Scientific Computing & HPC

Supercomputer-scale HPC in a single wafer-scale system

Industry Challenge:

Scientific computing and HPC power the forefront of research and discovery in fundamental physics and life sciences, leading to important advances in industry, security, and insights that improve and enrich our daily lives.

The HPC workloads that drive these applications often require massive sparse computation with high bandwidth memory access and communication. Even supercomputers are challenged by this work.

Using the CS-2, researchers can achieve orders of magnitude acceleration for traditional HPC workloads, AI surrogate workloads, or HPC+AI cognitive simulations that leverage both.

Use case

Computational fluid dynamics

Computational fluid dynamics (CFD) codes are a central component of aerospace and energy research. These workloads require massive sparse computation at extraordinary memory and communication bandwidth. Researchers typically take great lengths to develop state of the art implementations on massive supercomputers. The Cerebras WSE-2 puts all of these resources on a single chip, delivering supercomputer-impossible performance of 100s-10,000s of legacy C/GPU machines for this work.

Use Case

AI-accelerated modeling & simulation

Traditional HPC can be accelerated by AI surrogate models that learn physics or by AI models that augment traditional supercomputers to inform the next steps of a physics-based simulation code. As the world’s fastest AI computer, the CS-2 is uniquely well-suited as a specialist accelerator for this type of work in a heterogenous AI+HPC cluster.

Use Case

Molecular dynamics

Molecular dynamics simulations power a range of applications from drug discovery to materials science. Calculating the time dependent state and interactions across large-scale simulations typically requires hundreds of nodes on a supercomputer. Deep neural networks are increasingly used for the prediction of energies and forces in molecules. With the CS-2, researchers can train physics-informed neural networks and accelerate MD simulations codes on a single machine with the WSE-2’s high bandwidth local on-wafer SRAM and interconnect.

Testimonial
The CS-1 allowed us to reduce the experiment turnaround time on our cancer prediction models by 300X, ultimately enabling us to explore questions that previously would have taken years, in mere months.

Rick Stevens

Associate Laboratory Director of Computing, Environment and Life Sciences @ Argonne National Laboratory