AI-HPC Convergence: Optimising Hybrid Workflows

Webcast supported by

October 14th 2026 - 3pm BST

In this panel discussion, we’ll discover how AI and HPC capabilities can be combined to unlock new research opportunities.
 

The convergence of AI workloads and physics simulation-focused HPC is the next frontier for scientific research organisations. 

From drug discovery to genomic modelling, bench scientists and data stewards alike need a strong foundation for accurate next-gen models and governance.

While R&D teams look to maximise CPU and GPU-based discovery and performance, the challenges of disconnected information and resource management gaps remain due to siloed data. Addressing this requires unification of cloud, edge and on-premise data storage environments, all of which host an array of digital lab tools that host evolving and context-rich information.

A hybrid approach combining AI and HPC capabilities, underpinned by a global namespace with complete access to the data end users need, can unlock new project possibilities and outcomes. This combines higher levels of computing power and scalability with deeper analytical insights delivered at speed, all helping towards maintaining competitive advantages.

This online panel discussion will discuss the challenges impacting research organisations looking to continue adapting their R&D processes, and how data architecture can be boosted to facilitate AI-HPC convergence.

Who should attend?

• Hands-on scientists looking to get the best out of next-gen data platforms
• Data scientists focused on maintaining modelling capabilities and governance
• IT leaders aiming to optimise hybrid infrastructure
• General researchers and academics considering the next steps on their data journey

What will you learn?

• Current and future trends impacting AI and HPC-powered R&D
• Valuable applications and use cases for AI and HPC workflows
• How to build hybrid infrastructure that brings AI and HPC capabilities together
• How to reduce data connectivity gaps, and streamline project costs

Speakers

Floyd Christofferson, VP Product Marketing at Hammerspace

Floyd Christofferson has spent more than 25 years at the intersection of complex data infrastructure and the industries that depend on it most, including HPC, media and entertainment, life sciences, research, and enterprise AI. The through-line has been the same: how organisations store, manage, protect, and operationalise extreme volumes of unstructured data at scale. Data growth, infrastructure complexity, and the pressure to actually extract value from that data have only intensified; AI is accelerating it. That problem is what brought Floyd to Hammerspace as VP of Product Marketing, where he sits between product, technology, and market strategy. Hammerspace treats as a data problem what the industry has long treated as a storage problem: a unified global namespace across heterogeneous storage, sites, and clouds, using open standards and existing infrastructure, without proprietary clients or forced migrations. Floyd’s current focus is AI infrastructure, hybrid cloud data orchestration, Tier 0, the Hammerspace AI Data Platform, and the Open Flash Platform. Before Hammerspace, he held executive roles in data management and archive, including a decade at SGI on some of the world’s largest HPC storage environments.