About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

STAC Benchmarks GPUs for Options Risk Analytics

Subscribe to our newsletter

STAC has for the first time published results for its STAC-A2 options risk analytics benchmarks running on Nvidia graphics processing units (GPUs) that point to a near order of magnitude speed up compared to traditional x86 CPUs.

STAC-A2 is a suite of benchmark tests developed by market participants that measure the time to complete the calculation of a set of Greeks values for an option (which measure the sensitivity of the price of an option to changes, such as price of the underlying asset, volatility, interest rates, etc.). Thus, Greeks – which should be recalculated as an options price varies – provide a risk management tool for assessing market impacts on a portfolio of options.

In order to conduct the benchmarks, STAC built a system based on an IBM server with two Intel ‘Sandy Bridge’ x86 processors and two Nvidia K20Xm GPUs. Nvidia coded the STAC benchmarks using the CUDA toolkit, which is designed to implement parallel high performance computing workloads.

Among the several benchmarks calculated, results for STAC-A2.?2.GREEKS.TIME – the time taken to calculate a set of Greeks – showed a 9x improvement compared to benchmarks run on the same class of x86 processors, without GPU acceleration.

While the results are simply indicators of performance, they do point to the value of GPUs to handle complex calculations, which increasingly need to be performed in real time as part of intelligent trading strategies.

As such, GPUs complement other acceleration approaches, such as FPGAs, which have been widely implemented to perform data manipulation functions for low-latency market feed handling and trade execution. Future trading system architectures may well incorporate both FPGAs and GPUs alongside traditional CPUs to provide a best of breed platform for all aspects of a trading strategy.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: From Data to Alpha: AI Strategies for Taming Unstructured Data

Unstructured data and text now accounts for the majority of information flowing through financial markets organisations, spanning research content, corporate disclosures, communications, alternative data, and internal documents. While AI has created new opportunities to extract signals, many firms are discovering that value is constrained not by models, but by the quality of the content, architecture,...

BLOG

Exegy and STRANDS Target Institutional Workflows for Prediction Market and Digital Asset Data

Exegy and STRANDS have announced a partnership to bring real-time prediction-market, digital-asset and smart-contract data from centralised and decentralised venues into Exegy’s Axiom consolidated feed service, with initial content scheduled for delivery in May 2026. The announcement extends Exegy’s market-data offering into a broader set of emerging asset classes and data types, including prediction markets,...

EVENT

TEST Event page 1

Now in its 15th year the TradingTech Summit London brings together the European trading technology capital markets industry and examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...