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DESCRIPTION:[]\nTitle: Realizing Artificial Intelligence: Edge-to-Cloud-to-
 Exascale\n\nAbstract: Foundational models with trillions of parameters are
  being trained. Multi-modal GenAI and Inference Serving services are being
  deployed for a variety of use cases. To meet the computational demands of
  these AI workloads\, we now have infrastructure with larger than ever GPU
 s and networks with ever increasing bandwidths. In this presentation\, I w
 ill talk about challenges of running today’s AI workloads on extreme sca
 le infrastructure. Hewlett Packard Labs is pursuing different research dir
 ections for building resilient\, scalable and sustainable AI infrastructur
 es. I will discuss how we are tackling the complexities of orchestrating A
 I/ML workloads by leveraging AI Workload simulations\, GPU virtualization\
 , performant communication collectives and novel accelerators.\n\nVirtual:
  https://events.vtools.ieee.org/m/462458
LOCATION:Virtual: https://events.vtools.ieee.org/m/462458
ORGANIZER:westphal@ieee.org
SEQUENCE:23
SUMMARY:Realizing Artificial Intelligence: Edge-to-Cloud-to-Exascale
URL;VALUE=URI:https://events.vtools.ieee.org/m/462458
X-ALT-DESC:Description: <br /><div>&nbsp\;</div>\n<div><strong><img src="ht
 tps://events.vtools.ieee.org/vtools_ui/media/display/58c51d6c-c47e-4ee8-9a
 c9-e0e85f2659c5" alt="" width="600" height="338"></strong></div>\n<div><st
 rong>Title:</strong>&nbsp\;Realizing Artificial Intelligence: Edge-to-Clou
 d-to-Exascale<br><br></div>\n<div><strong>Abstract:</strong>&nbsp\;Foundat
 ional models with trillions of parameters are being trained. Multi-modal G
 enAI and Inference Serving services are being deployed for a variety of us
 e cases. To meet the computational demands of these AI workloads\, we now 
 have infrastructure with larger than ever GPUs and networks with ever incr
 easing bandwidths. In this presentation\, I will talk about challenges of 
 running today&rsquo\;s AI workloads on extreme scale infrastructure. Hewle
 tt Packard Labs is pursuing different research directions for building res
 ilient\, scalable and sustainable AI infrastructures. I will discuss how w
 e are tackling the complexities of orchestrating AI/ML workloads by levera
 ging AI Workload simulations\, GPU virtualization\, performant communicati
 on collectives and novel accelerators.<br><br><br></div>
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