Table 3 Resource usage of error correction tools for the correction of Human dataset S4
From: CARE 2.0: reducing false-positive sequencing error corrections using machine learning
Name | #threads | Runtime | Memory |
|---|---|---|---|
CARE 1.0 (CPU) | 64 | 157 | 241 |
CARE 1.0 (GPU) | 16 | 84 | 238 |
CARE 2.0 SE (CPU) | 64 | 95 | 234 |
CARE 2.0 SE (GPU) | 16 | 42 | 220 |
CARE 2.0 PE (CPU) | 64 | 98 | 235 |
CARE 2.0 PE (GPU) | 16 | 43 | 220 |
CARE 2.0 PE Forest (CPU) | 64 | 120 | 234 |
CARE 2.0 PE Forest (GPU) | 16 | 60 | 221 |
CARE 2.0 PE Forest* (CPU) | 64 | 265 | 245 |
CARE 2.0 PE Forest* (GPU) | 16 | 180 | 245 |
Musket | 64 | 249 | 138 |
SGA | 64 | 334 | 37 |
Karect | 128 | 6209 | 240 |
Bcool | 64 | 346 | 43 |
Lighter | 64 | 39 | 16 |
BFC | 64 | 85 | 108 |