Advanced Computing in the Age of AI | Monday, May 23, 2022

Accelerating insights in large scale AI projects 
Sponsored Content by HPE

Abstract technology background.

Enterprises across the world are innovating with AI, but each is striving for different goals and using different technologies. According to Gartner, most organizations will move away from big data towards small and wide data. Wide data combines "a variety of small and large, unstructured, and structured data sources" to find links between them and facilitate more robust AI. As AI evolves and diversifies, organizations are likely to need diverse AI infrastructures to meet specialized needs.

So, what is the best platform to run your AI project on? It all depends on your goals and the type of data you’re dealing with.

In a variety of AI use cases—specifically when datasets are large and complex, with relationships between data elements that make partitioning difficult—it is often better to keep the dataset in one piece. This speeds up insights, as you can avoid data transport and data re-assembly after analysis.

Benefits of large, shared memory

AI-focused systems often feature high-speed interconnects, which can move large amounts of data very quickly between each processor. But in scale-out AI clusters, moving data between nodes might lead to performance bottlenecks. There is latency when data is moved between servers, and it takes time to re-assemble large and complex datasets that were broken up and distributed across the cluster. In these use cases, a scale-up architecture with a large shared-memory capacity, such as HPE Superdome Flex family, improves time to insight compared to scale-out solutions because it removes the need to partition datasets and re-assemble them after analysis.

Let’s examine two recent use cases:

Use case: 10x faster virus genome sequencing

Genomic sequencing has become a vital tool in the fight against COVID-19. By revealing the genetic secrets of the virus, researchers are learning how it mutates and spreads. This enables health authorities to quickly roll out measures, and guides scientists to develop vaccines and treatments. And it’s how McMaster University is helping Canada emerge from the COVID-19 pandemic and prevent the next one.

Researchers at the M.G. DeGroote Institute for Infectious Disease Research are using genomic surveillance and advanced technology to track the pathogens and understand how they evolve and can be better controlled. By leveraging shared-memory solutions from HPE, they have been able to speed up data analysis by a factor of 10, even when facing tremendous data growth as a result of the accelerating pandemic.

Next for the team in Canada as the pandemic gets under control, is research around antimicrobial resistance (AMR), where a disease-causing bacteria becomes resistant to treatment.

Use case: High-performance storage for AI accelerators

Leading research institutes are choosing HPE Superdome Flex as the basis of new supercomputing systems designed to accelerate AI. Both the University of Edinburgh and Pittsburgh Supercomputing Center (PSC) are combining Superdome Flex with Cerebras CS-1, an AI accelerator based on the largest processor in the industry.

EPCC, the supercomputing center at the University of Edinburgh, is using Superdome Flex as a high performance front-end storage and pre-processing solution for the Cerebras CS-1 AI supercomputer. The role of Superdome Flex is to enable:

  • Application-specific pre- and post-processing of data for AI model training and inference, allowing the Cerebras CS-1s to operate at full bandwidth
  • Use of large datasets in memory

EPCC director Mark Parson says the center needed to invest in technology for large-scale AI challenges, and working with HPE has enabled it to “explore new and emerging technologies” such as Cerebras.

Learn more:

As we can see from these use cases, no two AI workloads are quite the same. When you are working with large, complex datasets, it is often better to store them in one large memory pool—especially if you want to reach insights faster.

For more about how the large shared memory of HPE Superdome Flex family can help you tackle AI problems holistically, visit

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