Operation run times estimation results - EIP-7904 ================================================= Table of contents ================= * [MULMOD](#mulmod) * [ADDMOD](#addmod) * [SMOD](#smod) * [MOD](#mod) * [SMOD](#smod) * [DIV](#div) * [SDIV](#sdiv) * [POINT_EVALUATION](#point_evaluation) * [BLS12_G1ADD](#bls12_g1add) * [BLS12_G2ADD](#bls12_g2add) * [ECADD](#ecadd) * [ECRECOVER](#ecrecover) * [KECCAK256](#keccak256) * [BLAKE2F](#blake2f) * [ECPAIRING](#ecpairing) # Introduction This is an automated report generated from the opcode run times estimation script `./src/estimate_7904_repricings.py`. The script uses data generated by running the [EEST benchmark suite](https://github.com/ethereum/execution-spec-tests/tree/main/tests/benchmark) with the [Nethermind benchmarking tooling](https://github.com/NethermindEth/gas-benchmarks). The data includes all the tests for operations repriced in EIP-7904 run between 2026-01-15 and 2026-01-29. For each operation and client, an NNLS linear regression model is fitted to estimate the operation run time as a function of the operation count and other operation-specific parameters (e.g., number of rounds for BLAKE2F, message size for KECCAK256, number of pairs for ECPAIRING). The results are presented below. # How to Interpret the Results ## Model Used **Non-Negative Least Squares (NNLS) Linear Regression** is used to estimate operation runtimes. This model ensures all coefficients are non-negative, which is physically meaningful since execution time cannot be negative. The model estimates runtime as a linear combination of: - **Constant term (intercept)**: Base overhead for executing the test, which includes setup and teardown time - **Operation count (slope)**: Number of times the operation is executed in the test. This paramater is the one that allows us to estimate the per-operation runtime. - **Operation-specific parameters**: Variables like number of rounds, message size, or number of pairs For simple operations, the model estimates: `runtime = intercept + slope × operation_count` For variable operations, the model estimates: `runtime = intercept + slope × operation_count + param1_coef × operation_count × param1 + param2_coef × operation_count × param2 + ...` ## Model Quality Metrics Each model reports two key metrics to assess the quality of the fit: **R² (R-squared / Coefficient of Determination)** - Ranges from 0 to 1 (or can be negative for very poor fits) - Measures how well the model explains the variance in the data - **Interpretation**: - R² > 0.9: Excellent fit - the model explains >90% of the variance - R² > 0.7: Good fit - the model captures most of the relationship - R² > 0.5: Acceptable fit - the model has predictive power but notable variance remains - R² < 0.5: Poor fit - the model may not be reliable **p-value** - Tests the statistical significance of each coefficient, based on a bootstrap sample estimation - **Interpretation**: - p < 0.05: Statistically significant - the parameter has a real effect on runtime - p ≥ 0.05: Not significant - the parameter's effect cannot be distinguished from random noise We also plot some diagnostic graphs for each operation and client combination to visually assess the model fit. # MULMOD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.989 Model: NNLS Adj. R-squared: 0.989 No. Observations: 320 RMSE: 36.73 Df Residuals: 318 MAE: 24.25 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.4231 2.9149 0.000 25.5657 37.3067 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MULMOD_reth_regression MULMOD_reth_diagnostics MULMOD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 430 RMSE: 66.34 Df Residuals: 428 MAE: 42.28 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 28.6369 4.5129 0.000 20.2732 37.8599 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MULMOD_nethermind_regression MULMOD_nethermind_diagnostics MULMOD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 430 RMSE: 59.81 Df Residuals: 428 MAE: 38.26 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.0378 3.9965 0.000 23.1553 38.7857 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MULMOD_geth_regression MULMOD_geth_diagnostics MULMOD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.980 Model: NNLS Adj. R-squared: 0.980 No. Observations: 430 RMSE: 217.33 Df Residuals: 428 MAE: 156.62 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 40.8901 15.5092 0.005 13.5016 72.9058 opcount 0.0002 0.0000 0.000 0.0002 0.0002 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MULMOD_besu_regression MULMOD_besu_diagnostics MULMOD_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 1.000 Model: NNLS Adj. R-squared: 1.000 No. Observations: 30 RMSE: 11.20 Df Residuals: 28 MAE: 7.82 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 27.7081 3.5287 0.000 20.3352 34.1970 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MULMOD_erigon_regression MULMOD_erigon_diagnostics MULMOD_erigon_bootstrap # ADDMOD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 320 RMSE: 26.74 Df Residuals: 318 MAE: 17.69 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 32.2943 2.0437 0.000 28.3926 36.4658 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ADDMOD_reth_regression ADDMOD_reth_diagnostics ADDMOD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 430 RMSE: 40.36 Df Residuals: 428 MAE: 22.15 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 26.2244 3.2736 0.000 19.5882 32.3655 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ADDMOD_nethermind_regression ADDMOD_nethermind_diagnostics ADDMOD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.989 Model: NNLS Adj. R-squared: 0.989 No. Observations: 430 RMSE: 57.35 Df Residuals: 428 MAE: 36.68 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 32.5766 3.9405 0.000 25.0283 40.1535 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ADDMOD_geth_regression ADDMOD_geth_diagnostics ADDMOD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.980 Model: NNLS Adj. R-squared: 0.979 No. Observations: 430 RMSE: 181.34 Df Residuals: 428 MAE: 130.22 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 35.6849 13.0733 0.006 8.6600 60.3857 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ADDMOD_besu_regression ADDMOD_besu_diagnostics ADDMOD_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 1.000 Model: NNLS Adj. R-squared: 0.999 No. Observations: 30 RMSE: 13.39 Df Residuals: 28 MAE: 11.06 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 29.4920 3.4449 0.000 22.3475 35.8904 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ADDMOD_erigon_regression ADDMOD_erigon_diagnostics ADDMOD_erigon_bootstrap # SMOD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 320 RMSE: 13.38 Df Residuals: 318 MAE: 9.28 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.8194 1.2095 0.000 29.4033 34.1328 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_reth_regression SMOD_reth_diagnostics SMOD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.985 Model: NNLS Adj. R-squared: 0.985 No. Observations: 430 RMSE: 16.88 Df Residuals: 428 MAE: 11.30 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.3208 1.1754 0.000 28.1543 32.6954 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_nethermind_regression SMOD_nethermind_diagnostics SMOD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.958 Model: NNLS Adj. R-squared: 0.958 No. Observations: 430 RMSE: 54.31 Df Residuals: 428 MAE: 26.24 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.3730 3.1894 0.000 27.0449 39.4705 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_geth_regression SMOD_geth_diagnostics SMOD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.984 Model: NNLS Adj. R-squared: 0.984 No. Observations: 430 RMSE: 44.74 Df Residuals: 428 MAE: 31.00 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.3917 3.3060 0.000 23.9998 36.2517 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_besu_regression SMOD_besu_diagnostics SMOD_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.993 Model: NNLS Adj. R-squared: 0.992 No. Observations: 30 RMSE: 24.50 Df Residuals: 28 MAE: 11.61 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 22.2938 6.5115 0.011 5.7869 31.2411 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_erigon_regression SMOD_erigon_diagnostics SMOD_erigon_bootstrap # MOD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 320 RMSE: 31.06 Df Residuals: 318 MAE: 20.79 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 34.6663 2.5633 0.000 29.9558 39.6959 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MOD_reth_regression MOD_reth_diagnostics MOD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 430 RMSE: 40.16 Df Residuals: 428 MAE: 24.83 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.1414 2.7993 0.000 24.6640 35.4751 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MOD_nethermind_regression MOD_nethermind_diagnostics MOD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.985 Model: NNLS Adj. R-squared: 0.985 No. Observations: 430 RMSE: 56.92 Df Residuals: 428 MAE: 33.00 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.6655 3.9946 0.000 26.0844 42.2995 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MOD_geth_regression MOD_geth_diagnostics MOD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.977 Model: NNLS Adj. R-squared: 0.977 No. Observations: 430 RMSE: 257.81 Df Residuals: 428 MAE: 180.30 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 42.7935 18.3770 0.013 4.6972 78.3645 opcount 0.0002 0.0000 0.000 0.0002 0.0002 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MOD_besu_regression MOD_besu_diagnostics MOD_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.996 Model: NNLS Adj. R-squared: 0.996 No. Observations: 30 RMSE: 30.18 Df Residuals: 28 MAE: 21.90 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 39.0732 8.3577 0.000 22.2512 55.7676 