Even before the global pandemic, many industries were experiencing rapid growth in datasets. Yet, the healthcare industry is currently dealing with a data explosion of unprecedented size, and this has sharpened the tech industry’s focus on the use of artificial intelligence and machine learning to handle it.

When it comes to making AI real, it’s hard to imagine anything more important than the lives of millions of people around the world.

“Healthcare is going through an explosion of data, about 3,000 times the content of the Library of Congress,” said Thierry Pellegrino (pictured, right), vice president of data-centric workloads and solutions, HPC, at Dell Technologies Inc. “Because it’s healthcare, because it’s the industry, we see that our family members at VMware, part of the Dell Technologies portfolio, are getting even more relevance in the discussion. What we’re working on right now is the next wave of AI and high-performance computing enablement.”

Thierry spoke with Stu Miniman, co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during a special CrowdChat presentation. He was joined by Ravi Pendekanti (pictured, left), senior vice president of server product management at Dell. Miniman also spoke with Krish Prasad, senior vice president and general manager, vSphere business at VMware Inc., and Josh Simons, chief technologist for HPC at VMware, in a separate interview. They discussed newly launched initiatives in AI and HPC, how virtualized resources can help a wide range of businesses, and scalable support for scientific workloads. (* Disclosure below.)

Virtualizing accelerators

To implement the next wave, Dell has introduced Dell EMC Ready Solutions for AI and vHPC. The offering leverages VMware’s Bitfusion technology and combines Dell EMC hardware with VMware’s Cloud Foundation software to virtualize accelerators, such as GPUs, field programmable gate arrays, and ASICs with the goal of expanding resources to run AI workloads.

“You see the advent of a lot of specialized accelerators, custom application-specific integrated circuits, field-programmable gate arrays and graphic processing units being used to accelerate the special algorithms that these AI and machine learning-type applications need,” Prasad explained. “With the Bitfusion launch today we are essentially doing to the accelerator space what we did to compute several years ago. We’re bringing virtualization to accelerators.”

What is the significance of this deployment in real life? Simons cites the example of a retail grocery store chain that relies on a large number of video cameras to monitor everything from inventory to security. This requires multiple applications consuming data from streaming images around the clock and performing analytics at the same time. That also demands a significant amount of compute power.

“They would be perfectly suited for this type of environment where you would like to be able to have multiple, independent applications running, but have them efficiently share the hardware resources of the GPUs,” Simons said. “With Bitfusion, it’s now possible for the data scientist to either consume multiple GPUs or single GPUs or even fractional GPUs.”

The VMware Bitfusion solution will initially extract value by virtualizing Nvidia Corp.’s chips, according to Simons, with plans to embrace FPGAs and domain specific ASICs in the near future.

“We are starting with the Nvidia GPU, which totally makes sense since that is the primary hardware accelerator engine for deep learning,” Simons said.

Amount and quality of data

Dell and VMware are anticipating that the mere 14% of companies currently reporting widespread deployment of AI is inevitably going to grow. VMware acquired Bitfusion.io Inc. in 2019 and then integrated it into vSphere, its cloud computing virtualization platform. By allocating a pool of virtual GPU accelerators, Dell’s customers will have expanded resources for AI workloads without the need for additional servers.

“There’s really nothing artificial about the real intelligence that comes through with all the solutions that we’re working on,” Pendekanti said. “As much as we talk about AI and machine learning, one of the biggest challenges that customers have today is ensuring they have the right amount and quality of data to go out and do the analytics. If you don’t do it, it’s garbage in and garbage out.”

Dell’s strategy to make AI real also involves making it scalable. The company is focused on supporting high-performance computing workloads, which power scientific simulations, weather forecasting, and molecular modeling. In October, Dell and the Italian energy company Eni announced the deployment of the world’s largest industrial supercomputer.

“They’ve been really driving innovation with us,” Pellegrino said. “They’re focusing on digital transformation and development of energy resources. We just deployed an 8,000 accelerator cluster with them, which is the largest commercial cluster in the world.”

The most recent launches by Dell and VMware in the AI and HPC space are designed to move the enterprise conversation from the theoretical to reality, where intelligence and computing technologies make a significant business impact.

“We can make this real in terms of moving away from a modeling scenario to something that customers are able to use today,” Pendekanti said. “We’re making sure we provide them with not just the products, but also the ready solutions. The flexibility that we provide all the way through services is truly our attempt at Dell Technologies to get the entire gamut of solutions available for the customer to go and choose what services they need.”

Here’s the complete video interview, one of many CUBE Conversations from SiliconANGLE and theCUBE: (* Disclosure: Dell Technologies Inc. sponsored this segment of theCUBE. Neither Dell Technologies nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE