Oracle AI Vector Search enables enterprises to leverage their own business data to build cutting-edge generative AI solutions. AI Vectors are data structures that encode the key features or essence of unstructured entities such as images or documents. The more similar two entities are, the shorter the mathematical distance between their corresponding AI vectors. With AI Vector search, Oracle Database is introducing a new vector datatype, new vector indexes (in-memory neighbor graph indexes and neighbor partitioned indexes), and new Vector SQL operators for highly efficient and powerful similarity search queries. Oracle AI Vector Search enables applications to combine their business data with large language models (LLMs) using a technique called Retrieval Augmentation Generation (RAG), to deliver amazingly accurate responses to natural language questions. With AI Vector Search in Oracle Database, users can easily build AI applications that combine relational searches with similarity search, without requiring data movement to a separate vector database, and without any loss of security, data integrity, consistency, or performance. At the heart of this are the hardware and system requirements needed to facilitate scale up and scale out AI vector search.

Compute performance demand has been growing exponentially in recent years, and with the advent of Generative AI, this demand is growing even faster. Moore’s law coming to an end as well as the Memory Wall (bandwidth & capacity) are the main performance bottlenecks. The chiplet system-in-package (SiP) is the industry's solution to these bottlenecks. Silicon interposers are industry’s main technology to connect chiplets in SiPs, but they introduce several new bottlenecks. The largest interposer going to production is 2700mm2, which is ~1/4 the largest standard package substrate. Thus, a SiP with silicon interposer has limited compute & memory chiplets, thus limited performance.
This presentation introduces Universal Memory Interface (UMI), a high bandwidth efficient D2D connectivity technology between compute and memory chiplets. UMI PHY on standard packaging provides similar bandwidth/power to D2D PHYs with silicon interposers, thus enables creation of large & powerful SiPs required to address Gen AI applications.

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.