📊 optiVLSI Benchmark Report

🖥️ N/A 🐍 Python 3.11.15 ⏱️ 45 benchmarks

Total Benchmarks

45

Algorithms Tested

5

Fastest Mean

161.3μs

Bellman Ford

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	182.71	156.57	259.27	167.15	5	× 1.0
NetworkX	1105.22	1085.97	1426.27	1099.03	655	× 6.0
Pythonic	50609.78	50432.16	50840.41	50603.59	20	× 277.0

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1669.38	1612.59	1777.41	1671.47	558	× 1.0
NetworkX	4143.76	4079.44	4491.42	4134.23	232	× 2.5
Pythonic	430826.07	429534.02	434432.22	429935.01	5	× 258.1

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	13781.68	13724.32	13836.02	13783.11	70	× 1.0
NetworkX	15887.91	15456.73	18722.70	15731.54	64	× 1.2
Pythonic	3429501.18	3401929.46	3445397.13	3430793.84	5	× 248.8

Dijkstra

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	309.80	262.52	432.31	285.41	5	× 1.0
Pythonic	316.94	306.40	635.73	313.20	2564	× 1.0
NetworkX	620.17	581.56	1291.60	604.88	829	× 2.0

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	527.98	505.82	824.58	526.23	1647	× 1.0
Pythonic	1332.22	1302.92	1742.93	1328.75	685	× 2.5
NetworkX	2127.89	1924.28	70452.94	1958.35	416	× 4.0

size = 175

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1794.85	1757.89	2080.48	1784.79	523	× 1.0
Pythonic	4312.65	4243.20	4716.36	4304.20	227	× 2.4
NetworkX	6453.61	6301.37	6958.91	6439.84	152	× 3.6

Kruskal

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	205.76	164.87	265.72	189.46	5	× 1.0
Pythonic	750.55	731.33	1044.25	748.82	1153	× 3.6
NetworkX	1378.05	1287.17	1926.52	1335.72	482	× 6.7

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	278.29	265.80	532.09	274.76	2590	× 1.0
Pythonic	2832.99	2798.75	3273.99	2822.33	344	× 10.2
NetworkX	5398.93	4643.35	92104.98	4835.81	200	× 19.4

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	697.49	675.01	785.17	697.13	1100	× 1.0
Pythonic	10903.76	10780.72	11539.57	10846.35	89	× 15.6
NetworkX	22358.94	17929.69	118030.88	18701.13	54	× 32.1

Lee

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	161.32	136.69	236.17	139.66	5	× 1.0
Pythonic	1001.15	978.58	1406.94	995.58	724	× 6.2
NetworkX	7214.03	7031.60	7417.41	7210.65	122	× 44.7

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	381.25	365.71	604.53	379.32	2373	× 1.0
Pythonic	3977.61	3950.95	4202.44	3970.67	242	× 10.4
NetworkX	34745.67	30452.17	118626.35	32114.39	31	× 91.1

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1413.42	1388.03	1699.44	1410.74	628	× 1.0
Pythonic	16255.74	16026.44	19012.58	16193.75	62	× 11.5
NetworkX	162978.37	138386.26	235020.21	140219.83	8	× 115.3

Prim

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	313.31	278.76	396.27	303.34	5	× 1.0
Pythonic	892.08	866.05	1326.56	889.30	842	× 2.8
NetworkX	1024.19	973.78	1451.67	1015.24	664	× 3.3

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1617.00	1596.86	1925.66	1613.75	566	× 1.0
Pythonic	3282.43	3223.10	3869.56	3271.17	254	× 2.0
NetworkX	3582.53	3510.80	4691.66	3566.84	254	× 2.2

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	15616.13	15488.77	16075.62	15599.26	63	× 1.0
NetworkX	18907.78	13552.39	115329.24	14809.15	65	× 1.2
Pythonic	19741.01	17553.51	21462.44	19644.86	46	× 1.3

⚡ Numba Speedup Summary

Algorithm	Size	Pythonic → Numba	NetworkX → Numba
Bellman Ford	50	277.0x	6.0x
Bellman Ford	100	258.1x	2.5x
Bellman Ford	200	248.8x	1.2x
Dijkstra	50	1.0x	2.0x
Dijkstra	100	2.5x	4.0x
Dijkstra	175	2.4x	3.6x
Kruskal	50	3.6x	6.7x
Kruskal	100	10.2x	19.4x
Kruskal	200	15.6x	32.1x
Lee	50	6.2x	44.7x
Lee	100	10.4x	91.1x
Lee	200	11.5x	115.3x
Prim	50	2.8x	3.3x
Prim	100	2.0x	2.2x
Prim	200	1.3x	1.2x