Scicomp Accelerates Market Leading Derivative Pricing Software With NVIDIA CUDA
SciComp Slashes Development Time and Automates Acceleration of Pricing Models with the GPUFor further information, contact:
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SANTA CLARA, CA—SEPTEMBER 16, 2008—Trading in over-the-counter financial derivatives is a high-risk, high-pressure venture. SciComp, an Austin, Texas-based company, has a high-tech derivatives software solution to shorten the development time and accelerate the performance of Monte Carlo pricing models. The company has enhanced SciFinance®, its flagship product, to deliver accurate NVIDIA® CUDA™-enabled derivatives pricing models that run up to 100 times faster than serial code. More significantly, this speed up can be achieved without any additional work or hand programming which, in a market where a slight delay or inaccuracy can end up costing millions, is a critical advance.
The key to this speedup is reliance on the graphics processing unit (GPU) for the calculations. A GPU is a many-core (up to 240 cores in the latest models) parallel processor that can run parallel applications many times faster than a computer’s CPU. This massive parallel computational power is unlocked by NVIDIA CUDA architecture, a programming environment based on the industry-standard C language that enables developers to write software to solve complex computational problems in a fraction of the time.
“The old aphorism ‘time is money’ has never been more true than in the pressurized world of OTC derivatives trading, where the constant flow of new contracts demands the ability to produce complex mathematical pricing models rapidly,” said Curt Randall, executive vice president of SciComp. “This process used to take days if not weeks of error prone hand coding, but with SciFinance, model developers make a few changes to a model specification of a half page or less and then generate accurate C or C++ source code pricing models in minutes.”
“SciFinance’s new ability to generate GPU-enabled code without additional programming is game-changing technology for our customers,” added Randall. “The code takes full advantage of the GPU’s parallel architecture, delivering an immediate 20-100X execution speed increase. Pricing models that used to run in minutes now complete in seconds, allowing financial institutions to test alternatives models, increase scenario analysis, and better understand their potential risk exposure. And best of all, they need not become experts in parallel coding concepts. SciFinance takes care of that.”
To take advantage of CUDA architecture, a bank’s in-house development team need simply use SciFinance’s high-level financial and mathematical language for describing the derivatives model. Adding the keyword “CUDA” to a model specification outputs CUDA-enabled source code which can run on any standard PC with a CUDA-enabled GPU.
Financial institutions such as banks and hedge funds engage in derivative transactions to help manage financial exposure and risks. Derivative contracts are financial instruments whose value is based upon fluctuations in an underlying variable (e.g., the value of a stock option depends on the volatility of the underlying stock). Pricing derivatives requires complex mathematical models, often requiring the running of millions of scenarios. Therefore, fast and accurate calculations are at a premium.
“SciComp is one of the first companies to fully embrace the potential that GPUs have in the field of computational finance,” said Andy Keane, general manager of the GPU Computing business at NVIDIA. “The ability to not just deliver small and incremental increases in performance, but instead to deliver 100X and reduce weeks of hand coding to immediate, real-time results is incredibly powerful. We look forward to working closely with SciComp going forward to bring more defining improvements to the SciFinance generated pricing models and in turn their customers’ businesses.”
About SciComp Inc.
Certain statements in this press release including, but not limited to, statements as to: the benefits, features, uses, impact, and capabilities of NVIDIA GPUs, NVIDIA CUDA technology and SciFinance are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: development of faster or more efficient GPUs by our competitors; use of the CPU rather than the GPU for computing applications; development of more effective or efficient technology for computing purposes; the impact of technological development and competition; design, manufacturing or software defects; changes in end-users' preferences and demands; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission including its Form 10-Q for the fiscal period ended July 27, 2008. Copies of reports filed with the SEC are posted on our website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.
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