Table 3.
avg. length | # of sequences | CPU, Farrar | 1 GPU | 4 GPUs |
---|---|---|---|---|
51 | 4000 | 24,113 | 8,064 | 2,070 |
8000 | 95,156 | 31,111 | 7,855 | |
12000 | 210,806 | 69,083 | 17,439 | |
154 | 2000 | 28,300 | 17,931 | 4,609 |
4000 | 112,109 | 67,747 | 17,284 | |
6000 | 251,730 | 149,030 | 37,622 | |
257 | 2000 | 61,182 | 49,226 | 12,535 |
4000 | 242,756 | 186,436 | 47,305 | |
6000 | 543,656 | 410,255 | 103,278 | |
459 | 2000 | 149,269 | 155,631 | 39,478 |
4000 | 594,976 | 594,140 | 149,539 | |
6000 | 1339,538 | 1332,831 | 333,593 | |
608 | 800 | 41,675 | 50,222 | 12,840 |
1200 | 92,776 | 106,840 | 27,406 | |
1600 | 164,135 | 191,793 | 48,463 | |
1103 | 800 | 123,572 | 164,780 | 41,946 |
1200 | 278,194 | 359,065 | 89,899 | |
1600 | 495,624 | 628,847 | 158,699 |
Mean times (in seconds) for the Smith-Waterman algorithm applied to different sets of sequences. Average lengths of sequences as well as cardinality of sets are given. The Farrar's implementation computes only scores while our GPU-based implementation computes scores and alignments.