Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

By Tiffany Trader

June 9, 2020

Pittsburgh Supercomputing Center (PSC – a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award from the National Science Foundation to build an AI supercomputer designed to accelerate AI research in pursuit of science, discovery and societal good. The new machine, called Neocortex, couples two Cerebras CS-1 AI servers with a shared-memory HPE Superdome Flex server. PSC will make Neocortex available to researchers across the Extreme Science and Engineering Discovery Environment (XSEDE) later this year.

Each Cerebras CS-1 is powered by one Cerebras Wafer Scale Engine (WSE) processor, which contains 400,000 AI-optimized cores implemented on a 46,225 square millimeter wafer with 1.2 trillion transistors. A front-end HPE Superdome Flex server will handle pre- and post-processing of data flowing in and out of the WSE processors. The HPE Superdome Flex is provisioned with 32 Intel Xeon CPUs, 24 terabytes of memory, 205 terabytes of flash storage, and 24 network interface cards.

The Superdome Flex connects to each CS-1 server via 12 100 gigabit Ethernet links, providing 1.2 terabits per second of bandwidth between the machines. That’s enough bandwidth to transfer 37 HD movies every second, said Nick Nystrom, chief scientist, Pittsburgh Supercomputing Center. The Neocortex team is considering implementing the network on a single switch to explore allowing the two CS-1s to interface directly at 1.2 terabits per second.

The WSE processor inside the CS-1 provides 9 petabytes per second of on-die memory bandwidth, equivalent to about a million HD movies per second, by Nystrom’s math.

Neocortex (named after the region of the brain responsible for higher-order brain functions, including language processing) is the first CS-1 installation funded by the NSF and the first publicly announced CS-1 cluster. Cerebras debuted its Wafer Scale Engine last August at Hot Chips and the CS-1 system unveiling followed at SC19 in November. The Department of Energy was the flagship customer; single-node CS-1 systems are deployed at Argonne National Lab and Lawrence Livermore National Lab.

Cerebras CS-1 system

Describing the impetus for the technology partnering, Nystrom said that PSC saw the opportunity to bring together the best of two worlds – “the extreme deep learning capability of the server CS-1, and the extreme shared memory of the Superdome Flex with HPE.”

“With shared memory, you don’t have to break your problem across many nodes. You don’t have to write MPI, and you don’t have to distribute your data structures. It’s just all there at high speed,” he added.

Both Cerebras and PSC expressed their expectation that the system will be able to take on a new class of problems, beyond what is available with traditional GPUs.

“We’re just scratching the surface of sort of a new class of AI models; we know of additional models that have been difficult to get running on graphics processing units and we are extremely eager to be partnering with pioneering researchers to show the world what these models might be able to do,” said Andrew Feldman, Cerebras cofounder and CEO. His list of target examples includes models with separable convolutions or models with native and induced sparsity, both coarse and fine grained, graph neural networks with irregular sparse connections, complex sequential models, and very large models where parallelism is desirable.

Even with current best-in-class PSC machines, like the GPU-based Bridges and Bridges-AI, research is constrained, said Paola Buitrago, principal investigator and PSC director of artificial intelligence and big data, noting “there is clearly a need for more compute, and fast interconnect and storage.”

“Artificial intelligence in 2012 started this kind of renaissance, thanks to neural networks being implemented on GPUs,” Buitrago shared in an interview with HPCwire. “GPUs absolutely do well with matrix operations, which is one of the main operations in our neural networks, but they weren’t designed for AI. Now with the Cerebras technology, we see a machine that is specifically designed for AI and for the potential optimizations in deep learning. We are excited to explore how it can speed up and transform what is currently happening in deep learning, allowing us to explore more and more ambitious science and reducing the time to curiosity.”

Buitrago expects Neocortex to be more powerful than the PSC Bridges-AI system by a few orders of magnitude. Providing further characterization of the system’s potential, Cerebras’ Feldman said the tuned system cluster with Cerebras’ wafer-scale cores and “the pre-processing machine from HPE” will have the power of 800-1,500 traditional GPUs, or “or about 20 racks worth of graphics processing with a single rack of Cerebras.”

Naturally, PSC will be putting Neocortex through its paces to see if this claim bears out. The Neocortex group at PSC has identified a number of benchmarks as being important to the community. “These were selected to demonstrate the capability of the system when it hits the ground, and the system will, of course, continue to mature over time,” said Nystrom, adding they will be evaluating the system with all the big complex networks that are very challenging right now, including LSTM.

“In addition to LSTM, we expect Neocortex will be very good at graph convolutional networks, important in all kinds of science,” said Nystrom. “And then over time across CNNs. So we’ll be using those initially, and we’ll be engaging early users to demonstrate scientific impact. That’s very important to the National Science Foundation.”

