That way, GSK can run an analysis in a traditional relational environment and then in Kinetica if the workload requires a more computationally-intensive environment, without having to do a lot of tooling and rework. To that point, one the things Kinetica can do is make its GPU database look like a relational database so users can interact with it using a traditional language such as SQL. "There has been a lot of software created to answer certain questions highly specialized tools have limited functionality and are tuned to do a certain workload." "One of the things I like about Kinetica is it gives us more of a general-purpose use of the technology," said Ramsey. It will eventually operationalize the learnings from that undertaking as well. Similarly, GSK has a relationship with the National Cancer Institute and the U.S. "We have a much better ability to select ideal targets." "The goal is for us to be able to select a potential target but have a much deeper knowledge base from a variety of dimensions around that target," said Ramsey. GSK is also importing learnings from the Open Targets knowledgebase created by the European Bioinformatics Institute (EBI) and the Sanger Institute and combining that with its own historical data about the experiments GSK has run against targets, its clinical trials and how all of that relates. By doing the subdivision, you can do simulation faster," said Mark Ramsey, chief data officer at GSK, who invented and patented a similar idea for IBM around 1998.
"What you want to do is distribute that over a large number of nodes and let each node its piece of the simulation.
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