User Spotlight: Yu Tian, Ph.D. Student at Monash University

by Liana

Meet Yu Tian, a Financial Mathematics PH.D. student from Monash University in Australia.

He is a member of our CULA user community and is being highlighted here for his contributions in the field of high performance computational finance. Last year, he and his colleagues published a paper entitled "Option Pricing with the SABR Model on the GPU."

He explained to us that in the world of computational finance, the most popular areas of research are centered on: 1) new financial models, and 2) novel computational methodologies to solve models more efficiently. His research is focused on the acceleration of option pricing models leveraging state-of-the-art techniques and technologies such as GPUs.

Modern option pricing techniques are extremely complex, but allow financial analysts to calculate the value of a stock option with tremendous accuracy. These models involve many linear algebra routines, such as CULA's Singular Value Decomposition function.

How CULA Has Helped

"We used the SVD function in CULA Basic in a least squares problem to solve an American option pricing model and were able to achieve a 4X speedup," said Yu Tian.  "Now that we have a supercomputing center at our university, our plan is to get the full version of CULA for all GPU nodes and do some research on calibrating complex financial derivatives models."

We're wishing Yu and his colleagues at Monash University best of luck in their research!

What is your experience with CULA? If you'd like to share, please contact us.


User Spotlight: David Hastie, Ph.D.

by Liana

This is the first post of a new blog series called User Spotlight. Our goal is to help facilitate knowledge sharing within our user community.  As often as possible, we will be writing about the cutting edge research our users are involved in, and how CULA has helped them.

On the spotlight today: Dr. David Hastie from the Imperial College in London.

Dr. Hastie is a research assistant in the Department of Epidemiology and Public Health, in the School of Public Health at the Imperial College. He is a member of the Biostatistics group and is currently focused on a project aimed at understanding how various factors combine towards the risk of lung cancer.  His work also involves the development of Evolutionary Stochastic Search, a variable selection algorithm based on an Evolutionary Monte Carlo approach, for single and multiple response linear models. Working with collaborators, he is looking to extend the algorithm to be applicable to logistic regression and regression with interaction terms.  He is involved in the development of the C++ software for this algorithm, and is also leveraging GPU programming techniques to improve performance of the algorithm.

How CULA has helped

“We use CULA within an algorithm we have developed to do variable selection. We are applying this algorithm to genetic data to see which genes are associated with different outcomes. This involves very large matrices. We are mainly using the QR decomposition functionality in CULA and have found it to be hugely helpful. In particular we have overcome bottlenecks that previously we had not been able to surmount,” said Hastie.

Once his research paper has been published, we will certainly add it to our library. Meanwhile, if you would like to learn more about his work, feel free to visit his web site.

If you'd like to share about your research work , including how CULA has contributed to it, please contact us!  We appreciate the feedback!


How is CULA being used?

by Liana

A few weeks ago we invited our users to share about their experience with CULA.  While we're still gathering their stories, we decided to go ahead and launch our new Research Papers page so it is easier for you to find papers that have been written about CULA, or make a reference to it.

We'd like to highlight and give special thanks to the Medical Image Processing Group at Institute of Automation, Chinese Academy of Sciences for their paper entitled The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography. Very educational paper!  Below is just a section we pulled from the abstract:

The CUBLAS and CULA are two main important and powerful libraries for programming on NVIDIA GPUs. With the help of CUBLAS and CULA, it is easy to code on NVIDIA GPU and there is no need to worry about the details about the hardware environment of a specific GPU.

If you have published a paper, let us know and we will add it to the new section. If you have not yet published your work, but would like to share your experience with CULA, you're welcome to do so. We are putting together case studies on the various applications that are being accelerated with CULA.  Your feedback is appreciated by the entire engineering team... they love to hear about it, and they love being challenged... so whether positive or negative, let us know how the software is working for you.

Looking forward to your submissions!  Please use our Contact page to send us your papers or case studies.