GPU-Accelerated Analysis of DNA Sequencing Data
Personalizing treatments based on patients’ genetic variation will revolutionize how medicine cures diseases; but time to analysis has become a major bottleneck. Parabricks has accelerated secondary analysis of sequencing data to analyze a 30x whole genome from days to ONE hour.
Using GPUs, Parabrick’s accelerated workflow with superior visualizations can finish alignment, pre-processing and variant calling—a process that can easily take up to 30 hours on 32 vCPU servers—at trailblazing speeds. Deep learning technologies such as DeepVariant have been accelerated by nearly an order of magnitude by using state of the art software stack from NVIDIA.
Watch this webinar replay for an in-depth understanding of how GPUs can be used for accelerating industry standard algorithms used in BWA-Mem, GATK4 such as Smith-Waterman, PairHMM and deep learning technologies used in variant calling.
By watching this webinar replay, you will:
- Develop an understanding of the compute intensive nature of genomic analysis;
- Learn about accelerating dynamic programming on GPUs, including deep learning on genomic data using DeepVariant; and
- Explore options for large computing power for genomic analysis, including the DGX-1, as well as GPUs on-premise and on the cloud
ON DEMAND WEBINAR REGISTRATION
Presented By
Mehrzad Samadi
CEO, Parabricks
Mehrzad Samadi has over 10 years’ experience in high performance computing. An accomplished author, he has published over 20 papers in the area of compiler optimizations for GPUs, heterogeneous computing and machine learning. He co-founded Parabricks in 2015 to provide high performance bioinformatics tools to enable researchers to accelerate their research efforts. Prior to Parabricks, Samadi worked as a researcher at Microsoft research. He received his Ph.D. in Computer Science and Engineering from the University of Michigan.
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