Cerebras Proposes AI Megacluster with Billions of AI Compute Cores

By Agam Shah

September 14, 2022

Chipmaker Cerebras is patching its chips – already considered the world’s largest – to create what could be the largest-ever computing cluster for AI computing.

A reasonably sized “wafer-scale cluster,” as Cerebras calls it, can network together 16 CS-2s into a cluster to create a computing system with 13.6 million cores for natural language processing. But wait, the cluster can be even larger.

“We can connect up to 192 CS-2s into a cluster,” Andrew Feldman, CEO of Cerebras, told HPCwire.

The AI chipmaker made its announcement at the AI Hardware Summit, where the company is presenting a paper on the technology behind patching together a megacluster. The company initially previewed the technology at last year’s Hot Chips, but expanded on the idea at this week’s show.

Cerebras has claimed that a single CS-2 system – which has one wafer-sized chip with 850,000 cores – had trained an AI natural language processing model with 20 billion parameters, which is the largest ever trained on a single chip. Cerebras’ goal is to train larger models, and in less time.

Weight streaming – disaggregating memory and compute with MemoryX (Cerebras graphic)

“We have run the largest NLP networks on clusters of CS-2s. We have seen linear performance as we add CS-2s. That means that as you go from one to two CS-2s the training time is cut in half,” Feldman said.

Larger natural-language processing models help in more accurate training. The largest models currently have more than a billion parameters, but are growing even larger. Researchers at Google have proposed new NLP model with 540 billion parameters and neural models that can scale up to 1 trillion parameters.

Each CS-2 system can support models with more than 1 trillion parameters, and Cerebras previously told HPCwire that CS-2 systems can handle models with up to 100 trillion parameters. A cluster of CS-2 such systems can be paired up to train larger AI models.

Cerebras has introduced a fabric called SwarmX that will connect CS-2 systems in the cluster. The execution model relies on a technology called “weight streaming,” which disaggregates the memory, compute and networking into separate clusters, which makes the communications straightforward.

AI computing depends on the model size and training speed, and the disaggregation allows users to size up the computing requirements to the problems they are looking to solve. In each CS-2 system, the model parameters are stored in an internal system called MemoryX, which is more of a memory element in the system. The computing being done on the 850,000 computing cores 

“The weight streaming execution model disaggregates compute and parameter storage. This allows computing and memory to scale separately and independently,” Feldman said.

Scaling via SwarmX

The SwarmX interconnect is a separate system that glues together the massive cluster of CS-2 systems. SwarmX operates at a cluster level, which is almost similar to the MemoryX operates at the single CS-2 system – it decouples the memory and computing elements in the cluster, and is able to scale up the number of computing cores available to solve larger problems.

“SwarmX connects MemoryX to clusters of CS-2s. Together the clusters are dead simple to configure and operate, and they produce linear performance scaling,” Feldman said.

The SwarmX technology takes the parameters stored in MemoryX and broadcasts it across the SwarmX fabric to multiple CS-2s. The parameters are replicated across the MemoryX systems in the cluster.

The cross SwarmX fabric uses multiple lanes of 100GbE as transport, and on-chip Swarm fabric is based on in-silicon wires, Feldman said.

Cerebras is targeting the CS-2 cluster system at NLP models with more than 1 billion parameters, even though one CS-2 system is enough to solve a problem. But Cerebras states that moving from one CS-2 to two CS-2s in a cluster cuts the training time in half and so forth.

“Together the clusters … produce linear performance scaling,” Feldman said, adding, “a cluster of 16 or 32 CS-2 could train a trillion-parameter model in less time than today’s GPU clusters train 80 billion parameter models.

Buying two CS-2 systems could put customers back by millions of dollars, but Cerebras in the presentation argued that such systems are cheaper than the GPU model in clusters, which can’t scale up as effectively and draws more energy. 

Cerebras argued that GPU cores need to operate identically across thousands of cores to get a coordinated response time. Calculations also need to be distributed among a complex network of cores, which can be time consuming and inefficient in power consumption.

By comparison, SwarmX divides data sets into parts for training purposes, and creates a scalable broadcast which distributes the weights among the CS-2 systems in a cluster, which sends back the gradients to the coordinated MemoryX cache systems across the cluster.

Switching over a training an NLP model from one CS-1 system to a cluster requires just changing the number of systems in a Python script.

“Large language models like GPT-3 can be spread over a cluster of CS-2s with a single keystroke. That’s how easy it is to do it,” Feldman said.

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!

Quantum Watchers – Terrific Interview with Caltech’s John Preskill by CERN

July 17, 2024

In case you missed it, there's a fascinating interview with John Preskill, the prominent Caltech physicist and pioneering quantum computing researcher that was recently posted by CERN’s department of experimental physi Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to communities across the globe. As climate change is warming ocea Read more…

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical claims. A paper published on July 10 by researchers in the U. Read more…

Belt-Tightening in Store for Most Federal FY25 Science Budets

July 15, 2024

If it’s summer, it’s federal budgeting time, not to mention an election year as well. There’s an excellent summary of the curent state of FY25 efforts reported in AIP’s policy FYI: Science Policy News. Belt-tight Read more…

Peter Shor Wins IEEE 2025 Shannon Award

July 15, 2024

Peter Shor, the MIT mathematician whose ‘Shor’s algorithm’ sent shivers of fear through the encryption community and helped galvanize ongoing efforts to build quantum computers, has been named the 2025 winner of th Read more…

Weekly Wire Roundup: July 8-July 12, 2024

July 12, 2024

HPC news can get pretty sleepy in June and July, but this week saw a bump in activity midweek as Americans realized they still had work to do after the previous holiday weekend. The world outside the United States also s Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to com Read more…

Shutterstock 1886124835

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical c Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Point and Click HPC: High-Performance Desktops

July 3, 2024

Recently, an interesting paper appeared on Arvix called Use Cases for High-Performance Research Desktops. To be clear, the term desktop in this context does not Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then 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…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Leading Solution Providers

Contributors

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l 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…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. 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…

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…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire