Table 2.
Computing time of the parallel SWNI algorithm for two types of networks on increased processors.
| Number of processors | Network of 1000 nodes | Network of 1500 nodes |
|---|---|---|
| 1 | 6501.85 | 48102.6 |
| 2 | 3670.23 | 24528.4 |
| 4 | 2193.08 | 12528.7 |
| 6 | 1485.62 | 8457.89 |
| 8 | 1122.75 | 6405.23 |
| 10 | 908.12 | 5139.61 |
| 12 | 769.32 | 4280.88 |
| 14 | 666.19 | 3685.25 |
| 16 | 588.23 | 3254.62 |
| 18 | 525.86 | 2887.79 |
| 20 | 479.17 | 2621.81 |
| 22 | 439.52 | 2399.3 |
| 24 | 406.73 | 2217.95 |
| 26 | 383.95 | 2054 |
| 28 | 357.58 | 1921.4 |
| 30 | 338.8 | 1780.58 |
| 32 | 322.09 | 1689.94 |
We simulated two types of artificial gene networks in size of 1000 nodes, 3054 edges, and 1500 nodes, 4630500 edges, respectively, to assess the performance of the parallelized SWNI algorithm. The computing time is calculated. The results show that as increase in the processors number, the computing time of the algorithm falls dramatically. The study suggested that the parallelized SWNI algorithm has good performance on the artificial gene networks.