Table 2.
GPU-powered tools for sequence alignment, along with the speed-up achieved and the solutions used for code parallelization
| Sequence alignment | ||||
|---|---|---|---|---|
| Tool name | Speed-up | Parallel solution | Reference | |
| Sequence alignment based on BWT | BarraCUDA | – | GPU | [22] |
| Sequence alignment based on BWT | CUSHAWGPU | – | GPU | [23] |
| Sequence alignment based on BWT | GPU-BWT | – | GPU | [24] |
| Sequence alignment based on BWT | SOAP3 | – | CPU-GPU | [25] |
| Sequence alignment based on hash table | SARUMAN | – | GPU | [26] |
| Sequence alignment with gaps based on BWT | SOAP3-dp | – | CPU-GPU | [27] |
| Tool to map SNP exploiting SOAP3-dp | G-SNPM | – | CPU-GPU | [28] |
| Sequence alignment exploiting SOAP3-dp | G-CNV | 18× | CPU-GPU | [29] |
| Alignment of gapped short reads with Bowtie2 algorithm | nvBowtie | 8× | GPU | [30] |
| Alignment of gapped short reads with Bowtie2 algorithm | MaxSSmap | – | GPU | [31] |
| Reads assembly exploiting the de Bruijn approach | GPU-Euler | 5× | GPU | [32] |
| Reads assembly exploiting the de Bruijn approach | MEGAHIT | 2× | GPU | [33] |
| Sequence alignment (against database) tool | – | 2× | GPU | [34] |
| Sequence alignment (against database) tool | CUDA-BLASTP | 6× | GPU | [35] |
| Sequence alignment (against database) tool | G-BLASTN | 14.8× | GPU | [36] |
| Sequence alignment with Smith-Waterman method | SW# | – | GPU | [37] |
| Sequence alignment based on suffix tree | MUMmerGPU 2.0 | 4× | GPU | [38] |
| Sequence similarity detection | GPU CAST | 10× | GPU | [39] |
| Sequence similarity detection based on profiled Hidden Markov Models | CUDAMPF | 11–37× | GPU | [40] |
| Multiple sequence alignment with Clustal | CUDAClustal | 2× | GPU | [41] |
| Multiple sequence alignment with Clustal | GPU-REMuSiC | – | GPU | [42] |