{"id":1203,"date":"2021-11-16T08:32:40","date_gmt":"2021-11-16T08:32:40","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/16\/universities-expand-research-horizons-with-nvidia-systems-networks\/"},"modified":"2021-11-16T08:32:40","modified_gmt":"2021-11-16T08:32:40","slug":"universities-expand-research-horizons-with-nvidia-systems-networks","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/16\/universities-expand-research-horizons-with-nvidia-systems-networks\/","title":{"rendered":"Universities Expand Research Horizons with NVIDIA Systems, Networks"},"content":{"rendered":"
\n

Just as the Dallas\/Fort Worth airport became a hub for travelers crisscrossing America, the north Texas region will be a gateway to AI if folks at Southern Methodist University have their way.<\/p>\n

SMU is installing an NVIDIA DGX SuperPOD<\/a>, an accelerated supercomputer it expects will power projects in machine learning for its sprawling metro community with more than 12,000 students and 2,400 faculty and staff.<\/p>\n

It\u2019s one of three universities in the south-central U.S. announcing plans to use NVIDIA technologies to shift research into high gear.<\/p>\n

Texas A&M and Mississippi State University are adopting NVIDIA Quantum-2<\/a>, our 400 Gbit\/second InfiniBand networking platform, as the backbone for their latest high-performance computers. In addition, a supercomputer in the U.K. has upgraded its InfiniBand network.<\/p>\n

Texas Lassos a SuperPOD<\/b><\/h2>\n

\u201cWe\u2019re the second university in America to get a DGX SuperPOD and that will put this community ahead in AI capabilities to fuel our degree programs and corporate partnerships,\u201d said Michael Hites, chief information officer of SMU, referring to a system<\/a> installed earlier this year at the University of Florida.<\/p>\n

A September report called the Dallas area \u201chobbled\u201d by a lack of major AI research. Ironically, the story hit the local newspaper just as SMU was buttoning up its plans for its DGX SuperPOD<\/a>.<\/p>\n

Previewing its initiative, an SMU report in March said AI is \u201cat the heart of digital transformation \u2026 and no sector of society will remain untouched\u201d by the technology. \u201cThe potential for dramatic improvements in K-12 education and workforce development is enormous and will contribute to the sustained economic growth of the region,\u201d it added.<\/p>\n

SMU Ignite<\/a>, a $1.5 billion fundraiser kicked off in September, will fuel the AI initiative, helping propel Southern Methodist into the top ranks of university research nationally. The university is hiring a chief innovation officer to help guide the effort.<\/p>\n

Crafting a Computational Crucible<\/b><\/h2>\n

It\u2019s all about the people, says Jason Warner, who manages the IT teams that support SMU\u2019s researchers. So, he hired a seminal group of data science specialists to staff a new center at SMU\u2019s Ford Hall for Research and Innovation, a hub Warner calls SMU\u2019s \u201ccomputational crucible.\u201d<\/p>\n

Eric Godat leads that team. He earned his Ph.D. in particle physics at SMU modeling nuclear structure using data from the Large Hadron Collider.<\/p>\n

Now he\u2019s helping fire up SMU\u2019s students about opportunities on the DGX SuperPOD. As a first step, he asked two SMU students to build a miniature model of a DGX SuperPOD using NVIDIA Jetson<\/a> modules.<\/p>\n

\u201cWe wanted to give people \u2014 especially those in nontechnical fields who haven\u2019t done AI \u2014 a sense of what\u2019s coming,\u201d Godat said.<\/p>\n

<\/p>\n

\"SMU's<\/p>\n

<\/a>

SMU undergrad Connor Ozenne helped build a miniature DGX SuperPOD that was featured in SMU\u2019s annual report. It uses 16 Jetson modules in a cluster students will benchmark as if it were a TOP500 system.<\/figcaption><\/figure>\n

The full-sized supercomputer, made up of 20 NVIDIA DGX A100 systems<\/a> on an NVIDIA Quantum InfiniBand network, could be up and running as early as January thanks to its Lego-like, modular architecture. It will deliver a whopping 100 petaflops of computing power, enough to give it a respectable slot on the TOP500 list of the world\u2019s fastest supercomputers.<\/p>\n

