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.] Complex pipelines are written in such a manner that they are portable to either clusters or clouds. Key kernels are also being ported to GPUs in a drop-in replacement for their non-accelerated counterpart. These techniques are helping to address challenges of scaling up genomics computations and porting validated pipelines to new systems. However, 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.