Shell Upstream has been processing large subsurface datasets for multiple decades driving significant business value.  Many of the state of the art algorithms for this have been developed using deep domain knowledge and have benefitted from the hardware technology improvements over the years. However, the demand for more efficient processing as datasets get bigger and the algorithms become even more complex is ever-growing. This talk will focus on the memory and data management challenges for a variety of traditional HPC workflows in the energy industry. It will also cover unique challenges for accelerating modern AI-based workflows requiring new innovations. 

There are a set of challenges that emanate from memory issues in GenAI deployments in enterprise
• Poor tooling for performance issues related from GPU and memory interconnectedness
• Latency issues as a result of data movement and poor memory capacity planning
• Failing AI training scenarios in low memory constraints

There is both opacity and immature tooling to manage a foundational infrastructure for GenAI deployment, memory. This is experienced by AI teams who need to double-click on the infrastructure and improve on these foundations to deploy AI at scale.

As the cost of sequencing drops and the quantity of data produced by sequencing grows, the amount of processing dedicated to genomics is increasing at a rapid pace.  [Genomics is evolving in a number of directions simultaneously.]  Complex pipelines are written in such a manner that they are portable to either clusters or clouds.  Key kernels are also being ported to GPUs in a drop-in replacement for their non-accelerated counterpart.  These techniques are helping to address challenges of scaling up genomics computations and porting validated pipelines to new systems.  However, all of these computations strain the bandwidth and capacity of available resources.  In this talk, Roche´s Tom Sheffler will share an overview of the memory-bound challenges present in genomics and venture some possible solutions.

What does an Intellectual Property strategy mean?  Most IP professionals consider an IP strategy to be setting goals, targets, and priorities, but have you considered framing your concepts for achieving your IP targets and expectations? This discussion emphasizes moving away from last-minute actions and adopting mid- and long-term approaches to ultimately contribute significantly to company results. By showing you how to build a solid alignment of IP activities with business objectives, we aim to provide you with the knowledge to create an “IP Ecosystem,” which will drive operational excellence in a sustainable way. 

Dive into the transformative world of Generative AI. Join Juergen Weichenberger VP AI Strategy & Innovation, Schneider Electric, discussing how it’s revolutionizing work process, integrating information and deducting knowledge. You will learn how GenAI unleashes a range of options for industrial applications, from PLC code generation to large visions models for quality & safety assurance, supply chain, order management, and beyond. During the session we will emphasize the advantages and challenges of GenAI in PLC code generation use cases.