Native Edlib Benchmark Report

• Platform: macOS-26.2-arm64-arm-64bit

• Python: 3.9.6

• Reads per benchmark case: 500

• Repetitions per benchmark case: 3

• Comparator: native Edlib C/C++ API, EDLIB_MODE_NW, EDLIB_TASK_DISTANCE, fixed threshold k.

• Additional baselines: exact hash lookup for k=0; BK-tree and neighbor lookup approximate baselines for k=1.

• Gate: make native-exact-gate requires zero mismatches, large-library exact rows to beat exact_hash_lookup, large-library indexed k=1 rows to beat exhaustive Edlib by >10x, beat the best BK-tree/neighbor baseline, and verify no more than 1.05 candidates/read, plus large-library k=2 substitution rows to beat exhaustive Edlib by >8x with no more than 1.05 verified candidates/read and Levenshtein k=2 insertion/deletion rows to beat exhaustive Edlib by >8x while verifying no more than 25 candidates/read.

• Assignment mismatches recorded across all rows: 0.

• Every benchmark run aborts on assignment disagreement between DotMatch and native Edlib scan.

Gated Native Scaling Claims

claim	large_library_rows	min_speedup_vs_edlib	median_speedup_vs_edlib	max_verified_per_read	min_speedup_required	max_verified_required
k=1 substitution indexed rows	36	790.91	1160.80	1.00	10.00	1.05
k=2 substitution indexed rows	36	8.91	17.45	1.00	8.00	1.05
Levenshtein k=2 insertion/deletion rows	18	8.08	13.00	1.00	8.00	25.00

Highest Observed Microbenchmark Speedups

dotmatch_tool	n_targets	len	k	error_mode	err	reads_per_sec_dotmatch	reads_per_sec_edlib	verified_per_read	peak_rss_kb	speedup_vs_edlib_native
dotmatch_exact_direct	4096	16	0	exact	0.000	13513538.20	943.30	1.00	9888.00	14714.22
dotmatch_exact_batch	4096	16	0	one_substitution	0.010	13513495.70	940.20	0.00	10128.00	14535.76
dotmatch_exact_batch	4096	16	0	one_substitution	0.000	13157875.20	946.80	0.00	10128.00	14347.26
dotmatch_exact_batch	4096	16	0	one_substitution	0.030	13513495.70	940.00	0.00	10128.00	14174.00
dotmatch_exact_direct	4096	16	0	one_substitution	0.005	13157915.50	941.00	0.00	10128.00	14147.29
dotmatch_exact_batch	4096	24	0	exact	0.000	9615376.00	874.50	1.00	11984.00	10995.31
dotmatch_exact_batch	4096	24	0	one_substitution	0.010	9433973.30	876.20	0.00	14464.00	10766.92
dotmatch_exact_batch	4096	24	0	one_substitution	0.000	9259268.60	873.00	0.00	14448.00	10623.30
dotmatch_exact_batch	4096	24	0	one_substitution	0.030	9259248.60	876.40	0.00	14464.00	10565.09
dotmatch_exact_direct	4096	24	0	one_substitution	0.005	9259248.60	877.00	0.00	14464.00	10557.89
dotmatch_exact_batch	4096	32	0	one_substitution	0.030	7352935.50	820.60	0.00	19328.00	8960.44
dotmatch_exact_batch	4096	32	0	one_substitution	0.005	7246382.80	821.80	0.00	19328.00	8861.91

Median Speedup Summary

len	k	n_targets	error_mode	speedup_vs_edlib_native
16	0	4096	exact	14714.22
16	0	4096	one_substitution	14355.12
24	0	4096	exact	10995.31
24	0	4096	one_substitution	10614.78
32	0	4096	one_substitution	8716.42
32	0	4096	exact	8663.13
16	0	737	exact	3251.34
16	0	737	one_substitution	2667.38
24	0	737	exact	2386.00
24	0	737	one_substitution	2116.58
32	0	737	exact	1921.31
16	1	4096	exact	1861.95