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` MOD_erigon_regression MOD_erigon_diagnostics MOD_erigon_bootstrap # SMOD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 320 RMSE: 13.38 Df Residuals: 318 MAE: 9.28 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.8194 1.2095 0.000 29.4033 34.1328 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_reth_regression SMOD_reth_diagnostics SMOD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.985 Model: NNLS Adj. R-squared: 0.985 No. Observations: 430 RMSE: 16.88 Df Residuals: 428 MAE: 11.30 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.3208 1.1754 0.000 28.1543 32.6954 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_nethermind_regression SMOD_nethermind_diagnostics SMOD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.958 Model: NNLS Adj. R-squared: 0.958 No. Observations: 430 RMSE: 54.31 Df Residuals: 428 MAE: 26.24 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.3730 3.1894 0.000 27.0449 39.4705 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_geth_regression SMOD_geth_diagnostics SMOD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.984 Model: NNLS Adj. R-squared: 0.984 No. Observations: 430 RMSE: 44.74 Df Residuals: 428 MAE: 31.00 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.3917 3.3060 0.000 23.9998 36.2517 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_besu_regression SMOD_besu_diagnostics SMOD_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.993 Model: NNLS Adj. R-squared: 0.992 No. Observations: 30 RMSE: 24.50 Df Residuals: 28 MAE: 11.61 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 22.2938 6.5115 0.011 5.7869 31.2411 opcount 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SMOD_erigon_regression SMOD_erigon_diagnostics SMOD_erigon_bootstrap # DIV ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 320 RMSE: 43.95 Df Residuals: 318 MAE: 27.80 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.6486 3.4720 0.000 26.8360 40.5650 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` DIV_reth_regression DIV_reth_diagnostics DIV_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 430 RMSE: 64.73 Df Residuals: 428 MAE: 39.87 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.8596 4.4705 0.000 22.2831 39.8098 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` DIV_nethermind_regression DIV_nethermind_diagnostics DIV_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 430 RMSE: 70.49 Df Residuals: 428 MAE: 44.11 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.4015 5.1975 0.000 21.3700 41.5393 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` DIV_geth_regression DIV_geth_diagnostics DIV_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.969 Model: NNLS Adj. R-squared: 0.969 No. Observations: 430 RMSE: 377.34 Df Residuals: 428 MAE: 276.74 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 41.3458 25.1943 0.060 0.0000 93.7752 opcount 0.0002 0.0000 0.000 0.0002 0.0002 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` DIV_besu_regression DIV_besu_diagnostics DIV_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 1.000 Model: NNLS Adj. R-squared: 1.000 No. Observations: 30 RMSE: 9.48 Df Residuals: 28 MAE: 7.18 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.7244 3.4691 0.000 26.9306 40.5072 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` DIV_erigon_regression DIV_erigon_diagnostics DIV_erigon_bootstrap # SDIV ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 320 RMSE: 57.16 Df Residuals: 318 MAE: 37.61 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.4257 4.5085 0.000 23.0489 41.0961 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SDIV_reth_regression SDIV_reth_diagnostics SDIV_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.977 Model: NNLS Adj. R-squared: 0.977 No. Observations: 430 RMSE: 108.13 Df Residuals: 428 MAE: 70.48 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 28.3499 7.6774 0.000 11.8263 42.8811 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SDIV_nethermind_regression SDIV_nethermind_diagnostics SDIV_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.988 Model: NNLS Adj. R-squared: 0.988 No. Observations: 430 RMSE: 68.54 Df Residuals: 428 MAE: 42.44 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 28.0247 5.5172 0.000 17.2510 38.4472 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SDIV_geth_regression SDIV_geth_diagnostics SDIV_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.971 Model: NNLS Adj. R-squared: 0.971 No. Observations: 430 RMSE: 483.08 Df Residuals: 428 MAE: 335.85 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 26.4209 27.4641 0.233 0.0000 90.8952 opcount 0.0003 0.0000 0.000 0.0003 0.0003 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SDIV_besu_regression SDIV_besu_diagnostics SDIV_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.999 Model: NNLS Adj. R-squared: 0.999 No. Observations: 30 RMSE: 