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Table 5 Computational times

From: Multi-objective optimization for RNA design with multiple target secondary structures

  

MODENA

  

Dataset

l (nt)

pop. size

#threads

t MODENA (s)

t Frnakenstein (s)

t RNAdesign (s)

two targets

149

100

4

446a

13,988a

2,924c

three targets

100

100

4

339a

1,924b

1,249c

four targets

100

100

4

385a

2,848b

1,653c

RNA deviced

95

100

4

2,428a

-

-

RNA devicee

95

200

6

15,460b

-

-

PK60

60

30

3

1,997b

-

-

PK80

80

30

3

7,780b

-

-

LE80

61

30

3

2,140b

-

-

  1. An l indicate the mean length for each dataset. The ‘pop. size’, ‘#threads’, and t MODENA columns indicate GA population size, the number of OpenMP threads used to parallelize a design, and the mean computational time of MODENA for each dataset, respectively. For example, from the ‘two targets’ row of this table, we can see that one MODENA run of the two-target design with a population size of 100 and four OpenMP threads took 446 seconds on average. PK60, PK80, and LE80 are the two-target datasets with pseudoknot. The computational times for “frnakenstein.py -s 100” and RNAdesign with option “-n 500 –thin 200 -b 100 –scale 1” are shown in t Frnakenstein and t RNAdesign columns, respectively
  2. aComputational times measured on a PC with Intel Xeon E5-2603 (1.80 GHz) and 16 GB of memory (CentOS 5.9)
  3. bThe times measured on a PC with Intel Xeon E5-2665 (2.40 GHz) and 132 GB of memory (CentOS 6.4)
  4. cComputational times measured on a PC with Intel Core i3-2100 (3.10 GHz) and 3.6 GB of memory (Fedora 18, which was installed in order to execute RNAdesign)
  5. dand e indicate RNA device designs which take and does not take energy barrier height into account, respectively
  6. euses the maximum number of GA generations of 400