As the cost of sequencing drops and the quantity of data produced by sequencing grows, the amount of processing dedicated to genomics is increasing at a rapid pace. Genomics is evolving in a number of directions simultaneously. Some key applications scale naturally to use resources available in the cloud, while other computations benefit from on-prem acceleration using FPGAs or GPUs. All of these computations strain the bandwidth and capacity of available resources. In this talk, Roche´s Tom Sheffler will share an overview of the memory-bound challenges present in genomics and venture some possible solutions.
Tom Sheffler
Tom earned his PhD from Carnegie Mellon in Computer Engineering with a focus on parallel computing architectures and prrogramming models. His interest in high-performance computing took him to NASA Ames, and then to Rambus where he worked on accelerated memory interfaces for providing high bandwidth. Following that, he co-founded the cloud video analytics company, Sensr.net, that applied scalable cloud computing to analyzing large streams of video data. He later joined Roche to work on next-generation sequencing and scalable genomics analysis platforms. Throughout his career, Tom has focused on the application of high performance computer systems to real world problems.