PyEvolve: a toolkit for statistical modelling of molecular evolution

BMC Bioinformatics

Table 1 PyEvolve benchmarking. Time taken was estimated as time for optimisation. Number of runs per condition ranged from 1 to 5. ¹Model – See text for details of the codon and dinuc substitution models; ²Levels – indicates whether Simulated Annealing (SA), Likelihood Function (LF) or BOTH parallelisation levels were used; ³Parallel degree refers to the number of virtual cpu's at the ⁴SA or LF levels (for the LF level, this is defined per SA virtual cpu); ⁵the number of likelihood function evaluations made during the optimisation for ⁶10 or 20 sequences, expressed in thousands. See text for details of the data and hardware used.

Model ¹	Levels ²	Total cpus	Parallel degree ³		lfe (1000's) ⁵		Total Time (minutes)		Time (seconds) per 1000 lfe
			SA ⁴	LF ⁴	10 ⁶	20 ⁶	10	20	10	20
codon	Serial	1	1	1	56	121	124	269	133.06	133.65
	LF	2	1	2	56	121	81	182	86.49	90.16
		4	1	4	56	122	55	130	58.69	63.78
		8	1	8	57	122	41	100	43.99	49.02
		16	1	16	57	120	35	82	36.52	41.02
	SA	2	2	1	57	122	85	178	89.40	87.22
		4	4	1	57	121	58	121	60.19	60.15
		8	8	1	57	122	44	99	46.10	48.83
		16	16	1	58	122	38	88	39.48	43.16
	BOTH	4	2	2	57	125	56	125	59.39	60.03
		8	2	4	58	122	40	89	41.57	43.74
		8	4	2	57	121	39	85	40.92	42.37
		16	2	8	56	121	30	69	32.28	34.47
		16	4	4	58	121	28	63	29.31	31.27
		16	8	2	57	121	31	71	32.70	35.30
dinuc	Serial	1	1	1	54	119	17	37	19.22	18.47
	LF	2	1	2	54	119	11	24	12.59	12.29
		4	1	4	54	119	7	16	7.80	7.82
		8	1	8	54	117	5	11	5.30	5.55
		16	1	16	55	119	4	9	4.04	4.41
	SA	2	2	1	53	118	11	23	12.19	11.51
		4	4	1	54	118	7	15	8.32	7.77
		8	8	1	54	118	5	12	5.91	5.89
		16	16	1	53	118	4	10	4.73	4.86
	BOTH	4	2	2	54	118	7	15	8.07	7.69
		8	2	4	54	118	5	10	5.14	5.04
		8	4	2	54	117	5	10	5.61	5.28
		16	2	8	54	118	3	7	3.76	3.74
		16	4	4	54	119	3	7	3.76	3.57
		16	8	2	54	119	4	8	4.13	4.10

ISSN: 1471-2105