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GPU
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March 24-27, 2014 | San Jose, California
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HANDS-ON LAB

Presentation
Details

S4790 - Hands-on Lab: Numerical Integration in CUDA

Carl Ponder ( DevTech Engineer, NVIDIA )
Highly-Rated Speaker
Carl Ponder
Carl is a DevTech Engineer at NVIDIA where he focuses on CUDA application tuning and performance. Carl received his Ph.D. in Computer Science from the University of California, Berkley.

Evaluating integrals is an important part of modelling physical systems. For sufficiently complex systems, integrals as closed-form expressions are difficult to derive or do not exist, so numerical approximation is the method of choice. In this session we will survey methods of Numerical Integration -- Tiling, Monto Carlo and transforms -- and discuss their efficiencies and the characteristics of their approximation error. We will work through some simple hands-on exercises of integrating the Gaussian function, estimating Pi, and measuring the volume of a multidimensional polytope. You will gain some practice writing simple CUDA code and using the cuRand library to generate high-quality random numbers in parallel, which are also applicable to other areas such as randomized simulation. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Numerical Algorithms & Libraries; Finance

Day: Tuesday, 03/25
Time: 16:00 - 17:20
Location: Room 230A

Hands-on lab
 

TALK

Presentation
Details

S4451 - GPU Computing in .NET for Financial Risk Analytics

Ryan Deering ( Director of Quantitative Development, Chatham Financial )
Ryan Deering
Ryan leads Chatham’s Quantitative Development team focusing on derivatives pricing, risk analytics, and credit risk modeling. Prior to joining Chatham, Ryan received his PhD in Mathematics from Duke University in Durham, North Carolina. His dissertation focused on signal processing with applications to speech recognition. Ryan also holds a BS and MA degrees in Mathematics from Duke University.

Learn how a rapidly growing mid-sized financial company incorporated GPU computing into its quantitative finance models. Our quantitative development team faced two major obstacles in adopting GPU computing. The first obstacle is the large cost of switching away from our mature .NET development process. The other obstacle arises from the difficulty of synchronizing a slow hardware purchasing cycle with a fast software delivery cycle. We addressed these concerns by creating a hybrid linear algebra library in .NET that dynamically switches to GPU computing when CUDA hardware is available. This library allows our developers to code in .NET and focus on the mathematical and financial models without worrying about CUDA syntax. In this session we will describe how we built the library in .NET using CUBLAS, CURAND, and CUDA Runtime libraries. We will also show the performance gains from switching to GPU computing in pricing Bermudan swaptions using the Libor Market Model.

Session Level: Beginner
Session Type: Talk
Tags: Finance

Day: Wednesday, 03/26
Time: 09:00 - 09:50
Location: Room 210C

S4331 - Fast and Easy GPU Offloading for Computational Finance

Lukasz Mendakiewicz ( Software Development Engineer in Test II, Microsoft Corp )
Lukasz Mendakiewicz
Łukasz Mendakiewicz is a software engineer at Microsoft, where he focuses on the customer experience with parallel programming models for C++. He is especially interested in GPGPU acceleration, and puts this passion to work on C++ AMP. He holds an M.S. in Computer Science from AGH UST in Krakow, Poland with the thesis on implementing real-time global illumination algorithms on a GPU.

This session provides insight on how to obtain superior performance for computational finance workloads without compromising developer productivity. C++ AMP technology lets you write C++ STL like code that runs on GPUs (and CPUs) in a platform (Windows and Linux) and vendor agnostic manner. The session will start with an overview of C++ AMP, dive into C++ AMP features, list various compilers that support C++ AMP and showcase the performance characteristics of options pricing workloads written using C++ AMP code. Attend this talk to see how you can write productive and easy to maintain code that offers superior performance. Thereby delivering the ability to write productivity code once and exploit the hardware to its fullest.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Big Data Analytics & Data Algorithms; Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 10:00 - 10:25
Location: Room 210C

S4784 - Monte-Carlo Simulation of American Options with GPUs

Julien Demouth ( Developer Technology Engineer, NVIDIA )
Highly-Rated Speaker
Julien is a Developer Technology Engineer at NVIDIA where he works on the optimization of CUDA applications. Julien has a Ph.D. in Computer Science from the INRIA in France.

In that session we will present our work on the computation of the Greeks of multi-asset American options. We will describe our implementation of the Longstaff-Schwartz algorithm and explain the programming techniques used to obtain a very efficient code for the Andersen-QE path discretization. This solution was developed in collaboration with IBM and STAC and is used to calculate the Greeks in real-time on a single workstation with Tesla GPUs.

Session Level: Intermediate
Session Type: Talk
Tags: Finance

Day: Wednesday, 03/26
Time: 10:30 - 10:55
Location: Room 210C

S4227 - GPU Implementation of Explicit and Implicit Finite Difference Methods in Finance

Mike Giles ( Professor of Scientific Computing, University of Oxford )
Mike Giles
Prior to joining the faculty of Oxford University, Mike was an Assistant/Associate Professor at MIT. at Oxford, Mike is also a CUDA Fellow and Director of the Oxford University CUDA Center of Excellence.

This talk will explain how to achieve excellent performance with GPU implementations of standard explicit and implicit finite difference methods in computational finance. Implicit methods are much harder to implement efficiently, but the task is made easier through the development of library software for the solution of multiple tridiagonal systems in parallel. The implementation strategies depend on the size and dimensionality of the problems being solved. 1D problems can be solved within one SMX unit of a GPU, 2D problems usually require more than one SMX, and 3D / 4D problems require the entire GPU for their solution. Computational performance results will be given for Kepler GPUs, and the talk will also discuss whether single precision arithmetic provides sufficient accuracy.

Session Level: All
Session Type: Talk
Tags: Finance

Day: Wednesday, 03/26
Time: 14:00 - 14:50
Location: Room 210C

S4395 - Real-Time Quantification Filters for Multidimensional Databases

Peter Strohm ( Software Developer, Jedox AG )
Peter Strohm
Peter Strohm obtained his diploma in Computer Science from the University of Freiburg, Germany, in 2008. After that he joined the Inline Processing Team at Fraunhofer Institute for Physical Measurement Techniques IPM, Freiburg, as a software developer for parallel real-time applications. Since 2013, he has been with Jedox as a GPU developer.

Learn how GPUs can speed up real-time calculation of advanced multidimensional data filters required in data analytics and business intelligence applications. We present the design of a massively parallel "quantification" algorithm which, given a set of dimensional elements, returns all those elements for which ANY (or ALL) numeric cells in the respective slice of a user-defined subcube satisfy a given condition. Such filters are especially useful for the exploration of big data spaces, for zero-suppression in large views, or for top-k analyses. In addition to the main algorithmic aspects, attendees will see how our implementation solves challenges such as economic utilization of the CUDA memory hierarchy or minimization of threading conflicts in parallel hashing.

Session Level: Intermediate
Session Type: Talk
Tags: Big Data Analytics & Data Algorithms; Finance

Day: Wednesday, 03/26
Time: 14:30 - 14:55
Location: Room 210B

S4289 - Efficient Solution of Multiple Scalar and Block-Tridiagonal Equations

Endre László ( Ph.D. student, University of Oxford, Oxford e-Research Center )
Endre László is a visiting Ph.D. student at the University of Oxford, Oxford e-Research Center under the supervision of prof. Michael B. Giles. Finished MSc in 2010 in Electrical and Computer Engineering at Pazmany Peter Catholic University (PPCU-FIT) in Budapest, Hungary. Worked for a financial consultancy company and the Institute for Technical Physics and Materials Science, Hungarian Academy of Sciences. Started PhD in Parallel Computing at PPCU-FIT in 2011.