17.05 Df Residuals: 28 MAE: 12.01 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 32.0771 5.4484 0.000 23.3307 44.2564 opcount 0.0001 0.0000 0.000 0.0001 0.0001 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` SDIV_erigon_regression SDIV_erigon_diagnostics SDIV_erigon_bootstrap # POINT_EVALUATION ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.993 Model: NNLS Adj. R-squared: 0.993 No. Observations: 320 RMSE: 364.33 Df Residuals: 318 MAE: 250.88 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.9425 27.3986 0.126 0.0000 92.9463 opcount 1.4861 0.0074 0.000 1.4701 1.4985 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` POINT_EVALUATION_reth_regression POINT_EVALUATION_reth_diagnostics POINT_EVALUATION_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.992 Model: NNLS Adj. R-squared: 0.992 No. Observations: 430 RMSE: 391.02 Df Residuals: 428 MAE: 255.69 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 13.1322 19.5270 0.327 0.0000 65.9079 opcount 1.4894 0.0059 0.000 1.4750 1.4982 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` POINT_EVALUATION_nethermind_regression POINT_EVALUATION_nethermind_diagnostics POINT_EVALUATION_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.992 Model: NNLS Adj. R-squared: 0.992 No. Observations: 430 RMSE: 388.20 Df Residuals: 428 MAE: 243.56 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 40.4320 26.1932 0.060 0.0000 99.7871 opcount 1.4835 0.0071 0.000 1.4683 1.4962 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` POINT_EVALUATION_geth_regression POINT_EVALUATION_geth_diagnostics POINT_EVALUATION_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 430 RMSE: 411.40 Df Residuals: 428 MAE: 305.87 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 28.5453 24.1757 0.177 0.0000 79.9144 opcount 1.4843 0.0066 0.000 1.4710 1.4964 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` POINT_EVALUATION_besu_regression POINT_EVALUATION_besu_diagnostics POINT_EVALUATION_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # BLS12_G1ADD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 320 RMSE: 73.12 Df Residuals: 318 MAE: 50.37 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 32.8474 6.6176 0.000 19.9333 45.6873 opcount 0.0051 0.0000 0.000 0.0051 0.0052 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G1ADD_reth_regression BLS12_G1ADD_reth_diagnostics BLS12_G1ADD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 430 RMSE: 100.40 Df Residuals: 428 MAE: 63.21 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 13.5899 8.7007 0.067 0.0000 31.3154 opcount 0.0059 0.0000 0.000 0.0058 0.0060 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G1ADD_nethermind_regression BLS12_G1ADD_nethermind_diagnostics BLS12_G1ADD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.992 Model: NNLS Adj. R-squared: 0.992 No. Observations: 430 RMSE: 66.89 Df Residuals: 428 MAE: 45.46 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.3366 4.9335 0.000 23.4914 42.7542 opcount 0.0051 0.0000 0.000 0.0051 0.0052 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G1ADD_geth_regression BLS12_G1ADD_geth_diagnostics BLS12_G1ADD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.943 Model: NNLS Adj. R-squared: 0.943 No. Observations: 430 RMSE: 376.50 Df Residuals: 428 MAE: 282.35 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 4.7707 17.4892 0.398 0.0000 58.1477 opcount 0.0107 0.0001 0.000 0.0104 0.0109 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G1ADD_besu_regression BLS12_G1ADD_besu_diagnostics BLS12_G1ADD_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # BLS12_G2ADD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 320 RMSE: 60.02 Df Residuals: 318 MAE: 41.12 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 34.2802 4.9762 0.000 24.8162 44.7806 opcount 0.0074 0.0000 0.000 0.0073 0.0075 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G2ADD_reth_regression BLS12_G2ADD_reth_diagnostics BLS12_G2ADD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 430 RMSE: 79.95 Df Residuals: 428 MAE: 52.21 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 0.0000 2.1068 1.000 0.0000 7.6520 opcount 0.0092 0.0000 0.000 0.0091 0.0093 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G2ADD_nethermind_regression BLS12_G2ADD_nethermind_diagnostics BLS12_G2ADD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.992 Model: NNLS Adj. R-squared: 0.992 No. Observations: 430 RMSE: 52.54 Df Residuals: 428 MAE: 35.01 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 29.6103 3.8388 0.000 22.2773 37.6501 opcount 0.0070 0.0000 0.000 0.0069 0.0071 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G2ADD_geth_regression BLS12_G2ADD_geth_diagnostics BLS12_G2ADD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.958 Model: NNLS Adj. R-squared: 0.958 No. Observations: 430 RMSE: 229.33 Df Residuals: 428 MAE: 173.41 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 1.9380 11.0376 