On the Impact of High Performance Computing in Big Data Analytics for Medicine

Abstract

For a long time, High Performance Computing (HPC) has been critical for running large-scale modeling and simulation using numerical models. The big data analytics domain (BDA) has been rapidly developed over the last years to process huge amounts of data now being generated in various domains. But, in general, the data analytics software was not developed inside the scientific computing community, and new approaches were adopted by BDA specialists. Data-intensive applications are needed in various fields of medicine and healthcare ranges from advanced research— as genomics, proteomics, epidemiology and systems biology—to medical diagnosis and treatments, or to commercial initiatives to develop new drugs. BDA needs the infrastructure and the fundamentals of HPC in order to face with the needed computational challenges. There are important differences in the approaches of these two domains: those that are working in BDA focus on the 5Vs of big data which are: volume, velocity, variety, veracity, and value, while HPC scientists tend to focus on performance, scaling, and the power efficiency of a computation. As we are heading towards extreme-scale HPC coupled with data intensive analytics, the integration of BDA and HPC is current hot topic of research with important impact on the development of BDA in medicine.