📊 optiVLSI Benchmark Report

🖥️ N/A 🐍 Python 3.11.15 ⏱️ 45 benchmarks

Total Benchmarks

45

Algorithms Tested

5

Fastest Mean

170.3μs

Bellman Ford

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	191.09	164.29	274.31	168.59	5	× 1.0
NetworkX	1080.50	1045.30	2273.00	1064.30	612	× 5.7
Pythonic	45623.90	45485.70	45828.48	45604.88	22	× 238.8

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1731.06	1658.94	1819.76	1721.01	560	× 1.0
NetworkX	4260.31	4180.02	4452.54	4254.16	221	× 2.5
Pythonic	438997.35	436274.74	443408.59	438739.35	5	× 253.6

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	14096.11	13640.70	14377.11	14234.07	71	× 1.0
NetworkX	17017.66	16136.81	19566.60	16821.99	58	× 1.2
Pythonic	3521849.91	3510845.99	3532681.47	3521329.84	5	× 249.8

Dijkstra

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	267.27	214.92	361.42	261.37	5	× 1.0
Pythonic	338.11	324.03	670.36	334.21	2313	× 1.3
NetworkX	578.20	557.71	880.43	578.58	870	× 2.2

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	611.00	574.72	956.51	609.68	1406	× 1.0
Pythonic	1303.93	1273.29	1697.79	1300.52	700	× 2.1
NetworkX	2000.61	1963.10	2311.30	1992.82	453	× 3.3

size = 175

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1841.20	1798.50	2397.19	1824.70	497	× 1.0
Pythonic	4298.44	4233.39	4583.71	4295.50	235	× 2.3
NetworkX	6408.61	6263.72	6683.62	6396.26	153	× 3.5

Kruskal

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	203.93	174.16	278.75	194.79	5	× 1.0
Pythonic	724.84	705.33	1257.18	723.38	1175	× 3.6
NetworkX	1385.34	1265.90	2395.93	1328.11	478	× 6.8

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	275.84	262.57	616.72	271.63	2753	× 1.0
Pythonic	2781.09	2740.23	3186.49	2764.68	345	× 10.1
NetworkX	4886.35	4545.09	5888.20	4747.69	205	× 17.7

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	685.34	646.74	785.43	684.13	1032	× 1.0
Pythonic	10989.53	10799.36	11593.20	10958.43	88	× 16.0
NetworkX	23226.19	17905.11	138913.30	18805.74	52	× 33.9

Lee

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	170.26	142.78	220.76	160.16	5	× 1.0
Pythonic	1001.60	983.09	1694.44	994.50	692	× 5.9
NetworkX	7274.60	7123.55	7554.89	7268.12	128	× 42.7

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	384.33	362.74	552.64	383.35	2316	× 1.0
Pythonic	3972.43	3936.95	4248.61	3964.23	246	× 10.3
NetworkX	36175.78	30274.44	149236.50	32323.60	32	× 94.1

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1436.77	1387.30	1594.27	1427.86	607	× 1.0
Pythonic	16166.10	16066.64	16543.80	16139.76	62	× 11.3
NetworkX	171126.45	140927.99	239882.67	146970.99	7	× 119.1

Prim

size = 50

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	313.46	275.65	400.62	291.19	5	× 1.0
Pythonic	914.94	891.42	1187.85	912.02	884	× 2.9
NetworkX	1026.69	985.20	1257.98	1017.85	385	× 3.3

size = 100

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	1646.77	1623.66	1785.28	1642.27	550	× 1.0
Pythonic	3312.11	3243.05	4162.43	3285.39	255	× 2.0
NetworkX	3647.51	3550.76	5002.01	3636.17	253	× 2.2

size = 200

Variant	Mean (μs)	Min (μs)	Max (μs)	Median (μs)	Rounds	vs Fastest
Numba	16017.42	15802.24	16857.12	15970.83	61	× 1.0
NetworkX	16471.05	13120.26	105586.32	13656.90	68	× 1.0
Pythonic	19653.61	18370.45	28004.18	19305.34	49	× 1.2

⚡ Numba Speedup Summary

Algorithm	Size	Pythonic → Numba	NetworkX → Numba
Bellman Ford	50	238.8x	5.7x
Bellman Ford	100	253.6x	2.5x
Bellman Ford	200	249.8x	1.2x
Dijkstra	50	1.3x	2.2x
Dijkstra	100	2.1x	3.3x
Dijkstra	175	2.3x	3.5x
Kruskal	50	3.6x	6.8x
Kruskal	100	10.1x	17.7x
Kruskal	200	16.0x	33.9x
Lee	50	5.9x	42.7x
Lee	100	10.3x	94.1x
Lee	200	11.3x	119.1x
Prim	50	2.9x	3.3x
Prim	100	2.0x	2.2x
Prim	200	1.2x	1.0x