Buitrago said that their users who are bounded by current hardware are “in large part working on natural language processing and working with transformer type networks, including BERT and Megatron-LM, where the models are quite big with hundreds of millions and billions of parameters,” adding, “that’s a specific use case that we will be enabling with the Neocortex system.”

HPE Superdome Flex

The number of applications that need AI is growing, encompassing virtually all fields of science, many drawing on computer vision, text processing, and natural language processing. “We want to explore use cases that come specifically from science streaming needs,” said Buitrago. “So we are working with cosmology researchers, people doing image analysis for healthcare where they need to [handle] the high resolution images and also images in more than two dimensions and seeing how to address what are the best solutions for those specific use cases.”

The project partners are particularly enthused about harnessing AI for social good. Drug discovery, more accurate weather prediction, improved materials for increased solar energy generation and understanding large plant genomes to boost crop yields are just a few of the areas PSC expects will benefit from Neocortex as well as the upcoming Bridges-2 system (see slide below right for system details).

Details about Bridges-2 were presented (virtually) by Nick Nystrom at the HPC-AI Advisory Council Stanford Conference in April

Both Neocortex and Bridges-2 — also built with HPE — will be deployed in the fall. “We’re launching two supercomputers in the same season,” Nystrom declared. “PSC has never done that before.”

As with Bridges-2, 90 percent of time on Neocortex will be allocated through XSEDE. “We’ll have a long early user period, but there’s also discretionary capacity for industry to work with us too, to use the world’s most advanced AI capability to develop their capacity for industrial competitiveness and for translational research,” said Nystrom.

There’s also a concerted focus, via the NSF-funded OpenCompass program, to collect and document best practices for running artificial intelligence at scale and communicate those to the open science community. This dovetails with a mission of PSC to support non-traditional users (from history, philosophy, etc.) and users who are just getting started with AI.

Neocortex will support the most popular deep learning frameworks and will be federated with PSC’s new Bridges-2 supercomputer, creating “a singularly powerful and flexible ecosystem for high performance AI, data analytics, modeling and simulation.”

Both Neocortex and Bridges-2 will be available at no cost for research and education, and at cost-recovery rates for industry users.

PSC will present a tutorial on AI hardware at PEARC (July 26-30) and will be talking more about the Neocortex system and what to expect. More details will be forthcoming at https://pearc.acm.org/pearc20/.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Intel’s Silicon Brain System a Blueprint for Future AI Computing Architectures

April 24, 2024

Intel is releasing a whole arsenal of AI chips and systems hoping something will stick in the market. Its latest entry is a neuromorphic system called Hala Point. The system includes Intel's research chip called Loihi 2, Read more…

Anders Dam Jensen on HPC Sovereignty, Sustainability, and JU Progress

April 23, 2024

The recent 2024 EuroHPC Summit meeting took place in Antwerp, with attendance substantially up since 2023 to 750 participants. HPCwire asked Intersect360 Research senior analyst Steve Conway, who closely tracks HPC, AI, Read more…

AI Saves the Planet this Earth Day

April 22, 2024

Earth Day was originally conceived as a day of reflection. Our planet’s life-sustaining properties are unlike any other celestial body that we’ve observed, and this day of contemplation is meant to provide all of us Read more…

Intel Announces Hala Point – World’s Largest Neuromorphic System for Sustainable AI

April 22, 2024

As we find ourselves on the brink of a technological revolution, the need for efficient and sustainable computing solutions has never been more critical.  A computer system that can mimic the way humans process and s Read more…

Empowering High-Performance Computing for Artificial Intelligence

April 19, 2024

Artificial intelligence (AI) presents some of the most challenging demands in information technology, especially concerning computing power and data movement. As a result of these challenges, high-performance computing Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that have occurred about once a decade. With this in mind, the ISC Read more…

Intel’s Silicon Brain System a Blueprint for Future AI Computing Architectures

April 24, 2024

Intel is releasing a whole arsenal of AI chips and systems hoping something will stick in the market. Its latest entry is a neuromorphic system called Hala Poin Read more…

Anders Dam Jensen on HPC Sovereignty, Sustainability, and JU Progress

April 23, 2024

The recent 2024 EuroHPC Summit meeting took place in Antwerp, with attendance substantially up since 2023 to 750 participants. HPCwire asked Intersect360 Resear Read more…

AI Saves the Planet this Earth Day

April 22, 2024

Earth Day was originally conceived as a day of reflection. Our planet’s life-sustaining properties are unlike any other celestial body that we’ve observed, Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that ha Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use o Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pre Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Leading Solution Providers

Contributors

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... Read more…

Intel’s Xeon General Manager Talks about Server Chips 

January 2, 2024

Intel is talking data-center growth and is done digging graves for its dead enterprise products, including GPUs, storage, and networking products, which fell to Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire