A digital twin for intense weather gives scientists a ‘loop’

Computer manufacturer Cerebras used its AI computer on a non-AI problem: simulating “buoyancy-driven Navier-Stokes flows” that capture the dynamics of many systems in nature and the built environment. The work, the first of its kind, enables a “digital twin” of the real world, allowing scientists to make predictions and see the effects of interventions in a kind of closed loop.

Cerebras/DoE NETL 2023

Simulating the real world in real time can offer scientists a way to make predictions based on running through scenarios as they unfold. This could be an advantage when dealing with extreme weather scenarios such as those associated with global warming.

AI computing pioneer Cerebras and the US Department of Energy’s National Energy Technology Laboratory on Tuesday announced an acceleration of scientific equations that they say can enable real-time simulation of extreme weather conditions.

“This is a real-time simulation of how liquids of different volumes behave in a dynamic environment,” said Andrew Feldman, CEO of Cerebras.

“In real time or faster, you can predict the future,” Feldman said. “From a starting point, the actual phenomenon unfolds more slowly than your simulation, and you can go back and make adjustments.”

This type of simulation, a digital twin of real-world conditions, essentially allows for a “closed loop” that allows you to manipulate reality, Feldman said.

In prepared remarks, Dr. Brian J. Anderson, Laboratory Manager at NETL: “We are excited about the potential of this real-time natural convection simulation as we can really dramatically speed up and improve the design process for some big projects that are essential for mitigating climate change and enabling a secure energy future of are critical – projects like carbon sequestration and blue hydrogen production.”

Anderson added, “On a traditional supercomputer, this workload is hundreds of times slower, eliminating the possibility of real-time rates or extremely high-resolution flows.”

Also: “We can solve this problem at a time that no number of GPUs or CPUs can match,” says startup Cerebras at the supercomputing conference

In a video created by the researchers, streams of hot and cold liquids flow up and down like an alien landscape.

Cerebras has made a name for itself with exotic hardware and software for large artificial intelligence training programs. However, the company has expanded its repertoire by focusing on challenging fundamental research problems that are computationally intensive and potentially unrelated to AI.

In the field of Computational Fluid Dynamics, the Cerebras machine, dubbed CS-2, is able to simulate what is known as Rayleigh-Bénard convection, a phenomenon that occurs when a liquid is heated from below and cooled from above.

The work was made possible by running a new software package developed by Cerebras and NETL last fall called the WSE Field Equations API, a Python-based front end that describes field equations. Field equations are a type of differential equations that “describe almost every physical phenomenon in nature at the finest space-time scales,” according to the GitHub documentation.

Basically, field equations model everything in the known universe except quantum entanglement.

The API, described in the November article “Disruptive Changes in Field Equation Modeling: A Simple Interface for Wafer Scale Engines” published on arXiv, was explicitly designed to take advantage of the Cerebras computer’s special AI chip. Dubbed the Wafer Scale Engine or WSE, the chip was unveiled in 2019 and is the world’s largest computer chip, nearly the size of an entire semiconductor wafer.

The November paper described the WSE as being able to perform field equations two orders of magnitude faster than NETL’s Joule 2.0 supercomputer, built by Hewlett Packard Enterprise with 86,400 Intel Xeon processor cores and 200 GPU chips from Nvidia.

Because of their highly distributed nature, supercomputers are valued for their ability to execute components of equations concurrently to speed up overall computation time. However, the NETL scientists found that running field equations encountered bandwidth and latency limitations when moving data from off-chip memory to the processor and GPU cores.

Also: AI startup Cerebras has celebrated chip triumphs where others have tried and failed

The field equations API instead leveraged the capability of Cerebra’s WSE’s vast on-chip memory. WSE 2, the second version of the chip, packs 40GB of on-chip memory, a thousand times more than Nvidia’s A100 GPU chip, Nvidia’s current mainstream offering.

As the authors of NETL and Cerebras describe the matter,

While GPU bandwidth is high, latency is also high. Little’s Law dictates that when both latency and bandwidth are high, a large amount of data must be in transit to keep utilization high. Maintaining significant amounts of data in transit results in large subdomain sizes. These single-device scaling characteristics limit the achievable iteration rates on GPUs. On the other hand, the WSE has L1 cache bandwidths and single-cycle latency, hence the achievable iteration rates on each processor are much higher.

The simulation works on a kind of Excel spreadsheet with over 2 million cells with changing values.

While research in November found the CS-2 to be far faster than the Joule in field equations, scientists at NETL have yet to report official speed comparisons for the fluid dynamics work announced Tuesday. That work is currently being done on a cluster of GPUs for comparison, Feldman said.

In the press release, NETL and Cerebras said, “The simulation is expected to run hundreds of times faster than traditional distributed computing, as previously demonstrated with similar workloads.”

Also: AI startup Cerebras has celebrated chip triumphs where others have tried and failed

The Cerebras CS-2 machine used in the project is installed at Carnegie Mellon University’s Pittsburgh Supercomputing Center as part of the Neocortex system, a “high-performance artificial intelligence (AI) computer” funded by the National Science Foundation.

The current work isn’t the first time Cerebras has branched out from AI. In 2020, the WSE chip excelled in another partnership with the DoE on another problem set of partial differential equations in fluid dynamics.

Feldman, CEO of Cerebras, indicated that there are many more scientific computing opportunities to come for the company.

The simulation of buoyancy-driven Navier-Stokes flows, Feldman noted, uses “fundamental” equations in computational fluid dynamics.

“The fact that we can crush it in this simulation bodes very well for us in a variety of applications.”

Source: www.zdnet.com