Open Source Linux Statistics Software

Mapgraph is SYSTAP’s disruptive new technology to exploit the main memory bandwidth advantages of GPUs. The early work was co-developed with the University of Utah SCI Institute and has its pedigree in the UINTAH software running on over 750M cores on the TITAN Super Computer. Today, SYSTAP has commercialized this technology into it’s Blazegraph Accelerator and Blazegraph HPC products. Checkout our options for GPU acceleration of graphs or contact us to learn more: https://www.blazegraph.com/product/gpu-accelerated/. The early work was released under the Apache 2 open source license and is available on here at Sourceforge. This work was (partially) funded by the DARPA XDATA program under AFRL Contract #FA8750-13-C-0002 and DARPA Contract #D14PC00029.

An application built onto the Open Table Explorer engine for the acquisition and analysis of home energy consumption and solar energy production.Moved to github as Open Table Explorer

Hrothgar is a parallel minimizer and Markov Chain Monte Carlo generator by Andisheh Mahdavi of San Francisco State University. It has been used to solve optimization problems in astrophysics (galaxy cluster mass profiles) as well as in experimental particle physics (hadronic tau decays). It is probably adaptable enough to be applied to your merit function if you can write it in C.

An easy, small and handy math calculator for Unix/Linux systems. It can calculate easy and complex mathematical expressions passed as command line arguments.

R packages supporting parallel computing.

Predict if storage utilised will reach capacity within defined period (maximum 5 years), using recent historical data contained within RRD files (maximum -1 year). Produces HTML (or partial HTML) and, more importanly, a PNG RRD Graph showing the prediction and, should the capacity be reached, an annotation indicating when. Optionally email out exception notices.

Welcome to the Volunteer-Based System for Research on the Internet! This system is designed to provide detail data about applications used in the Internet. This information can be used for: - obtaining the knowledge which applications are most frequently used in the network - providing the users some basic statistics about their Internet connection usage (for example for which kinds of applications their connection is used the most) - creating scientific profiles of traffic generated by different applications or different groups of applications - creating a traffic generator, to imitate traffic generated by particular applications, or to imitate the real traffic in the network - implementing smart assessment of QoS in the network at the users' level and in the core of the network - obtaining precise data needed to train Machine Learning Algorithms - many more cases :-)