STAC Research develops financial benchmarks in partnership with leading banks and software or hardware vendors. The STAC-A2 suite of benchmarks aims to represent the standard risk analysis workload that banks and insurance companies use to measure exposure on the financial markets. Earlier this year we published a Parallel Forall post on Monte Carlo simulation for the pricing of American options in STAC-A2.
Record Performance with Tesla K80
Recently, STAC Research published astonishing performance results for the STAC-A2 benchmarks on an NVIDIA Tesla K80. In short, a single Tesla K80 driven by two CPU cores outperforms all previously audited systems in terms of pure performance and power efficiency.
For more on these results, read “Bank on It: Tesla Platform Shatters Record on Risk-Management Benchmark” on the NVIDIA Blog.
We obtained these new results after several optimizations of our previously audited code. First of all, a large fraction of the computations are now avoided due to a better factorization of the underlying mathematical process. Secondly, we tuned some of the kernel parameters to take advantage of the larger register file of the Tesla K80. Finally, we were able to significantly reduce the latency in one of the main loops of the benchmark. Let’s take a look at these optimizations. Continue reading
In finance, an option (or derivative) is the common name for a contract that, under certain conditions, gives a firm the right or obligation to receive or supply certain assets or cash flows. A financial firm uses options to hedge risks when it operates in the markets. It is critical for a firm to be able to accurately price those instruments and understand their dynamics to evaluate its positions, balance its portfolio and limit exposure to potential threats. The calculation of risk and prices for options is a computationally intensive task for which GPUs have a lot to offer. This post describes an efficient implementation of American Option Pricing using Monte Carlo Simulation with a GPU-optimized implementation of the Longstaff Schwarz algorithm.
NVIDIA recently partnered with IBM and STAC to implement the STAC-A2™ benchmark on two NVIDIA Tesla K20X GPUs. It is the first system that was able to calculate the risk and pricing of this particular complex option in less than a second. A system with two Tesla K20X GPUs is up to 6 times faster than a state-of-the-art configuration using only CPUs. Even more interestingly, adding one or two Tesla K20X GPUs to a system offers speedups of slightly more than 5x and 9x, respectively, compared to the same system without GPUs. Continue reading
This week’s CUDA Spotlight is on Jon Rogers of Texas A&M University. Jon is director of the Helicopter and Unmanned Systems Lab, where he works on new technologies for autonomous systems.
He is currently exploring new algorithms and sensing technologies to increase task complexity of robotic devices. His research encompasses the fields of non-linear dynamics, robust control, and high-performance computing.
You can read Jon’s full Spotlight here. Here is an excerpt.
NVIDIA: What problems has CUDA helped you solve?
Jon: CUDA has provided an entry point to GPU programming and execution that is highly compatible with our current guidance and control software. As we search for new ways to incorporate uncertainty quantification in real-time guidance laws, we are naturally drawn to GPU-based Monte Carlo due to its flexibility in handling nonlinear dynamics and non-Gaussian behavior.
We leverage CUDA primarily for parallel trajectory simulation, which means we have developed dynamic models for several vehicles (mostly aircraft) that run within a GPU kernel. Launching thousands of threads means we can run numerous dynamic simulations at once.