Performance
Intensive
Computing

Running heavy AI/ML workloads can be a challenge for any server, but the SuperBlade has extremely fast networking options, upgradability, the ability to run two AMD EPYC™ 7000-series 64-core processors and the Horovod open-source framework for scaling deep-learning training across multiple GPUs.

The AMD Threadripper™ CPU may be a desktop processor, but desktop computing was never like this. The new chipset comes in a variety of multi-core versions, with a maximum of 64 cores running up to 128 threads, 256MB of L3 cache and 2TB of DDR 8-channel memory. The newest Threadrippers are built with AMD’s latest 7 nanometer dies.  

The Supermicro SuperBlade's advantage for the Red Hat OCP environment is that it supports a higher-density infrastructure and lower-latency network configuration, along with benefits from reduced cabling, power and shared cooling features. SuperBlades feature multiple AMD EPYC™ processors using fast DDR4 3200MHz memory modules.

“AMD EPYC™ processors are now a part of the world’s hyperscale data centers,” said Lisa Su, AMD’s CEO. Meta/Facebook is now building its servers with powerful third-generation AMD EPYC™ CPUs.

The Budapest Institute for Computer Science and Control (known as SZTAKI) 

Lodestar is a complete management suite for developing artificial intelligence-based computer vision models from video data. It can handle the navigation and curation of a native video stream without any preparation. Lodestar annotates and labels video, and using artificial intelligence, creates searchable, structured data.

A white paper from IDC projects a new role for IT leaders in preparing the infrastructure required to properly power performance-intensive computing (PIC) for enterprise workloads, such as data-driven insights, AI/machine learning, big data, modeling and simulation and more. Get the full white paper to learn best practices and avoid pitfalls when implementing performance-intensive computing infrastructure.

CERN is trying to discover what happened in the nanoseconds following the Big Bang that created all matter. It is manipulating data flows with custom AMD circuitry that slices up the Large Hadron Collider data into smaller pieces. “You need to get all the data pieces together in a single location because only then can you do a meaningful calculation on this stuff,” said Niko Neufeld, a CERN project leader. The effort entails rapid data processing, high-bandwidth access to lots of memory and very speedy I/O among many servers.