Many numerical methods require the solution of multiple independent tridiagonal systems. This talk will describe optimized methods for solving such systems, considering both the case where the tridiagonal elements are scalar, and the case where they are composed of square blocks of dimension D, typically 3-8. For the scalar case very good performance is achieved using a combination of the Thomas algorithm and parallel cyclic reduction. In the block case it is shown that good performance can be achieved by using D cooperating threads, all within the same warp.

Session Level: Advanced
Session Type: Talk
Tags: Numerical Algorithms & Libraries; Finance; Computational Physics

Day: Wednesday, 03/26
Time: 15:00 - 15:25
Location: Room LL20D

S4655 - Efficient Lifetime Portfolio Sensitivities: AAD Versus Early-Start Longstaff-Schwartz Compression

Chris Kenyon ( Director, Quantitative Research – CVA / FVA, Lloyds Banking Group )
Chris Kenyon
Chris Kenyon is a Director in the Quantitative Research – CVA / FVA team at Lloyds Bank. Previously he was head quant for counterparty credit risk globally at Credit Suisse, and at DEPFA Bank PLC he was Head of Structured Credit Valuation (post crisis), working on pricing model development, and validation. He has also held positions at IBM Research, and Schlumberger where he applied real options pricing to everything from offshore rig lease extension options to variable volume outsourcing contracts. Chris holds a PhD in Applied Mathematics from Cambridge University where he was a Research Fellow (Computer Modeling), and has an MSc in Operations Research from the University of Austin, Texas.
Andrew Green ( Head of Quantitative Research - CVA / FVA, Lloyds Banking Group )
Andrew Green (Head of Quantitative Research – CVA / FVA) has headed the Quantitative Research – CVA / FVA team at Lloyds Bank for the last five years and is responsible for the development of models for credit and funding valuation adjustments. Prior to joining Lloyds, he headed the DCRM Quant team at Barclays Capital with responsibility for CVA model development. During his career as a quantitative analyst, Andrew has worked on fixed income, equity, credit and hybrid derivative models. Andrew holds a DPhil and BA in Physics from Oxford University and the Certificate in Advanced Study in Mathematics (Part III) from Cambridge University.

Developments in financial regulation (Dodd-Frank, Basel III) emphasize capital adequacy. Efficient lifetime portfolio VaR-based capital calculations for trading decisions are highly computationally challenging. The major impediment to widespread GPU adoption has been the need for multiple code-bases, however the Xcelerit middleware solves this. We give a single source CPU/GPU approach for highly-efficient lifetime portfolio sensitivity calculations. This talk introduces Early-Start Longstaff-Schwartz Compression (ES-LSC) which replaces (Automatic) Algorithmic Differentiation (AAD) that we demonstrate is technically unsuitable after t=0. Longstaff-Schwartz is a state-space method for pricing which we also apply to non-American derivatives as a compression technique. Early-Start means simulations (GPU/CPU) start from the past so the state space is available both at t=0 and all later times for VaR calculations for capital pricing (or IM). State space regressions provide sensitivities either analytically, or by finite difference.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Numerical Algorithms & Libraries

Day: Wednesday, 03/26
Time: 15:00 - 15:50
Location: Room 210C

S4471 - High Speed Analysis of Big Data Using NVIDIA GPUs and Hadoop

Partha Sen ( COO, Fuzzy Logix )
Partha Sen
Partha has a passion for solving complex business problems using quantitative methods, data mining and pattern recognition. For a period of about 12 years from 1995 to 2007, Partha pursued this passion as a hobby and developed about 100 algorithms and over 700 quantitative models. These algorithms and models are the basis for the solutions being implemented by Fuzzy Logix today. Before founding Fuzzy Logix, Partha worked at Bank of America where he held senior management positions in the commercial and investment bank and in the portfolio strategies group. In the commercial and investment bank, Partha led the initiative to build a quantitative model driven credit rating methodology for the entire commercial loan portfolio. The methodology is used by the bank for allocating reserves against potential losses from loans. In the portfolio strategies group, Partha led a team to devise various strategies for effectively hedging the credit risk for the bank's commercial loan portfolio and for minimizing the impact of mark-to-market volatility of the portfolio of hedging instruments (Credit Default Swaps, Credit Default Swaptions, and CDS Indexes). Partha was also responsible for managing the Quantitative Management Associate Program at Bank of America. This is a two-year associate development program which has groomed over 75 quantitative managers within the enterprise. Prior to working at Bank of America, Partha held managerial positions at Ernst and Young and Tata Consultancy Services. He has a Bachelor of Engineering, with a major in computer science and a minor in mathematics from the Indian Institute of Technology. He also has an MBA from Wake Forest University.

Performing analytics on data stored in Hadoop can be time consuming. While Hadoop is great at ingesting and storing data, getting timely insight out of the data can be difficult which reduces effectiveness and time-to-action. The use of NVIDIA GPUs to accelerate analytics on Hadoop is an optimal solution that drives high price to performance benefits. In this session, we'll demonstrate a solution using NVIDIA GPUs for the analysis of big data in Hadoop. The demo will show how you can leverage the Hadoop file system, it's map reduce architecture and GPUs to run computationally intense models bringing together both data and computational parallelism. Methods demonstrated will include classification techniques such as decision trees, logistic regression and support vector machines and clustering techniques like k means, fuzzy k means and hierarchical k means on marketing, social and digital media data.

Session Level: Intermediate
Session Type: Talk
Tags: Big Data Analytics & Data Algorithms; Finance; Bioinformatics & Genomics; Recommended Press Session – HPC-Science

Day: Wednesday, 03/26
Time: 15:30 - 15:55
Location: Room 210B

S4536 - An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management

Yigal Jhirad ( Head of Quantitative Strategies and Risk Management , Cohen & Steers )
Yigal  Jhirad
Yigal D. Jhirad, Senior Vice President, is Director of Quantitative Strategies and a Portfolio Manager for Cohen & Steers’ options and real assets strategies. Mr. Jhirad heads the firm’s Investment Risk Committee. He has 26 years of experience. Prior to joining the firm in 2007, Mr. Jhirad was an executive director in the institutional equities division of Morgan Stanley, where he headed the company’s portfolio and derivatives strategies effort. He was responsible for developing, implementing and marketing quantitative and derivatives products to a broad array of institutional clients, including hedge funds, active and passive funds, pension funds and endowments. Mr. Jhirad holds a BS from the Wharton School. He is a Financial Risk Manager (FRM), as Certified by the Global Association of Risk Professionals. He is based in New York.
Blay Tarnoff ( Senior Software Engineer, Cohen & Steers )
Blay Tarnoff
Blay Tarnoff is a senior applications developer and database architect. He specializes in array programming and database design and development. He has developed equity and derivatives applications for program trading, proprietary trading, quantitative strategy, and risk management. He is currently a consultant at Cohen & Steers and was previously at Morgan Stanley.

This session discusses the convergence of parallel processing and big data in finance as the next step in evolution of risk management and trading systems. We advocate a risk management approach in finance should evolve from more traditional inter-day top down metrics to intra-day bottom up approach using signal generation and pattern recognition. We have also determined that parallel processing is a key tool to absorb greater insights into market patterns providing "trading DNA" and more effective tools to manage risk in real time.

Session Level: All
Session Type: Talk
Tags: Finance; Big Data Analytics & Data Algorithms; Numerical Algorithms & Libraries

Day: Wednesday, 03/26
Time: 16:00 - 16:50
Location: Room 210C

S4265 - RDMA GPU Direct for the Fusion-io ioDrive

Robert Wipfel ( Fellow, Fusion-io )
Robert started his career at a parallel processing startup, and then at INMOS, worked on a distributed operating system for the Transputer. Robert next helped Unisys and Intel jointly enter the commercial parallel processing market. He worked on single system image Unix and Oracle Parallel Server. At Novell Robert was an architect or engineering lead for various Data Center products that integrated clustering, virtualization, and network storage. His work on management products combined web-scale automation, process orchestration and a federated CMDB to create IT as a Service. Robert joined Fusion-io as an architect and helped the company deliver its second generation ioMemory product line. He is presently chief architect for the ION Data Accelerator all-Flash SCSI storage appliance.

Learn how to eliminate I/O bottlenecks by integrating Fusion-io's ioDrive Flash storage into your GPU applications. The first part of this session is a technical overview of Fusion-io's PCIe attached ioDrive. The second part presents developer best practices and tuning for GPU applications using ioDrive based storage. Topics will cover threading, pipe-lining, and data path acceleration via RDMA GPU Direct. Demos and example code showing integration between RDMA GPU Direct and Fusion-io's ioDrive will be given.

Session Level: Intermediate
Session Type: Talk
Tags: Performance Optimization; Finance; Big Data Analytics & Data Algorithms

Day: Wednesday, 03/26
Time: 16:30 - 17:20
Location: Room 212B

S4199 - Effortless GPU Models for Finance

Ben Young ( Senior Software Engineer, SunGard )
Ben Young
Ben Young is a senior developer working across the Adaptiv product range. He has been at SunGard for over eight years and has been looking at Adaptiv Analytics performance as part of his work for the last six years or so

Learn how SunGard provides support for GPUs, such that both SunGard engineers, and quantitative developers at our clients have to make only trivial code changes to exploit both the CPU and GPU to full effect.

Session Level: Intermediate
Session Type: Talk
Tags: Finance

Day: Wednesday, 03/26
Time: 17:00 - 17:25
Location: Room 210C

S4376 - Hybridizer: Develop in Dot Net - Debug and Execute on GPU

Florent Duguet ( Founder, Altimesh )
Florent Duguet graduated Ph.D. in Computer Graphics in 2005. He implemented solutions for front office and risk departments of investment banks, with an exclusive focus on GPGPU since 2007; starting from the proof of concept leading up to production. He founded Altimesh in 2008, in an effort to reduce the learning curve of GPU computing for high-level language developpers. The outcome is the Hybridizer, which enables many-core computing in high-level programming environments such as dot net.

GPU computing performance and capabilities have improved at an unprecedented pace. CUDA dramatically reduced the learning curve to GPU usage for general purpose computing. The Hybridizer takes a step further in enabling GPUs in other development ecosystems (C#, java, dot net) and execution platforms (Linux, Windows, Excel). Transforming dot net binaries into CUDA source code, the Hybridizer is your in house GPU guru. With a growing number of features including virtual functions, generics and more, the Hybridizer also offers number of coding features for the multi- and many-core architectures, while making use of advanced optimization features like AVX and ILP.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 17:30 - 17:55
Location: Room 210C

S4583 - Middleware Framework Approach for BigData Analytics Using GPGPU

Ettikan Kandasamy Karuppiah ( Principal Researcher , MIMOS Bhd )
Ettikan KK, (Ph.D in the area of Distributed Computing) is the Principal Researcher and Head of Accelerative Technology Lab of ICT Division @MIMOS. Current research interest includes Big/Media Data Processing, Multi-processors, GPGPU & FPGA and Network Processing. Previously he was attached with Panasonic R&D {Panasonic Corporate Research Arm} as Principal Engineer and Group Manager of Panasonic Kuala Lumpur Lab with R&D responsibility in IP, AV, distributed and embedded communications protocols in the Home Networking/Network Processing products. Prior to Panasonic, he was with Intel Communication Group responsible for Network Processor related R&D. He has numerous international patents, publication and directly involved in world commercial product developments in those organizations. (ettikan.org)

Current application of GPU processors for parallel computing tasks shows excellent results in terms of speed-ups compared to CPU processors. However, there is no existing middleware framework that enables automatic distribution of data and processing across heterogeneous computing resources for structured and unstructured BigData applications. Thus, we propose a middleware framework for 'Big Data' analytics that provides mechanisms for automatic data segmentation, distribution, execution, information retrieval across multiple cards (CPU & GPU) and machines, a modular design for easy addition of new GPU kernels at both analytic and processing layer, and information presentation. The architecture and components of the framework such as multi-card data distribution and execution, data structures for efficient memory ac-cess, algorithms for parallel GPU computation and results for various test con-figurations are shown. Our results show proposed middleware framework pro-vides alternative and cheaper HPC solution to users.

