The London-based bank HSBC demonstrated that it may be able to save millions of dollars in computer costs by moving a portfolio pricing process from a grid of Intel Xeon processors to NVIDIA Tesla GPUs, reports Xcelerit, the company that helped the bank with its experiment by providing CUDA programming tools.

In April, Xcelerit reported on the promising experiment conducted by the Quantitative Risk and Valuation Group (QRVG) at HSBC, which reported more than $2.6 trillion in assets in 2012. The QRVG is responsible for running Credit Value Adjustment (CVA) processes every night over HSBC’s entire portfolio to compute its risk exposure, per Basel III requirements. 

Currently, it takes several hours to run the CVA processes on a grid of Intel Xeon processors. Eurico Covas, Head of QRVG Development and Hedge Accounting Systems at HCBC, wanted to see whether it was possible to use GPUs to run this calculation on an intra-day rather than an overnight basis, according to Xcelerit’s blog post.

HSBC has major investments in the code that drives the CVA workload on traditional Intel processors, but its developers lack the CUDA expertise needed to program NVIDIA’s GPUs. “We had heard that Xcelerit offered an easy way to get our existing code to drive GPUs at their maximum speeds,” Covas said in the blog post.

After working on the project for several days, a single developer had identified a promising section of the application that drives the CVA processes to try out on the GPUs. The work of transitioning the code to CUDA involved little more than inserting Xcelerit’s API calls into the code, according to Xcelerit.

Next, the QRVG group set out to test some pricing calculations on the GPUs. In one example, Xcelerit reports that the QRVG took a set of 10,000 swap instruments and priced it for a set of 1,000 Monte-Carlo scenarios at 26 time steps, for a total of 260 million individual calculations.

When the pricing routine was run on a single Tesla K20 GPU, it ran 19 times faster than on a 4-core Intel Xeon E5620 CPU, according to a performance analysis. When run on a system with 3 GPUs, it scaled almost linearly, and completed the work 57 times faster than the Intel CPU.

While it was just a small experiment that involved a small and relatively simple component of the complex CVA processes, it demonstrated a potential for big savings for HSBC. A full set of GPU-enabled systems would cost about $22,000, compared to the $1 million for a 600-CPU grid, according to a Forbes article on the HSBC experiment. 

“We are just dipping our toes in the water on this,” Covas says in the Xcelerit blog post. “Our quant teams have a voracious appetite for computing power and obviously using GPUs offers a cost-effective solution to that problem.”

As regulators continue to strengthen banking laws and tighten capital requirement, banks will demand faster and more efficient hardware to run CVA and risk calculations across their holdings. GPU computing, with its ability to not only cut hardware expenditures but help with the electricity bill too, may provide part of the solution.

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