As both CPUs and GPUs become employed in a wide range of applications, it has been
acknowledged that both of these Processing Units (PUs) have their unique features and …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

The paper focuses on the small-signal stability analysis of large power systems with
inclusion of multiple delayed signals. The following four techniques are compared: 1) a …

Accelerator-enhanced computing platforms have drawn a lot of attention due to their
massive peak commutational capacity. Despite significant advances in the programming …

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route
optimization in urban planning, benefiting navigation apps, transportation network …

Analysis of neural data with multiple modes and high density has recently become a trend
with the advances in neuroscience research and practices. There exists a pressing need for …

PEPPHER, a three-year European FP7 project, addresses efficient utilization of hybrid
(heterogeneous) computer systems consisting of multicore CPUs with GPU-type …

We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous
multicore and multi-GPU systems to support dense matrix computations efficiently. The main …

GPUs clusters are becoming widespread HPC platforms. Exploiting them is however
challenging, as this requires two separate paradigms (MPI and CUDA or OpenCL) and …

In this paper, we propose READYS, a reinforcement learning algorithm for the dynamic
scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to …