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|Pavilion to Provide NVMe-Over-Fabrics Storage for SCinet’19 High Performance Computing (HPC) Simulations|
|Posted: Tue Nov 19, 2019 08:36:46 AM|
The Pavilion Hyperparallel Flash Array to be shown at Supercomputing 2019
November 19, 2019 –Denver, Colo.– Pavilion Data Systems, the leader in NVMe-Over-Fabrics, today announced that the company will be part of a project called SCinet’19 to provide the high-performance storage infrastructure with NVMe and NVMe-Over-Fabrics (NVMeoF) of leading HPC organizations across the globe. Spearheaded by Caltech Professor Harvey Newman, SCinet’19 showcases how people and organizations gain insights and knowledge in gene sequencing, social and economic analysis, and other big data applications. The work will be shown at Supercomputing 2019 (SC’19), taking place in Denver, Colorado, November 17-21, 2019. Find out more about the SCinet’19 project in this blog post blog post.
Pavilion’s Hyperparallel Flash Array (HFA) was selected by Professor Newman and team to meet the extreme IOPs and bandwidth requirements of NVMe-oF for SCinet’19.
“As we searched for a storage system that could accommodate hundreds of gigabytes per second of throughput at microsecond latencies that could accommodate our space and power requirements, Pavilion’s HFA was an ideal fit,” said Harvey B. Newman, Marvin L. Goldberger Professor of Physics at Caltech.
“We are looking forward to our work with Caltech and the broader HPC community to advance networking and storage technologies,” added VR Satish, CTO of Pavilion. “Some of the most sophisticated artificial intelligence, machine learning and deep learning projects take place through collaboration at events such as Supercomputing 2019 and with platforms like SCinet’19. We are pleased to be selected by Caltech as their storage platform for this event as we push the envelope of performance, density and low-latency.”
SCinet’19 features the latest advances in Terabit/sec networks, intelligent global operations and monitoring systems, workflow optimization methodologies with real-time analytics, and state of the art long-distance data transfer methods, with tools and storage designs to meet the challenges faced by leading-edge programs in high energy physics, astrophysics, climate and other fields of data-intensive science.