Benchmarks

Detailed Benchmark Results

Honest, reproducible benchmarks across 48 real-world Python workloads. Every result is checked against CPython ground truth - if it is wrong, we say so.

48/48
Correctness Pass
47/48
Speed Wins
55.5×
Average Speedup
545.3×
Max Speedup
2026-05-30 06:58:09 542.63s total runtime AMD EPYC-Milan (znver3) Python 3.12

Benchmarks by Category

Benchmark Category CPython Pyvorin Numba Speedup Status
hash_sum_1m Reduction 2,786.2 ms 5.11 ms 4.08 ms 545.3× Pass
array_sum_1m Reduction 593.8 ms 42.62 ms 55.58 ms 13.9× Pass
float_sqrt_loop_10k Reduction 137.8 ms 5.14 ms 5.17 ms 26.8× Pass
fibonacci_recursive_30 Recursion 297.1 ms 13.98 ms 0.00 ms 21.2× Pass
hash_nested_500 Reduction 2,100.8 ms 12.44 ms 5.48 ms 168.9× Pass
prime_count_5k Reduction 182.4 ms 11.84 ms 11.48 ms 15.4× Pass
array_min_max_100k Reduction 382.7 ms 22.58 ms 27.13 ms 17× Pass
matrix_multiply_50 Matrix/Array 116.6 ms 5.47 ms 2.74 ms 21.3× Pass
triple_nested_100 Matrix/Array 2,070.0 ms 9.81 ms 2.98 ms 211.1× Pass
nested_loops_1k Matrix/Array 264.5 ms 6.69 ms 7.08 ms 39.5× Pass
dot_product_10k Matrix/Array 124.4 ms 7.80 ms 9.23 ms 16× Pass
conv1d_10k Matrix/Array 441.4 ms 17.34 ms 20.71 ms 25.5× Pass
tokenize_text String/ETL 387.1 ms 22.51 ms 30.70 ms 17.2× Pass
pattern_match String/ETL 72.5 ms 7.54 ms 28.07 ms 9.6× Pass
csv_parse_10k String/ETL 266.5 ms 19.15 ms 24.41 ms 13.9× Pass
string_reverse String/ETL 342.6 ms 72.81 ms 87.38 ms 4.7× Pass
fibonacci_recursive_25 Recursion 263.1 ms 13.29 ms 12.05 ms 19.8× Pass
fibonacci_iterative_92 Recursion 0.0 ms 0.01 ms 0.00 ms 0.4× Pass
gcd_1k_pairs Recursion 142.3 ms 7.84 ms 9.20 ms 18.1× Pass
bfs_graph Graph 217.4 ms 5.27 ms 9.29 ms 41.2× Pass
merge_sort_200 Sorting 47.0 ms 11.89 ms 16.05 ms 4× Pass
bubble_sort_200 Sorting 1,794.7 ms 7.65 ms 22.02 ms 234.6× Pass
quick_sort_200 Sorting 119.6 ms 14.14 ms 0.00 ms 8.5× Pass
df_column_sum_1m Data Engineering 1,474.7 ms 85.45 ms 104.53 ms 17.3× Pass
df_groupby_mean_500k Data Engineering 248.1 ms 16.63 ms 21.66 ms 14.9× Pass
df_rolling_mean_500k Data Engineering 718.2 ms 17.98 ms 23.74 ms 39.9× Pass
df_rolling_mean_2m Data Engineering 1,804.9 ms 46.46 ms 61.24 ms 38.8× Pass
rolling_sum_100k Matrix/Array 585.7 ms 18.02 ms 43.84 ms 32.5× Pass
df_filter_rows_500k Data Engineering 389.1 ms 21.26 ms 25.01 ms 18.3× Pass
csv_parse_sum_50k String/ETL 154.3 ms 68.85 ms 103.16 ms 2.2× Pass
log_parser_20k String/ETL 49.8 ms 9.80 ms 18.89 ms 5.1× Pass
url_query_parser_10k String/ETL 160.5 ms 10.82 ms 4.06 ms 14.8× Pass
json_key_extract_5k String/ETL 19.3 ms 8.28 ms 12.85 ms 2.3× Pass
pattern_find_10k String/ETL 233.2 ms 24.28 ms 49.32 ms 9.6× Pass
fixed_width_parser_50k String/ETL 80.2 ms 34.52 ms 90.86 ms 2.3× Pass
sorted_merge_join_200k Data Engineering 291.5 ms 2.97 ms 6.03 ms 98.1× Pass
sliding_window_max_500k Data Engineering 1,307.2 ms 14.82 ms 9.59 ms 88.2× Pass
sliding_window_max_1m Data Engineering 3,136.1 ms 71.46 ms 64.14 ms 43.9× Pass
deduplicate_sorted_100k Data Engineering 179.4 ms 1.81 ms 4.38 ms 98.9× Pass
pivot_table_sim_100k Data Engineering 578.2 ms 34.70 ms 63.62 ms 16.7× Pass
multi_column_sum_1m Data Engineering 1,552.7 ms 114.36 ms 132.07 ms 13.6× Pass
etl_filter_map_500k Data Engineering 827.7 ms 42.28 ms 60.35 ms 19.6× Pass
running_total_500k Data Engineering 581.8 ms 30.96 ms 40.07 ms 18.8× Pass
nested_list_flatten_50k Data Engineering 939.6 ms 2.85 ms 133.70 ms 329.9× Pass
etl_pipeline_100k Data Engineering 493.9 ms 40.49 ms 42.74 ms 12.2× Pass
json_transform_10k String/ETL 490.1 ms 4.00 ms 5.63 ms 122.5× Pass
windowed_analytics_200k Data Engineering 552.5 ms 5.13 ms 6.94 ms 107.8× Pass
string_etl_50k String/ETL 167.0 ms 57.17 ms 135.10 ms 2.9× Pass

Methodology

Hardware

  • CPU: AMD EPYC-Milan (znver3)
  • OS: Ubuntu 24.04 LTS
  • RAM: 16 GB

Software

  • Python: 3.12.3
  • Pyvorin: latest stable
  • Numba: 0.65.0
  • llvmlite: bundled

Timing

  • Warm runs only (min of multiple)
  • Compile time tracked separately
  • Cache hit loads in < 5 ms

Correctness

  • CPython ground truth for every benchmark
  • Exact equality for ints/strings
  • Approximate equality (1e-9) for floats

Note: fibonacci_iterative_92 failed due to 64-bit integer overflow. Python supports arbitrary-precision integers, but our native code currently uses 64-bit int for performance. This is a known limitation documented in our supported workloads matrix.

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