Session Level: Intermediate
Session Type: Talk
Tags: Big Data Analytics & Data Algorithms; Video & Image Processing; Finance

Day: Thursday, 03/27
Time: 09:00 - 09:25
Location: Room 210B

S4360 - Monte Carlo Calibration to Implied Volatility Surface: A New Computational Paradigm

Chuan-Hsiang Han ( Associate Professor, National Tsing-Hua University )
Chuan-Hsiang Han
Chuan-Hsiang is: Associate Professor. Department Quantitative Finance, National Tsing-Hua University, Taiwan; Adjunct Associate Professor. Department of Mathematics, National Taiwan University; Co-director of NVIDIA-NTHU Joint Lab on Computational Finance; Software Developer of Volatility Information Platform (VIP) and Director of Taiwan Financial Engineers and Traders. Association

This presentation offers a new possibility that Monte Carlo simulation is capable of fast solving the calibration problem of implied volatility surfaces. Dimension separation and standard error reduction constitute the two-stage procedure. The first stage aims to reduce dimensionality of the solving optimization problem by utilizing the Fourier transform representation of the volatility dynamics. The second stage provides a high performance computing paradigm for option pricing by standard error reduction. GPU a parallel accelerating device drastically increases the total number of simulations in addition to variance reduction algorithms. In virtue of its flexibility, this two-stage Monte Carlo method is applied to estimate various volatility models such as hybrid models and multiscale stochastic volatility models.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Big Data Analytics & Data Algorithms

Day: Thursday, 03/27
Time: 09:30 - 09:55
Location: Room 210C

S4777 - The Esther Solution for XVA Mega-Models: An In-Memory Architecture Built Around the K10 As An Algebraic Engine

Claudio Albanese ( CEO, Global Valuation Ltd )
Claudio Albanese
Claudio graduated with a Ph.D. in Physics from ETH Zurich and held faculty positions at UCLA, NYU, Princeton, Toronto and Imperial College in Physics, Mathematical Physics and Mathematical Finance. He is currently the CEO of Global Valuation Ltd., a London based software firm specializing in high performance computing solutions for OTC portfolio simulations.

Mega-models denoted by three-letter acronyms such as CVA/FVA/DVA (collectively XVA) have sprouted on the wave of the banking reform and represent a major challenge to the traditional cluster computing paradigm of parallelism. The Esther architecture is an innovative solution breaking new ground in this space. Esther is the first in-memory risk analytics engine running on large memory servers. It is based on new Mathematics built from the ground up with the objective of capturing bottlenecks in matrix multiplication logic handled by K10 multi-GPU engines. It achieves unparalleled levels of performance on standard XVA metrics and grants access to new classes of hard XVA metrics for massive portfolios. Applications include interactive pre-trade XVA analytics, capital simulation for balance-sheet optimization and waterfall modelling at Clearing Houses.

Session Level: All
Session Type: Talk
Tags: Finance; Supercomputing

Day: Thursday, 03/27
Time: 10:00 - 10:25
Location: Room 210C

S4291 - Heavy Parallelization of Alternating Direction Schemes in Multi-Factor Option Valuation Models

Cris Doloc ( Founder & Principal, Quantras Research Ltd. )
Highly-Rated Speaker
Cris Doloc
Cris Doloc holds a Ph.D. in Computational Physics and he has spent the last 25 years in the field of Computational Engineering, specifically in Thermonuclear Fusion research, Algorithmic Trading and Financial Engineering. Cris is the founder of Quantras Research, a Technology R&D firm specialized in developing advanced technologies for the Financial Markets, as well as state-of-the-arts Algorithmic trading tools for Proprietary Trading groups and Hedge Funds. Currently Cris is responsible for overseeing the design of the global architecture and the implementation of the valuation infrastructure that provides theoretical data to proprietary trading desks. His main area of expertise is in architecting and developing enterprise level systems for trading, valuation and risk.