Repeated-Run Statistics

tool	error_mode	n_targets	len	k	err	reads_per_sec_mean	reads_per_sec_p50	reads_per_sec_p95	reads_per_sec_cv	peak_rss_kb_max	mismatches_sum
dotmatch_exact_batch	exact	4096	16	0	0.000	13433100.10	13513538.20	14208527.11	0.07	9888.00	0.00
dotmatch_exact_batch	exact	4096	24	0	0.000	9554908.70	9615376.00	9615395.35	0.01	11984.00	0.00
dotmatch_exact_batch	exact	4096	32	0	0.000	7045981.87	7042257.60	7225959.21	0.03	18992.00	0.00
dotmatch_exact_batch	exact	737	16	0	0.000	16487456.27	16666669.10	16666669.10	0.02	3200.00	0.00
dotmatch_exact_batch	exact	737	24	0	0.000	11316437.77	11627912.90	11877054.86	0.07	10944.00	0.00
dotmatch_exact_batch	exact	737	32	0	0.000	8824142.83	8771934.70	8912896.66	0.01	14816.00	0.00
dotmatch_exact_batch	exact	96	16	0	0.000	20728472.00	25000100.70	26184185.34	0.41	1776.00	0.00
dotmatch_exact_batch	exact	96	24	0	0.000	21437178.57	21739074.90	21739173.99	0.02	10848.00	0.00
dotmatch_exact_batch	exact	96	32	0	0.000	17446642.80	17241386.50	17795578.51	0.02	14784.00	0.00
dotmatch_exact_batch	one_substitution	4096	16	0	0.000	13289094.93	13157875.20	13815813.63	0.04	10128.00	0.00
dotmatch_exact_batch	one_substitution	4096	16	0	0.005	13276428.80	13157915.50	13477937.68	0.02	10128.00	0.00
dotmatch_exact_batch	one_substitution	4096	16	0	0.010	13638636.43	13513495.70	13851375.68	0.02	10128.00	0.00
dotmatch_exact_batch	one_substitution	4096	16	0	0.030	12884968.10	13513495.70	13513495.70	0.08	10128.00	0.00
dotmatch_exact_batch	one_substitution	4096	24	0	0.000	9440817.10	9259268.60	9749449.55	0.03	14448.00	0.00
dotmatch_exact_batch	one_substitution	4096	24	0	0.005	9046396.80	9259248.60	9259248.60	0.04	14464.00	0.00
dotmatch_exact_batch	one_substitution	4096	24	0	0.010	9442934.73	9433973.30	9766920.02	0.04	14464.00	0.00
dotmatch_exact_batch	one_substitution	4096	24	0	0.030	9203138.00	9259248.60	9259248.60	0.01	14464.00	0.00
dotmatch_exact_batch	one_substitution	4096	32	0	0.000	7226336.50	7042257.60	7627299.54	0.06	19328.00	0.00
dotmatch_exact_batch	one_substitution	4096	32	0	0.005	7375778.90	7246382.80	7867493.86	0.07	19328.00	0.00
dotmatch_exact_batch	one_substitution	4096	32	0	0.010	6977047.27	6944447.70	7032476.61	0.01	19328.00	0.00
dotmatch_exact_batch	one_substitution	4096	32	0	0.030	7087229.50	7042246.10	7420649.75	0.05	19328.00	0.00
dotmatch_exact_batch	one_substitution	737	16	0	0.000	13888873.00	13888873.00	13888873.00	0.00	3280.00	0.00
dotmatch_exact_batch	one_substitution	737	16	0	0.005	13909788.03	14285700.50	14285743.34	0.05	3280.00	0.00
dotmatch_exact_batch	one_substitution	737	16	0	0.010	13909788.03	14285700.50	14285743.34	0.05	3280.00	0.00

Evidence Boundary

These are native Edlib scan microbenchmarks for exact short-DNA assignment workloads, plus simple exact-hash and BK-tree/neighbor baselines. The largest rows are useful for understanding algorithmic scaling against exhaustive scan, but they are not end-to-end workflow speed claims. Exact k=0 lookup should be judged against hash-table baselines: broad exact-hash superiority is not claimed unless the exact gate proves it, while large-library exact rows (n_targets >= 4096) may be described only when make native-exact-gate records a >1.0 ratio against exact_hash_lookup. For k=1, large-library indexed rows may be described as non-exhaustive only when the same gate records zero Edlib disagreements, >10x speedup over exhaustive Edlib scan, >1.0 speedup over the best BK-tree/neighbor baseline, and no more than 1.05 verified candidates/read. Fixed-length k=2 substitution rows may be described as non-exhaustive only when the same gate records zero Edlib disagreements, >8x speedup over exhaustive Edlib scan, and no more than 1.05 verified candidates/read. Levenshtein k=2 insertion/deletion rows may be described as non-exhaustive only when the same gate records zero Edlib disagreements, >8x speedup over exhaustive Edlib scan, and no more than 25 verified candidates/read. This remains scoped to packed A/C/G/T fixed-window assignment up to 32 bases with fallback preserving semantics for unsupported cases.