AI/ML-powered genomic sequencing techniques offer new computational approaches to streamlining key analytical problems in genomic medicine. Making sense of extremely large volumes of genomic sequencing data and integrating it with other molecular and clinical data is a major challenge.
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Our fully-integrated Ku:l Micro Data Centre which comes in multiple deployment configurations can enable distributed AI/ML workloads in virtually any location and at any scale. This GPU-rich solution offers maximum performance, improved energy efficiency and sustained high heat recovery, freeing HPC from the data centre.
Learn moreThe aim of genomic sequencing is to determining the order of the four chemical building blocks - called "bases" - that make up the DNA molecule. The sequence tells scientists the kind of genetic information that is carried in a particular DNA segment. When compared to large stretched of DNA from multiple bases, the sequence information can yield an enormous amount of information about the role of inheritance in susceptibility to disease and in response to environmental influences. In addition, the ability to sequence the genome more rapidly and cost-effectively creates vast potential for diagnostics and therapies. These are the top applications of AI/ML-powered genomic sequencing.
Deploying high-performance computing (HPI) to enable AI/ML-powered genomic sequencing workloads on-premises doesn't come without its challenges. Deploying IT on-premises means deploying it in close proximity to people. People present risk to the IT in terms of accidental damage, tampering and contamination whilst the IT presents risk to the people in terms of noise, heat, germs and space consumption. Other challenges also present themselves such as space availability, power availability, heat rejection, scalability, monitoring and maintenance.
View the solutionFreeing HPC from the data centre
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