High-performance computing comes in especially handy in operations involving the analysis of large datasets. Traditional application areas such as meteorology, research, manufacturing, life sciences and finance all share this trait.

With finance, in particular, HPC is being used at the quantitative level to perform activities ranging from credit valuation adjustment to risk analysis and trading signal collation.

“It’s being used for risk pricing and discovery and capital markets. And that’s what we call quantitative finance in HPC,” said Prabhu Ramamoorthy (pictured, middle), customer/partner developer relationship manager at Nvidia Corp. “Customers are doing large-scale simulations and risk valuations in real time so they can get ahead of the competition. Combined with artificial intelligence and other technologies, people are using large language models for trading signals and to create their own quantitative finance algorithms.”

Ramamoorthy; Andrew Luu (left), product manager at Dell Technologies Inc.; and Peter Nabicht (right), president of the Securities Technology Analysis Center LLC, spoke with theCUBE industry analyst John Furrier at the ISC High Performance event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed trends in the financial industry applications for HPC. (* Disclosure below.)

The signals on quantitative finance and HPC

Guided by the STAC Benchmark Council, which covers over 50 leading technology vendors and over 400 financial firms, STAC exists to improve finance-related technology discovery and assessment, according to Nabicht. It carries out that function through both research and community dialogue.

“STAC came along and helped bring together the Benchmark Council to define those workloads so that we could do apples-to-apples comparisons of different technology stacks to see how they solve the problems, how quickly they do it, how much throughput they can get done, and how efficiently they do it,” Nabicht explained. “Now, 15 years later, that’s what we do in a variety of areas, including HPC and AI.”

Importantly, STAC has observed a change in the nature of analytics to become more instantaneous to where it’s merging with critical trade paths, Nabicht added.

“People need to do more real-time HPC and analysis of data,” he said. “Therefore, where we used to talk in nanoseconds for electronic trading and milliseconds or seconds for analytics, those are merging together. And it’s not because the real time’s getting slower; it’s because that analytics has to get faster and faster.”

HPC is very hardware intensive. Thus, to help its financial services customers stay at the cutting edge of computing capabilities, Dell Technologies is leveraging Nvidia’s graphical processing units to underpin its PowerEdge server portfolio, according to Luu.

“We are heavily investing in the financial services industry,” he explained. “We’ve been working closely with Nvidia, and STAC has also been a great partner for us. We’ve also been listening to our customers discovering what they want in their solutions, and that’s led to us putting together Nvidia products like the A100 GPU along with PowerEdge servers like the XE8545.”

Speed and throughput are the most critical factors sought by financial services organizations, and companies like Nvidia and Dell Technologies are creating solutions to directly meet those problems, Nabicht added.

“In capital markets and financial services, speed and throughput are the ante,” he said. “They’re table stakes for everything that is done in capital markets. It’s been great to see Dell over the last couple of years, and Nvidia as well, step up and give financial firms the data they need to make decisions they need to make on the solutions they’re looking to procure.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the ISC High Performance event:

(* 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