0.482 0.0000 37.3520 opcount 0.0127 0.0001 0.000 0.0125 0.0129 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLS12_G2ADD_besu_regression BLS12_G2ADD_besu_diagnostics BLS12_G2ADD_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # ECADD ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 320 RMSE: 104.34 Df Residuals: 318 MAE: 76.32 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 29.7112 8.6267 0.001 13.3779 46.9809 opcount 0.0038 0.0000 0.000 0.0037 0.0038 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECADD_reth_regression ECADD_reth_diagnostics ECADD_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.985 No. Observations: 430 RMSE: 84.76 Df Residuals: 428 MAE: 55.88 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 14.8697 5.7797 0.005 3.2806 26.1806 opcount 0.0024 0.0000 0.000 0.0024 0.0025 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECADD_nethermind_regression ECADD_nethermind_diagnostics ECADD_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.989 Model: NNLS Adj. R-squared: 0.989 No. Observations: 430 RMSE: 86.48 Df Residuals: 428 MAE: 59.62 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 21.6074 6.2813 0.000 10.0396 34.1265 opcount 0.0029 0.0000 0.000 0.0029 0.0029 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECADD_geth_regression ECADD_geth_diagnostics ECADD_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.982 Model: NNLS Adj. R-squared: 0.982 No. Observations: 430 RMSE: 204.25 Df Residuals: 428 MAE: 144.53 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 25.9624 13.9894 0.034 0.0000 53.1046 opcount 0.0052 0.0000 0.000 0.0052 0.0053 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECADD_besu_regression ECADD_besu_diagnostics ECADD_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # ECRECOVER ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 320 RMSE: 106.43 Df Residuals: 318 MAE: 69.10 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 40.5342 9.2076 0.000 23.0866 59.6352 opcount 0.0469 0.0003 0.000 0.0463 0.0475 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECRECOVER_reth_regression ECRECOVER_reth_diagnostics ECRECOVER_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.991 Model: NNLS Adj. R-squared: 0.991 No. Observations: 430 RMSE: 98.35 Df Residuals: 428 MAE: 63.25 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.1650 6.9901 0.000 16.7049 44.6032 opcount 0.0446 0.0002 0.000 0.0441 0.0450 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECRECOVER_nethermind_regression ECRECOVER_nethermind_diagnostics ECRECOVER_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.993 Model: NNLS Adj. R-squared: 0.993 No. Observations: 430 RMSE: 94.49 Df Residuals: 428 MAE: 63.08 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 36.6028 6.4618 0.000 24.6673 49.6262 opcount 0.0480 0.0002 0.000 0.0477 0.0484 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECRECOVER_geth_regression ECRECOVER_geth_diagnostics ECRECOVER_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.985 Model: NNLS Adj. R-squared: 0.985 No. Observations: 430 RMSE: 135.74 Df Residuals: 428 MAE: 91.87 Df Model: 1 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 29.7974 9.8595 0.001 11.3764 49.1138 opcount 0.0484 0.0003 0.000 0.0478 0.0490 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECRECOVER_besu_regression ECRECOVER_besu_diagnostics ECRECOVER_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # KECCAK256 ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.993 Model: NNLS Adj. R-squared: 0.993 No. Observations: 5120 RMSE: 59.67 Df Residuals: 5117 MAE: 39.23 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 30.8731 1.1967 0.000 28.6487 33.2196 opcount 0.0000 0.0000 0.000 0.0000 0.0000 msg_size 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` KECCAK256_reth_regression KECCAK256_reth_diagnostics KECCAK256_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.985 Model: NNLS Adj. R-squared: 0.985 No. Observations: 6880 RMSE: 98.77 Df Residuals: 6877 MAE: 77.41 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 27.1124 1.7795 0.000 23.5517 30.5938 opcount 0.0000 0.0000 0.000 0.0000 0.0000 msg_size 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` KECCAK256_nethermind_regression KECCAK256_nethermind_diagnostics KECCAK256_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.925 Model: NNLS Adj. R-squared: 0.925 No. Observations: 6880 RMSE: 239.33 Df Residuals: 6877 MAE: 168.50 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 7.8135 4.3612 0.044 0.0000 16.3268 opcount 0.0005 0.0000 0.000 0.0005 0.0005 msg_size 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` KECCAK256_geth_regression KECCAK256_geth_diagnostics KECCAK256_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.955 Model: NNLS Adj. R-squared: 0.955 No. Observations: 6880 RMSE: 263.59 Df Residuals: 6877 MAE: 198.95 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 5.8207 4.2703 0.096 