The integration of graphics processing units (GPUs) on high-end compute nodes has
established a new accelerator-based heterogeneous computing model, which now …

In this paper we detail the key features, architectural design, and implementation of rCUDA,
an advanced framework to enable remote and transparent GPGPU acceleration in HPC …

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud
workloads, necessitates re-visiting the traditional rack-mountable servers based datacenter …

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; …

Field-programmable gate arrays (FPGAs) can offer invaluable computational performance
for many compute-intensive algorithms. However, to justify their purchase and administration …

Over the last decade, CUDA and the underlying GPU hardware architecture have
continuously gained popularity in various high-performance computing application domains …

Emerging WLAN standards have been incorporating a variety of channel widths ranging
from 5MHz to 160MHz, in order to match the diverse traffic demands on different networks …

SLURM is a resource manager that can be leveraged to share a collection of heterogeneous
resources among the jobs in execution in a cluster. However, SLURM is not designed to …

The increasing demand for machine learning computation contributes to the convergence of
high-performance computing and cloud computing, in which the virtualization of Graphics …

The use of hardware accelerators to increase the performance of parallel applications is
very common nowadays. For a number of reasons, however, the access to local accelerators …