Learn how to use the latest GPU technology to substantially improve the performance of numerical implementations for Alternating Direction schemes. These numerical methods are used in pricing problems associated with high-dimensional PDEs where the use of more common Finite Difference techniques, like Crank-Nicholson are very challenging and inefficient. The Alternating Direction schemes both Implicit and Explicit, are unconditionally stable and very efficient second-order methods in both space and time variables. The suggested GPU implementation of the heavy parallelized Alternating Direction scheme provides a significant increase in performance over the CPUs when dealing with multi-factor exotic derivatives like barrier or rainbow options. The goal of this session is to offer an interesting insight into how technology savvy Trading firms could use the latest GPU architecture to improve the efficiency of real-time risk control while reducing the costs associated with their technology infrastructure.

Session Level: All
Session Type: Talk
Tags: Finance

Day: Thursday, 03/27
Time: 14:00 - 14:50
Location: Room 210C

S4154 - Pricing American Options with Least Square Monte Carlo simulations on GPUs

Massimiliano Fatica ( Manager, Tesla Performance Group, NVIDIA )
Massimiliano Fatica
Massimiliano Fatica is a manager of the Tesla Performance Group at NVIDIA where he works in the area of GPU computing (high-performance computing and clusters). He holds a laurea in Aeronautical Engineering and a Phd in Theoretical and Applied Mechanics from the University of Rome "La Sapienza". Prior to joining NVIDIA, he was a research staff member at Stanford University where he worked at the Center for Turbulence Research and Center for Integrated Turbulent Simulations on applications for the Stanford Streaming Supercomputer.

This talk will present a CUDA implementation of the Least Square Monte Carlo method by Longstaff and Schwartz to price American options on GPUs. We will examine all the details of the implementation, from the random number and paths generations to the Least Square estimation of the continuation value. The implementation can price a put option with 200,000 paths and 50 time steps in less than 10 ms on a Tesla K20X.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Numerical Algorithms & Libraries

Day: Thursday, 03/27
Time: 15:00 - 15:25
Location: Room 210C

S4557 - Accelerating Option Risk Analytics in R Using GPUs

Matthew Dixon ( Term Assistant Professor of Analytics, University of San Francisco )
Matthew Dixon
Matthew is a term assistant professor of analytics in the School of Management and Department of Computer Science at the University of San Francisco. He is also a consulting director of risk for HedgeFacts, LLP, a portfolio analytics and fund administration platform for hedge funds. In addition to holding academic appointments as Krener Assistant Professor at UC Davis and postdoctoral researcher at Stanford University, Matthew has worked and consulted for various investment banks and the Bank for International Settlements on quantitative risk methodology. He serves on the Global Association of Risk Professionals’ San Francisco chapter committee and co-chairs the workshop on high performance computational finance at SC, the International Conference for High Performance Computing, Networking, Storage and Analysis.

Learn how to combine the convenience of the R Statistical Software Package with the computational resources provided by GPUs to accelerate computationally intensive financial computations exhibiting high degrees of parallelism. In this talk, we describe ongoing work towards the development of a R library providing GPU optimized computationally intensive kernels frequently appearing in option risk analytics applications. Such kernels are bottlenecks in a work-flow which is often highly dependent on a rich set of numerical and statistical functionality native to R. This functionality may be difficult to replicate outside of R. We demonstration the utility of our approach to the intra-day calibration of the Bates stochastic volatility jump-diffusion models, often used for risk analysis of equity derivatives. The combined performance gain from rewriting the error function in C++ and deploying the computations on a NVIDIA Tesla K20c (Kepler architecture) is approximately 760x. Detailed results will be presented during the talk.

