With the growing popularity of shared resources, large volumes of complex data of different
types are collected automatically. Traditional data mining algorithms generally have …

Many modern parallel computing systems are heterogeneous at their node level. Such
nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon …

Recent developments in sensor technology, wearable computing, Internet of Things (IoT),
and wireless communication have given rise to research in ubiquitous healthcare and …

This paper shows a global picture of the deployment of networked processing services for
genomic data sets. Many current research and medical activities make an extensive use of …

For more than a decade, machine learning (ML) and deep learning (DL) techniques have
been a mainstay in the toolset for the analysis of large amounts of weakly correlated or high …

Heterogeneous computing systems provide high performance and energy efficiency.
However, to optimally utilize such systems, solutions that distribute the work across host …

On the road to exascale, coprocessors are increasingly becoming key building blocks of
High Performance Computing platforms. In addition to their energy efficiency, these many …

The DNA sequence analysis is a data and computationally intensive problem and therefore
demands suitable parallel computing resources and algorithms. In this paper, we describe …

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors
of complex cellular systems are among the most challenging goals in Systems Biology …

The paper deals with parallelization of computing similarity measures between large
vectors. Such computations are important components within many applications and …