7826ea82900d542fef8a25a06add2bb01f6da4\", \"f4331870c33b1b7c90f548c88d07b801503c89e5.cubin\": \"84d63457c31f3966a080e1ce0b4a3b07748a9ea2aa61ff30168c7ea31a327b15\"}, \"execution_override\": \"cuda\", \"loaded_cuda_module\": \"baseline.cuda_continuous_time_adapter.py.txt\", \"loaded_evaluation_modules\": {\"calibrex.data.adaptive_voxel\": \"baseline.adaptive_voxel.py.txt\", \"calibrex.data.livox\": \"baseline.livox.py.txt\", \"calibrex.data.numba_lidar_deskew_adapter\": \"baseline.numba_lidar_deskew_adapter.py.txt\", \"calibrex.data.odometry_track\": \"baseline.odometry_track.py.txt\", 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    },
    {
      "method_id": "baseline",
      "split_id": "repeat-03",
      "status": "success",
      "metrics": {
        "adaptive_query_calls": 0.0,
        "adaptive_query_seconds": 0.0,
        "complete_result_matches": 1.0,
        "interpolated_pose_calculations": 20326546.0,
        "pipeline_seconds": 60.55491730000358,
        "solver_seconds": 44.87235690001398,
        "subprocess_wall_seconds": 64.3123635998927,
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      "runtime_seconds": 64.3123635998927,
      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker baseline --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/baseline.repeat-03 --candidate-query-backend cuda --baseline-query-backend cuda",
        "config_sha256": "1e84922f03bde8b97f598237d8cd53e7a716db84a5f1f5651aa45043953d0186",
        "input_sha256": "107e3deb3aaeab52628a93d8c6eed80aa1663c3492f6a4d2e280d553e47cd27c",
        "output_sha256": "11e8ea6d129100f1314c0a5c5ee2b6bbf7c6be5f61669d97eb56e92454e42e5d",
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        "solver_seconds": 30.861820499994792,
        "subprocess_wall_seconds": 43.09404689993244,
        "uniform_normal_calculations": 883.0,
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        "worker_import_seconds": 1.8185097000095993
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      "runtime_seconds": 43.09404689993244,
      "provenance": {
        "command": "<PYTHON> <WORKSPACE>/tools/benchmark_adaptive_plane_search.py --worker sparse --baseline-scope cuda-full --config examples/public_datasets/glim_versatile/online_continuous_time_compiled_dense_capture_config.yaml --output-dir <WORKSPACE>/outputs/public-glim-linearization-reuse-dense-v3/sparse.repeat-03 --candidate-query-backend cuda --baseline-query-backend cuda",
        "config_sha256": "1e84922f03bde8b97f598237d8cd53e7a716db84a5f1f5651aa45043953d0186",
        "input_sha256": "107e3deb3aaeab52628a93d8c6eed80aa1663c3492f6a4d2e280d553e47cd27c",
        "output_sha256": "51874482f9cf2403c3abfd9b68237d8ed1f6532ebbcad640bd3223fd871bbfec",
        "execution_host": "Windows-11-10.0.26300-SP0",
        "notes": [
          "{\"cuda_cache_after\": {\"9ed58aaf0a8ff1513d8b1ff1f380f41d5d36ce51.cubin\": \"439af15bcb3e76072e7edf15df7826ea82900d542fef8a25a06add2bb01f6da4\", \"f4331870c33b1b7c90f548c88d07b801503c89e5.cubin\": \"84d63457c31f3966a080e1ce0b4a3b07748a9ea2aa61ff30168c7ea31a327b15\"}, \"cuda_cache_before\": {\"9ed58aaf0a8ff1513d8b1ff1f380f41d5d36ce51.cubin\": \"439af15bcb3e76072e7edf15df7826ea82900d542fef8a25a06add2bb01f6da4\", \"f4331870c33b1b7c90f548c88d07b801503c89e5.cubin\": \"84d63457c31f3966a080e1ce0b4a3b07748a9ea2aa61ff30168c7ea31a327b15\"}, \"execution_override\": \"cuda\", \"loaded_cuda_module\": \"<WORKSPACE>/src/calibrex/solvers/cuda_continuous_time_adapter.py\", \"loaded_evaluation_modules\": {\"calibrex.data.adaptive_voxel\": \"<WORKSPACE>/src/calibrex/data/adaptive_voxel.py\", \"calibrex.data.livox\": \"<WORKSPACE>/src/calibrex/data/livox.py\", \"calibrex.data.numba_lidar_deskew_adapter\": \"<WORKSPACE>/src/calibrex/data/numba_lidar_deskew_adapter.py\", 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    "Single public sensor duplicated as an identity control; no absolute two-sensor accuracy claim.",
    "Fresh interpreters; no warmup or outlier exclusion; OS caches persist.",
    "Scope cuda-full substitutes 14 modules. All candidates, refinements, train/holdout budgets and writers are shared. Module filenames and source/input bytes are verified.",
    "Wall includes startup, imports, replay, optimization, diagnostics, input hashes, YAML output and worker measurement save; parent audits and CLI presentation are excluded.",
    "Phase timers wrap existing calls without extra work; their wrapper cost is included.",
    "Full saved/API artifacts and git revision are retained. Comparison normalizes path separators and excludes only the exact optional kernel identity note.",
    "Numba cache policy is recorded per trial, with compiled file hashes before/after. The first candidate uses an empty cache; all import/compile/load work is inside wall.",
    "Compiled scope gives each method its own persistent cache, initially empty.",
    "Candidate execution override: cuda; baseline execution override: cuda; all physical options and splits are shared. CUDA runs also own a fresh persistent kernel cache with trial-level before/after digests; compilation stays inside wall. CUDA dispatch retains CPU execution below the 8192-row capture budget.",
    "Worker CPU sums all worker threads from entry through the saved-result hash; interpreter startup and measurement JSON serialization are outside that diagnostic.",
    "Raw checksums and conversion lineage are verified before measurement.",
    "Split IDs hash ordered sampled target indices, capture timestamps and XYZ; the distinct temporal partitions and unchanged sampling caps are verified each run.",
    "Descriptive timings are local observations, not portable confidence estimates."
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