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.

With the introduction of NVIDIA Tesla Bio Workbench, it provides bio-physicists and computational chemists the tools to push the boundaries of bio-chemical research, optimizing the scientific workflow and accelerating the pace of research. Learn more.

BGI Speeds Genome Analysis with GPUs
Learn how Chinese genomics powerhouse BGI used NVIDIA Tesla GPUs to accelerate DNA sequencing.

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Accelerating HMMER using GPUs Scalable Informatics	MUMmerGPU: High-through DNA sequence alignment using GPUs Schatz, et al

Key Bioinformatics ISVs and Applications using CUDA

ISV	DESCRIPTION	GPU ADVANTAGE
CUDA-BLASTP	Protein sequence database scanning using NCBI BLASTP	10x speed-up: from minutes on CPUs to seconds on GPUs
CUDASW++	Protein sequence database (Smith-Waterman) scanning	10x-50x speed-up: achieving up to 30 GCUPs on query lengths over 5000
GPU HMMER	HMMER accelerated on CUDA	60-100x speed-up: from hours on CPUs to minutes on GPUs
PIPER Docking	Protein-Protein interaction modeling application	6x speed-up: superior to FPGA-based system for protein-protein docking
OpenEye ROCS	Molecule shape comparison application	170x speed-up: faster screening for drug discovery (see slide 43)

Other Relevant Software using CUDA

Technical Reports on using CUDA for Bioinformatics

Sequence alignment

Docking

CUDA-Acceleration in Related Verticals

See Also