Bioinformatics is now Harder Better FASTER Stronger.. Courtesy GPU Computing

GPU computing or GPGPU is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing.

The model for GPU computing is to use a CPU and GPU together in a heterogeneous co-processing computing model. The sequential part of the application runs on the CPU and the computationally-intensive part is accelerated by the GPU. From the user’s perspective, the application just runs faster because it is using the high-performance of the GPU to boost performance.

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. 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 bio-informatics and life sciences codes.

Some examples are given below:

GPU-HMMER accelerates the hmmsearch tool using GPUs and gets speed-ups ranging from 60-100x. GPU-HMMER can take advantage of multiple Tesla GPUs in a workstation to reduce the search from hours on a CPU to minutes using a GPU.

MUMmerGPU uses the new Compute Unified Device Architecture
(CUDA) from nVidia to align multiple query sequences against a single reference sequence stored
as a suffix tree. By processing the queries in parallel on the highly parallel graphics card,
MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence
alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU
by 3.5-fold in total application time when aligning reads from recent sequencing projects using
Solexa/Illumina, 454, and Sanger sequencing technologies.

CUDA-BLASTP running on a workstation with two Tesla C1060 GPUs is 10x faster than NCBI BLAST (2.2.22) running on an Intel i7-920 CPU. This cuts compute time from minutes on CPUs to seconds using GPUs.

CUDASW++ is a bio-informatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. CUDASW++ supports query lengths up to 59K.

VMD is a molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting. Several key kernels and applications in VMD now take advantage of the massively parallel CUDA architecture of NVIDIA’s GPUs. These applications run 20x to 100x faster when using a NVIDIA CUDA GPU compared to running them on a CPU only.

For more information, visit http://www.nvidia.com/object/tesla_bio_workbench.html

Source: www.nvidia.com