Aggies Tap NVIDIA Quantum-2 InfiniBand for ACES<\/b><\/h2>\n

About 200 miles south, the high performance computing center at Texas A&M will be among the first to plug into the NVIDIA Quantum-2 InfiniBand platform. Its ACES supercomputer, built by Dell Technologies, will use the 400G InfiniBand network to connect researchers to a mix of five accelerators from four vendors.<\/p>\n

NVIDIA Quantum-2 ensures \u201cthat a single job on ACES can scale up using all the computing cores and accelerators.\u00a0 Besides the obvious 2x jump in throughput from NVIDIA Quantum-1 InfiniBand at 200G, it will provide improved total cost of ownership, beefed up in-network computing features and increased scaling,\u201d said Honggao Liu, ACES\u2019s principal investigator and project director.<\/p>\n

Texas A&M already gives researchers access to accelerated computing<\/a> in four systems that include more than 600 NVIDIA A100 Tensor Core and prior-generation GPUs. Two of the four systems use an earlier version of NVIDIA\u2019s InfiniBand technology.<\/p>\n

MSU Rides a 400G Train<\/b><\/h2>\n

Mississippi State University will also tap the NVIDIA Quantum-2 InfiniBand platform. It\u2019s the network of choice for a new system that supplements Orion, the largest of four clusters MSU manages, all using earlier versions of InfiniBand.<\/p>\n

Both Orion and the new system are funded by the U.S. National Oceanic and Atmospheric Administration (NOAA) and built by Dell. They conduct work for NOAA\u2019s missions as well as research for MSU.<\/p>\n

Orion was listed as the fourth largest academic supercomputer in America when it debuted on the TOP500 list in June 2019.<\/p>\n

\u201cWe\u2019re using InfiniBand in four generations of supercomputers here at MSU so we know it\u2019s both powerful and mature to run our big jobs reliably,\u201d said Trey Breckenridge, director of high performance computing at MSU.<\/p>\n

\u201cWe\u2019re adding a new system with NVIDIA Quantum-2 to stay at the leading edge in HPC,\u201d he added.<\/p>\n

Quantum Nets Cover the UK<\/b><\/h2>\n

Across the pond in the U.K., the Data Intensive supercomputer at the University of Leicester, known as the DIaL system, has upgraded to NVIDIA Quantum, the 200G version of InfiniBand.<\/p>\n

\u201cDIaL is specifically designed to tackle the complex, data-intensive questions which must be answered to evolve our understanding of the universe around us,\u201d said Mark Wilkinson, professor of theoretical astrophysics at the University of Leicester and director of its HPC center.<\/p>\n

\u201cThe intense requirements of these specialist workloads rely on the unparalleled bandwidth and latency that only InfiniBand can provide to make the research possible,\u201d he said.<\/p>\n

DIaL is one of four supercomputers in the U.K.\u2019s DiRAC facility using InfiniBand, including the Tursa system at the University of Edinburgh.<\/p>\n

InfiniBand Shines in Evaluation<\/strong><\/h2>\n

In a technical evaluation, researchers found Tursa with NVIDIA GPU accelerators on a Quantum network delivered 5x the performance of their CPU-only Tesseract system using an alternative interconnect.<\/p>\n

Application benchmarks show 16 nodes of Tursa have twice the performance of 512 nodes of Tesseract. Tursa delivers 10 teraflops\/node using 90 percent of the network\u2019s bandwidth at a significant improvement in performance per kilowatt over Tesseract.<\/p>\n

It\u2019s another example of why most of the world\u2019s TOP500 systems<\/a> are using NVIDIA technologies.<\/p>\n

For more, watch our special address<\/a> at SC21 either live on Monday, Nov. 15 at 3 pm PST or later on demand. NVIDIA\u2019s Marc Hamilton will provide an overview of our latest news, innovations and technologies, followed by a live Q&A panel with NVIDIA experts.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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