Clusters of GPUs are emerging as a new computational scenario. Programming them
requires the use of hybrid models that increase the complexity of the applications, reducing …

Hybrid computer systems combine compute units (CUs) of different nature like CPUs, GPUs
and FPGAs. Simultaneously exploiting the computing power of these CUs requires a careful …

The astonishing development of diverse and different hardware platforms is twofold: on one
side, the challenge for the exascale performance for big data processing and management; …

The improved understanding of complex interactions of marine ecosystem components
makes the use of fully coupled hydrodynamic, biogeochemical and individual based models …

Nowadays the use of hardware accelerators, such as the graphics processing units or
XeonPhi coprocessors, is key in solving computationally costly problems that require high …

The calculation of pairwise correlation coefficient on a dataset, known as the correlation
matrix, is often used in data analysis, signal processing, pattern recognition, image …

Data reduction is perhaps the most critical component in retrieving information from big data
(ie, petascale-sized data) in many data-mining processes. The central issue of these data …

Causal discovery is a technique to find the causal relationship between variables using
data. This technique has many applications in data mining and knowledge discovery …

Intelligent optimization algorithm (IOA) has been widely studied and applied to solve various
optimization problems. When scholars improve IOA with mathematical methods, they also …

Scientific image processing is a topic of interest for a broad scientific community since it is a
mean of gaining understanding and insight into the data for a growing number of …