The automatic character recognition of historic documents gained more attention from scholars recently, due to the big improvements in computer vision, image processing, and digitization. While Neural Networks, current state-of-the-art models used for recognition, are very performant, they typically suffer using large amounts training data. In our study we manually built own relatively small da...

This paper describes the ARGUS prototype, a high-density, low-power supercomputer built from an IXIA network analyzer chassis and load modules. The prototype is configured as a diskless distributed system that is scalable to 128 processors in a single 9U chassis. The entire system has a footprint of 0.25 m2 (2.5 ft2), a volume of 0.09 m3 (3.3 ft3) and maximum power consumption of less than 2200...

The advent of affordable parallel computers such as Beowulf PC clusters and, more recently, of multi-core PCs has been highly beneficial for a large number of scientists and smaller institutions that might not otherwise have access to substantial computing facilities. However, there has not been an analogous progress in the development and dissemination of parallel software: scientists need the...

A lot of attention has been paid to so-called Beowulf/Avalon clusters, where PCs or Alphas are strung together with 100Mbit/s Ethernet and portable programs from supercomputers have been run on these, particularly when modest bandwidth and latency requirements are posed by the example applications. In addition, heroic efforts to scale clusters using early gigabit/s scalable fabrics has been don...

We implemented four basic cellular automata (CA) algorithms on a Beowulf cluster with 8 processors. The CA algorithms are, namely, (1) Game of Life, (2) GreenburgHasting, (3) Cyclic Space, and (4) Hodgepodge Machine.

In the last decade, the use of the Beowulf Cluster concept for High Performance Computing and Cloud Computing has exploded. In addition, the Linux operating system used by many of these computing platforms has also greatly advanced. Understanding the effects of the underlying kernel scheduler on the computational performance of compute nodes is one of the main concerns in customizing a Linux op...

Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. Most available techniques for hyperspectral image classification focus on analyzing the data without incorporating the spatial information; i.e. the data is treated not as an image but as an unordered listing o...