Session Level: Intermediate
Session Type: Talk
Tags: Finance; Numerical Algorithms & Libraries

Day: Thursday, 03/27
Time: 15:30 - 15:55
Location: Room 210C

S4175 - GPUs in Quantitative Asset Management

Daniel Egloff ( Partner and Managing Director, Incube Advisory and QuantAlea )
Highly-Rated Speaker
In 2008 Daniel Egloff set up his own software engineering and consulting company and founded QuantAlea by the end of 2009. Since then he has advised several high profile clients on quantitative finance, software development and high performance computing. In 2012 he joined InCube Advisory to further strengthen their consulting capabilities, continuing to manage QuantAlea as a software engineering provider. Over the last few years he has become a well-known expert in GPU computing and parallel algorithms and successfully applied GPUs in productive systems for derivative pricing, risk calculations and statistical analysis. Before setting up his own company he had spent more than fifteen years in the financial service industry, where his work revolved around derivative pricing, risk management with a special focus on market and credit risk, and high performance computing on clusters and grids. He studied mathematics, theoretical physics and computer science at the University of Zurich and the ETH Zurich, and has a PhD in mathematics from the University of Fribourg, Switzerland.

Modern portfolio theory, initially developed by Harry Markowitz, has been used in the industry for several decades to construct optimal portfolios, which properly balance risk and return. In recent years more refined quantitative methods have been developed to improve asset allocations and create optimal portfolios in a more stable and robust manner. We will discuss some of these new ideas and explain where large-scale numerical problems appear and how they can be solved with special algorithms on GPUs. You will learn how GPUs can help to consistently blend historical data and expert views in order to obtain more robust and realistic inputs for portfolio optimization, either with Bayesian techniques or with the minimum discrimination information principle, and how back-testing can be brought to a new level of sophistication.

Session Level: Intermediate
Session Type: Talk
Tags: Finance

Day: Thursday, 03/27
Time: 16:00 - 16:25
Location: Room 210C

S4407 - Incremental Risk Charge With cuFFT: A Case Study of Enabling Multi Dimensional Gain with Few GPUs

Amit Kalele ( Associate Consultant, Tata Consultancy Services Limited )
Amit Kalele is presently working as Associate Consultant with TCS at Center of Excellence for optimization and parallelization. Prior to TCS Amit was working as Scientist at Computational Research laboratory. His research areas are HPC, parallel computing, computational aspect in finance, cryptography and cryptanalysis. Amit Kalele has a PhD in Electrical Engineering from Indian Institute of Technology Bombay, Mumbai India.
Manoj Nambiar ( Principal Scientist, Tata Consultancy Services Limited )
Manoj Nambiar is currently working with TCS as a Principal Scientist, heading the Performance Engineering Research Center (PERC). He also leads the Parallelization and Optimization Centre of excellence as a part of the company’s HPC Initiative. Until 2011, Manoj has been working as a research lead in High Performance Messaging, Networking and Operating Systems in PERC. Prior to this has executed several consulting assignments in the performance engineering area specializing in network and systems performance. Manoj has a B.E from the University of Bombay, and a post graduate diploma in VLSI design from C-DAC, India.

GPUs are well suited for massively parallel problems but many a times users have a dilemma of adoption due to limited memory bandwidth between host and device. The problem of Incremental Risk Charge calculation was posed to us by one of our customer. This proof of concept demonstrates that GPUs with cuFFT library and multi-stream computations not only enables speedy performance but also achieves substantial reduction in hardware footprint and energy consumption. These gains cannot be overlooked by any business unit. This study is also helpful in taking an informed decision for choosing the right technology for business use.

Session Level: Beginner
Session Type: Talk
Tags: Finance; Numerical Algorithms & Libraries

Day: Thursday, 03/27
Time: 16:30 - 16:55
Location: Room 210C

Talk