February 20, 2015 Nicole Hemsoth
From processors, memory, network, and beyond, making architectural choices to support large-scale genomics research is often fed as much by trial and error as it is empirical knowledge about what will work for a demanding application set.
Statistical genomics requires snappy rehashing of a central, consistent dataset (which can, luckily, be managed in cache) against an ever-evolving set of variables to strike correlations. “The problem isn’t with processing genomic data—it’s fairly easy to understand and accelerate from a computational perspective. It’s rather the discovery, the statistical genetics, which is the top-down comparison of many thousands of genomes all at once,” …Read more