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NOVEMBER 12-17 | Denver, CO

SambaNova at SC23

Powering the next wave of Generative AI for scientific innovation

CONNECT WITH SAMBANOVA’S SUPERCOMPUTING EXPERTS AT SC23

Find us at Booth #681, or schedule a meeting to discuss how SambaNova is using a revolutionary new architecture, as part of a full stack system, which enables transformer models with long sequence lengths and trillions of parameters to power the next wave of Generative AI for scientific innovation and discovery.

kunle

Kunle Olukuton

Co-founder and Chief Technologist

marshall

Marshall Choy

SVP of Product

jennifer

Jennifer Glore

VP of Customer Engineering

dawei

Dawei Huang

Senior Director of Engineering

petro

Petro Junior Milan

Principal Software Engineer

benjamin

Benjamin Glick

Principal Software Engineer

Schedule

Attend scheduled SambaNova sessions and events.

08:30AM

Tutorial: Programming Novel AI Accelerators for Scientific Computing

Join a panel of experts, including SambaNova’s own Petro Junior Milan, as they explore the landscape of AI technologies and how you can use them to scale your own AI implementations. In this tutorial, we will present an overview of novel AI accelerators. This includes presentations on hardware and software features on each system. We present steps on how to program these systems by porting deep learning models in standard DL framework implementations, compiling and running on the accelerator hardware.

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10:30AM

Workshop: RDARuntime: An OS for AI Accelerators

Today’s supercomputers are more heterogeneous than ever before. As the share of AI workloads in data centers continues to grow, the share of GPUs and AI-specific hardware grows with it. AI accelerators are different from traditional hardware, affecting all aspects of system design, from data-center scale to single-chip scale. AI accelerators are much more efficient than CPUs or GPUs for some HPC workloads, especially in AI for Science. They also add complexity to system architecture, management, and programming. Although runtime frameworks are critical to reducing system complexity, there is little literature describing AI accelerator runtimes. In this paper, we introduce RDARuntime – an AI-specific OS tailored for the development and operation of SambaNova’s reconfigurable dataflow architecture. We introduce the architecture, our design decisions, and some of the results we have achieved, along with some lessons we have learned while helping to deploy the Reconfigurable Dataflow Unit (RDU) to production environments.

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07:00PM

Grand Opening Gala Reception

SC23 will host its annual Grand Opening Gala in the Exhibit Hall. This will be your first opportunity to see the latest high performance computing, networking, storage, analysis, and research products, services, and innovations.

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04:00PM

Meet the Experts Happy Hour

Join SambaNova’s own Kunle Olukuton, Marshall Choy, Jennifer Glore, Petro Junior Milan, Dawei Huang, and Benjamin Glick for complimentary cocktails and to connect with industry leaders

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05:15PM

Birds of a Feather: Advances in FPGA Programming and Technology for HPC

FPGAs have gone from niche components to being a central part of many data centers worldwide. The last year has seen tremendous advances in FPGA programmability and technology, especially in the shift to reconfigurable architectures that are heterogeneous and/or based on CGRAs or other AI engines. This BoF has two parts. The first is a series of lightning talks presenting advances in tools, technologies, and use-cases for these emerging architectures. The second part of the BoF will be a general discussion driven by the interests of the attendees, potentially including additional topics.

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Powering the next wave of Generative AI for scientific innovation

Multi-trillion parameter large language models are enabling research organizations to unlock data insights through multi-modal inputs and achieve groundbreaking discoveries across numerous areas of research including high resolution 3D image analysis, large language models for science, and multi-physics simulation workloads.

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