Queen Mary University of London (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 2,000 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 5,000 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.
QUML’s aged job scheduler was running subpar and impacting users who could not run their preferred software like Tensorflow on nVidia Tesla K80 GPUs. Recently having eliminated upgrade offerings that were cost-prohibitive, migration-intensive, lacked support or large installed bases, QMUL selected Univa Grid Engine for its rich features, high performance, large installed base (including universities), expert support, and easiest upgrade path.
“We have great confidence in the stability and performance of Univa Grid Engine,” says 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.”
You can read the full case study here.