Nowadays, GPU programming plays an important role in software development. The massive parallel hardware enables us to analyze bigger datasets, process data in finer details or visualize simulations in real-time. Still, due to the programming complexity and the lack of knowledge, GPUs are not regularly used in many scientific fields. Thankfully, GPU programming matures and provides simpler tools to utilize GPU hardware. In this presentation, we describe multiple GPU programming frameworks (ranging from high-level to low-level) and showcase them on selected bioinformatics algorithms.

About the speaker

Adam Šmelko is a third year doctoral student in the Department of Distributed and Dependable Systems at Charles University in Prague with the dissertation topic of Employing parallel computing in data-intensive tasks. 
He specializes in GPU programming and in the analysis of memory accesses in programs. 
His current research topic involves development of tools for effective memory layouts  and traversals of data structures.