0.0000 14.6842 opcount 0.0007 0.0000 0.000 0.0007 0.0007 msg_size 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` KECCAK256_besu_regression KECCAK256_besu_diagnostics KECCAK256_besu_bootstrap ## erigon ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.947 Model: NNLS Adj. R-squared: 0.947 No. Observations: 480 RMSE: 189.92 Df Residuals: 477 MAE: 154.33 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 11.2532 10.4939 0.175 0.0000 35.1932 opcount 0.0004 0.0000 0.000 0.0004 0.0005 msg_size 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` KECCAK256_erigon_regression KECCAK256_erigon_diagnostics KECCAK256_erigon_bootstrap # BLAKE2F ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.952 Model: NNLS Adj. R-squared: 0.952 No. Observations: 1280 RMSE: 115.39 Df Residuals: 1277 MAE: 92.63 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 14.8301 4.6685 0.001 5.5662 24.3891 opcount 0.0006 0.0000 0.000 0.0006 0.0006 num_rounds 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLAKE2F_reth_regression BLAKE2F_reth_diagnostics BLAKE2F_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.977 Model: NNLS Adj. R-squared: 0.977 No. Observations: 1720 RMSE: 77.08 Df Residuals: 1717 MAE: 54.97 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 0.0000 0.0000 1.000 0.0000 0.0000 opcount 0.0007 0.0000 0.000 0.0007 0.0007 num_rounds 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLAKE2F_nethermind_regression BLAKE2F_nethermind_diagnostics BLAKE2F_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.988 Model: NNLS Adj. R-squared: 0.988 No. Observations: 1720 RMSE: 56.02 Df Residuals: 1717 MAE: 41.08 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 17.6718 1.9910 0.000 13.6075 21.6085 opcount 0.0007 0.0000 0.000 0.0007 0.0007 num_rounds 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLAKE2F_geth_regression BLAKE2F_geth_diagnostics BLAKE2F_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.975 Model: NNLS Adj. R-squared: 0.975 No. Observations: 1720 RMSE: 277.67 Df Residuals: 1717 MAE: 196.53 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 62.5567 10.3996 0.000 42.2762 82.6965 opcount 0.0028 0.0000 0.000 0.0028 0.0028 num_rounds 0.0000 0.0000 0.000 0.0000 0.0000 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` BLAKE2F_besu_regression BLAKE2F_besu_diagnostics BLAKE2F_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values # ECPAIRING ## reth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.988 Model: NNLS Adj. R-squared: 0.988 No. Observations: 1432 RMSE: 93.51 Df Residuals: 1429 MAE: 50.85 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 34.1944 3.4072 0.000 27.9524 41.3956 opcount 0.5785 0.0055 0.000 0.5678 0.5895 num_pairs 0.4691 0.0016 0.000 0.4658 0.4721 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECPAIRING_reth_regression ECPAIRING_reth_diagnostics ECPAIRING_reth_bootstrap ## nethermind ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.990 Model: NNLS Adj. R-squared: 0.990 No. Observations: 1982 RMSE: 96.86 Df Residuals: 1979 MAE: 56.12 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 33.2452 3.2925 0.000 26.9497 39.7779 opcount 0.3005 0.0043 0.000 0.2925 0.3093 num_pairs 0.5684 0.0016 0.000 0.5651 0.5714 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECPAIRING_nethermind_regression ECPAIRING_nethermind_diagnostics ECPAIRING_nethermind_bootstrap ## geth ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.989 Model: NNLS Adj. R-squared: 0.989 No. Observations: 1982 RMSE: 41.44 Df Residuals: 1979 MAE: 21.09 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 35.2135 1.3674 0.000 32.7371 38.0138 opcount 0.3013 0.0022 0.000 0.2973 0.3058 num_pairs 0.2147 0.0006 0.000 0.2135 0.2159 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECPAIRING_geth_regression ECPAIRING_geth_diagnostics ECPAIRING_geth_bootstrap ## besu ```python ============================================================================== NNLS Regression Results ============================================================================== Dep. Variable: run_duration_ms R-squared: 0.986 Model: NNLS Adj. R-squared: 0.986 No. Observations: 1982 RMSE: 46.00 Df Residuals: 1979 MAE: 27.86 Df Model: 2 ============================================================================== coef std err P-value [0.025 0.975] ------------------------------------------------------------------------------ const 31.3547 1.5207 0.000 28.2816 34.4007 opcount 0.3031 0.0024 0.000 0.2986 0.3077 num_pairs 0.2107 0.0007 0.000 0.2093 0.2121 ============================================================================== Notes: Non-negative least squares with bootstrap inference (1000 iterations) ============================================================================== ``` ECPAIRING_besu_regression ECPAIRING_besu_diagnostics ECPAIRING_besu_bootstrap ## erigon NNLS model did not run... Error: No valid data remaining after dropping NaN values