GPU Applications

Bioinformatics and Life Sciences

Sequencing and protein docking are very compute-intensive tasks that see a large performance benefit by using a CUDA-enabled GPU. There is quite a bit of ongoing work on using GPUs for a range of Bioinformatics and life sciences codes.


Technical Reports on CUDA for Bioinformatics
> High-performance computational pipeline for metagenomic data analysis
>  MrBayes on GPU
> Stochastic simulation algorithm (SSA) for biological systems
> Self-organizing computation models of human visual cortex


> 3D Protein Docking
> Accelerating PIPER using CUDA
> Protein Docking work at University of Wisconsin and video interview of David Dynerman
> Jack Collins from National Cancer Institute talks about GPU computing

Sequence alignment

> CUSHAW: a CUDA compatible short read aligner to large genomes
> MUMmerGPU: High-through DNA sequence alignment using GPUs
> CUDASW++: Smith-Waterman Sequence Database Searches
> Smith-Waterman sequence alignment using CUDA
> Papers on Bioinformatics using CUDA
> Infernal-GPU: CUDA-accelerated RNA Alignment , Infernal (INFERence of RNA Alignment)
> SWAMP Sequence alignment
> CMatch: Fast protein and gene sequence string matching
Other Relevant Software using CUDA
> MUMmerGPU: High-throughput DNA sequence alignment using CUDA
> Smith-Waterman Code on GPUs
> Folding @ home using CUDA-enabled GPUs
> LISSOM: model of human neocortex using CUDA
> CUDA-based AutoDock from Silicon Informatics

CUDA-Acceleration in Related Verticals
> Medical Imaging
> Molecular Dynamics
> Computational Chemistry
> ArrayFire GPU function library for C, C++, FORTRAN

See Also
> Tesla/CUDA Success stories
> Other Tesla Vertical Solutions
> CUDA Software development tools & libraries
> Buy Tesla