High Performance Computing

Feb 06, 2017 at 4:34 pm


The WFU DEAC Cluster provides the critical infrastructure necessary for researchers to reliably upload research codes, perform large scale computations, store their actively utilized results, and have confidence in the persistence of their data in the event of storage failures.

Clusters provide enormous computational power to tackle more advanced scientific problems, opening up the possibility for our students to gain a unique perspective on topics in the forefront of research today.

Any Wake Forest University or Wake Forest University School of Medicine faculty member is strongly encouraged to request the use of the cluster for a course they are teaching. Several courses have already used the WFU DEAC cluster to enhance the student’s exposure to the advanced computing environment provided by the University.

Current Projects

iPhone HPC App

The iPhone HPC app will help the DEAC cluster users manage and view their submitted computational jobs. The flow of the app is for the users to use their credentials to the cluster and see the list of jobs they currently have running and/or waiting from our job and resource manager SLURM. The users will then be able to perform basic job management actions such as canceling or view detail of jobs as the basic goal of the app. The project is headed by Prof. Sam Cho and Prof. Daniel Canas from the Computer Science Department, and the work will be developed by a CS graduate student.

Software Engineering Course Project

A group of five students from the Computer Science department will be developing HTML and javascript applications for the DEAC cluster’s website that will report the health of the cluster and chassis, number of jobs and utilization numbers of the environment every hour as a class project. The goal of the project is to visually represent these various reports by querying our job and resource manager SLURM and integrate the reports into our website. The Software Engineering course is a graduate-senior level course in the CS department taught by Prof. Victor Pauca.

Performance Modeling of the DEAC Cluster

Our former summer-intern and Computer Science graduate student Riana Freedman is currently employing five distinct benchmarks on the DEAC cluster that provide meaningful metrics of processors, memory, I/O, network and GPU performance. The goal of this project is to model the performance behavior of the different hardware components utilizing algorithm numerical methods. This work is part of her Independent Studies course with Dr. Damian Valles from the IS department and Intro to Numerical Methods course with Prof. Grey Ballard from the Computer Science department.

For more information about HPC, visit the High Performance Computing page. 

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