27 Mar 2018 London - Recently having eliminated upgrade offerings that were cost-prohibitive, migration-intensive, lacked support or large installed bases, Queen Mary University of London (QMUL) selected Univa Grid Engine for its rich features, high performance, large installed base - including universities, expert support, and easiest upgrade path.
QMUL is globally recognized for pushing the boundaries of research and Innovation. Queen Mary's high-performance computing cluster supports a student and research community of over 2000 users in all disciplines, such as Astronomy, Computational Chemistry, Bioinformatics, Computer Science & Machine Learning, Engineering, Mathematics and Statistics, and Clinical Research. The HPC cluster comprises 5000 InfiniBand-interconnected cores and 2PB high-performance storage running hundreds of commercial and open-source applications of various types, such as Gaussian, MATLAB, Ansys, Stata, genomics applications, plus Tensorflow for GPUs in singularity containers.
QMUL's aged job scheduler was running subpar and impacting users who could not run their preferred software like Tensorflow on NVIDIA Tesla K80 GPUs.
The migration to Univa was "painless" and was performed as a phased approach during the university's concurrent HPC upgrade to RedHat Centos7, the installation of new nodes, and a major storage upgrade. Consistently high resource usage was realized immediately conservatively estimated as equaling the cost of at least a couple of nodes.
"We have great confidence in the stability and performance of Univa Grid Engine", stated Simon Butcher, Head of Research Applications, Queen Mary University of London. "We are now exploring Univa's Navops Launch for hybrid Cloud-bursting, which will further extend our HPC cluster well into the future."
Univa made a full case study about this recent QMUL migration to Univa Grid Engine.