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Making AI Accessible to Any Size Enterprise

In this sponsored post, our friends over at Lenovo and NetApp have teamed up with NVIDIA to discuss how the companies are helping to drive Artificial Intelligence (AI) into smaller organizations and hopefully seed that creative garden. Experience tells us that there is a relationship between organizational size and technology adoption:  Larger, more resource-rich, enterprises generally adopt new technologies first, while smaller, more resource constrained organizations follow afterward, (provided that the small organization isn’t in the technology business). 

At SC20: Intel Provides Aurora Update as Argonne Developers Use Intel Xe-HP GPUs in Lieu of ‘Ponte Vecchio’

In an update to yesterday’s “Bridge to ‘Ponte Vecchio'” story, today we interviewed, Jeff McVeigh, Intel VP/GM of data center XPU products and solutions, who updated us on developments at Intel with direct bearing on Aurora, including the projected delivery of Ponte Vecchio (unchanged); on Aurora’s deployment (sooner than forecast yesterday by industry analyst firm Hyperion Research); on Intel’s “XPU” cross-architecture strategy and its impact on Aurora application development work ongoing at Argonne; and on the upcoming release of the first production version of oneAPI (next month), Intel’s cross-architecture programming model for CPUs, GPUs, FPGAs and other accelerators.

At SC20: Fugaku Extends HPC Top500 Lead, Benchmarks 2 ExaFLOPS on HPC-AI; Summit, Sierra Nos. 2 and 3; China at Nos. 4 and 6

The Arm-based Fugaku supercomputer, residing at the Riken Center for Computational Science in Japan, solidified the lead it seized in June atop the Top500 list of the world’s most powerful supercomputers. Announced today at virtual SC20, the 56th edition of the bi-annual list reflects a “flattening performance growth curve,” according to the organizations, which said […]

AI Workflow Scalability through Expansion

In this special guest feature, Tim Miller, Braden Cooper, Product Marketing Manager at One Stop Systems (OSS), suggests that for AI inferencing platforms, the data must be processed in real time to make the split-second decisions that are required to maximize effectiveness.  Without compromising the size of the data set, the best way to scale the model training speed is to add modular data processing nodes.

AWS Launches Nvidia GPU-Driven EC2 P4d Instances for AI, HPC

Amazon Web Services today announced the general availability of Amazon EC2 P4d Instances powered by Nvidia GPUs with EC2 UltraClusters capability delivering 3x faster performance, up to 60 percent lower cost, and 2.5x more GPU memory for machine learning training and HPC workloads compared to previous-generation P3 instances, according to AWS. The company said P4d […]

Zero to an ExaFLOP in Under a Second

In this sponsored post, Matthew Ziegler at Lenovo discusses today’s metric for raw speed of compute. Much like racing cars, servers do “time trials” to gauge their performance relative to a given workload.  There are more Spec or Web benchmarks out there for servers than there are racetracks and drag strips.  Perhaps the most important measure is the raw calculating throughput that a system delivers: FLOPS, or Floating-Point Operations Per Second. 

Choosing the Best Data Flow Design for GPU Accelerated Applications

In this sponsored article from our friends over at Supermicro, we discusses how deciding on the correct type of GPU accelerated computation hardware depends on many factors. One particularly important aspect is the data flow patterns across the PCIe bus and between GPUs and Intel® Xeon® Scalable processors. These factors, along with some application insights are explored below.

The Hyperion-insideHPC Interviews: NERSC’s Jeff Broughton on the End of the Top500 and Exascale Begetting Petaflops in a Rack

NERSC’s Jeff Broughton career extends back to HPC ancient times (1979) when, fresh out of college, he was promoted to a project management role at Lawrence Livermore National Laboratory – a big job for a young man. Broughton has taken on big jobs in the ensuing 40 years. In this interview, he talks about such […]

Transform Your Business with the Next Generation of Accelerated Computing

In this white paper, you’ll find a compelling discussion regarding how Supermicro servers optimized for NVIDIA A100 GPUs are solving the world’s greatest HPC and AI challenges. As the expansion of HPC and AI poses mounting challenges to IT environments, Supermicro and NVIDIA are equipping organizations for success, with world-class solutions to empower business transformation. The Supermicro team is continually testing and validating advanced hardware featuring optimized software components to support a rising number of use cases.

Getting to Exascale: Nothing Is Easy

In the weeks leading to today’s Exascale Day observance, we set ourselves the task of asking supercomputing experts about the unique challenges, the particularly vexing problems, of building a computer capable of 10,000,000,000,000,000,000 calculations per second. Readers of this publication might guess, given Intel’s trouble producing the 7nm “Ponte Vecchio” GPU for its delayed Aurora system for Argonne National Laboratory, that compute is the toughest exascale nut to crack. But according to the people we interviewed, the difficulties of engineering exascale-class supercomputing run the systems gamut. As we listened to exascale’s daunting litany of technology difficulties….