GPU processors are built as multi-core architectures. They present Hundreds of parallel cores, Thousands of threads in parallel.

GPUs are getting faster, faster than CPUs. Hardware peaks at 450 Giga Flops on a single GPU ship in 2007, and reaches over the Tera Flops in 2008.

Paradigm change in scientific computing, algorithms and methods are designed with multiprocessing in mind.

OnEye Libraries enables algorithms not designed to run in parallel to access the processing power of multi-core and GPU hardware.

Develop, Test, Deploy High Performance processes from virtually any client, in a flexible and responsive environment.

Flexible, on-demand supercomputing for professionals.

GPU solutions dramatically reduces the Total Cost of Ownership for superior throughput.

GPU increases the capacity to design better products - one can run 50 design simulations on a GPU processor when the state of the art CPU produces 1. GPU delivers products with higher performance characteristics and lower costs to meet deadlines.

Reduces Energy Footprints (Power Supply, Cooling), an enormous economical benefit with the reduction of power and space needed to run and house GPUs.

By using GPU technology we provide parallel computing solutions that deliver massive processing power with the lowest possible operating cost in terms of power, cooling and footprint.

Studies have demonstrated that IT organizations spend 48% of their budget on energy, and 70% of the surveyed acknowledge that power and cooling were the biggest problem. More than 50% of the power is consumed for cooling purposes. Source: eWeek, Vol.24,No.32).

A GPU based computing solution utilizes 75% less space and energy than its traditional CPU based counterpart.

Using GPUs, our Algorithm Trading Solution can price billions of instruments per second. We are presenting speedup results for Black Scholes Option Pricing, Lattice Models and Monte Carlo Simulation. Read More Here »

Using GPUs, analysts run their models on parallel servers, clusters, and grids to optimize thousands of individual portfolios overnight based on the previous day’s trading results..

Valuing financial derivatives is computationally intensive, and requires large amounts of computer time. A firm may need to value and compute hedge strategies for hundreds of thousands of policy holders in its portfolio on a regular and timely basis. Software written for GPU enables analysts to explore new valuation methodologies using high performance computing techniques to run billions of complex scenarios.

The rise of identity theft together with the popularity of online shopping has resulted in a huge increase in credit card fraud. As thieves become increasingly shrewd in exploiting security weaknesses, banks and credit card companies need to be extremely agile to stay ahead of them. GPUs enable a bank to run more sophisticated fraud detection algorithms against tens of millions of credit card accounts.

In balancing a large portfolio of stocks, analysts need to search for short- and long-term patterns, identify correlations between securities, and develop forecasts. Intense computations are required against terabyte-sized “tick data” databases – potentially a decade or more of trading data for thousands of securities. Using GPU software allows faster reaction time to market conditions, enabling analysts to evaluate more sophisticated algorithms that take into account larger data sets.

GPU is a natural fit for creating and running the multiple simulations (each with numerous scenarios and variables) needed to accurately assess the degree of risk in stock portfolios, in a set of financial contracts, or other investment vehicles. In particular, “outlier” cases require a great many simulations to capture the level of risk.