Accelerating Option Risk Analytics in R Using GPUs PDF Download
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Author: Matthew Francis Dixon
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
In this paper we describe our work on speeding up the Heston stochastic volatility model calibration, a financial application, on GPUs. The Heston volatility model is used extensively across the capital markets to price and measure the market risk of exchange traded financial options. When fitted to sub-minute level market mid-price quotes, the model may require frequent calibration every few minutes. The R statistical software package is easy to use and is popular with quantitative analysts in the financial industry. However, a typical R based implementation of the Heston Model calibration on a CPU does not meet the performance requirements for sub-minute level trading, i.e. mid to high frequency trading. The calibration of a Heston model is performed over M option data points which remains fixed during the calibration computation. A typical organization of this computation involves calling an optimization routine with a pointer to ErrorFunction() which estimates the error between market observed and model option prices. We implemented the calibration computation in R and observed that the computation time is dominated by the calculation of the ErrorFunction(). This paper describes the implementation of a GPU optimized kernel for this computation that can be called by the R script performing the calibration process. For M=1024 we demonstrate a factor of 760x improvement in the overall calibration time over the R sequential implementation by off-loading ErrorFunction() on a system with an Intel Core i5 processor and NVIDIA Tesla K20c (Kepler architecture) consisting of 2496 cores. Note that not all the performance gain is due to the GPU - partly it is due to the reduction in the overhead of R for the Heston model calculation. For comparison we also implemented the calibration code using C. We observed a speed up of 230x for the GPU based implementation over the C version indicating that a factor of 3.4x improvement is due to avoiding the R overhead for the Heston Model Calculation. However, the overall calibration time using R based optimization routines combined with the GPU off-loaded ErrorFunction() is comparable to a C GPU based calibration code.
Author: Matthew Francis Dixon
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
In this paper we describe our work on speeding up the Heston stochastic volatility model calibration, a financial application, on GPUs. The Heston volatility model is used extensively across the capital markets to price and measure the market risk of exchange traded financial options. When fitted to sub-minute level market mid-price quotes, the model may require frequent calibration every few minutes. The R statistical software package is easy to use and is popular with quantitative analysts in the financial industry. However, a typical R based implementation of the Heston Model calibration on a CPU does not meet the performance requirements for sub-minute level trading, i.e. mid to high frequency trading. The calibration of a Heston model is performed over M option data points which remains fixed during the calibration computation. A typical organization of this computation involves calling an optimization routine with a pointer to ErrorFunction() which estimates the error between market observed and model option prices. We implemented the calibration computation in R and observed that the computation time is dominated by the calculation of the ErrorFunction(). This paper describes the implementation of a GPU optimized kernel for this computation that can be called by the R script performing the calibration process. For M=1024 we demonstrate a factor of 760x improvement in the overall calibration time over the R sequential implementation by off-loading ErrorFunction() on a system with an Intel Core i5 processor and NVIDIA Tesla K20c (Kepler architecture) consisting of 2496 cores. Note that not all the performance gain is due to the GPU - partly it is due to the reduction in the overhead of R for the Heston model calculation. For comparison we also implemented the calibration code using C. We observed a speed up of 230x for the GPU based implementation over the C version indicating that a factor of 3.4x improvement is due to avoiding the R overhead for the Heston Model Calculation. However, the overall calibration time using R based optimization routines combined with the GPU off-loaded ErrorFunction() is comparable to a C GPU based calibration code.
Author: Michele Chambers
Publisher: Pearson Education
ISBN: 0133498603
Category : Business & Economics
Languages : en
Pages : 337
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Book Description
Advanced Analytics Methodologies is today's definitive guide to analytics implementation for MBA and university-level business students and sophisticated practitioners. Its expanded, cutting-edge coverage helps readers systematically "jump the gap" between their organization's current analytical capabilities and where they need to be. Step by step, Michele Chambers and Thomas Dinsmore help readers customize a complete roadmap for implementing analytics that supports unique corporate strategies, aligns with specific corporate cultures, and serves unique customer and stakeholder communities. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore provide advanced applications and examples not available elsewhere, describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. They show how to: Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy Link key business objectives with core characteristics of your organization, value chain, and stakeholders Take advantage of game changing opportunities before competitors do Effectively integrate the managerial and operational aspects of analytics Measure performance with dashboards, scorecards, visualization, simulation, and more Prioritize and score prospective analytics projects Identify "Quick Wins" you can implement while you're planning for the long-term Build an effective Analytic Program Office to make your roadmap persistent Update and revise your roadmap for new needs and technologies This advanced text will serve the needs of students and faculty studying cutting-edge analytics techniques, as well as experienced analytics leaders and professionals including Chief Analytics Officers; Chief Data Officers; Chief Scientists; Chief Marketing Officers; Chief Risk Officers; Chief Strategy Officers; VPs of Analytics or Big Data; data scientists; business strategists; and many line-of-business executives.
Author: Michele Chambers
Publisher: Pearson Education
ISBN: 0133498581
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
Many organizations now understand the gap between their current analytical capabilities and where they need to be. Far fewer organizations know how to overcome that gap, monetize analytics, and fully capitalize on Big Data. Modern Analytics Methodologies helps you customize a complete roadmap for implementing analytics that supports your strategy, aligns with your culture, and is unique for your organization. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. Writing for both professionals and students, they show how to: Leverage the convergence of macro trends ranging from "flattening" and "green" to Big Data and machine learning Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy Link key business objectives with core characteristics of your organization, value chain, and stakeholders Take advantage of game changing opportunities before competitors do Effectively integrate the managerial and operational aspects of analytics Measure performance with dashboards, scorecards, visualization, simulation, and more Prioritize and score prospective analytics projects Identify "Quick Wins" you can implement while you're planning for the long-term Build an effective Analytic Program Office to make your roadmap persistent Update and revise your roadmap for new needs and technologies
Author: Meikang Qiu
Publisher: Springer Nature
ISBN: 3030341399
Category : Computers
Languages : en
Pages : 426
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Book Description
This book constitutes the refereed proceedings of the 4th International Conference on Smart Computing and Communications, SmartCom 2019, held in Birmingham, UK, in October 2019. The 40 papers presented in this volume were carefully reviewed and selected from 286 submissions. They focus on both smart computing and communications fields and aimed to collect recent academic work to improve the research and practical application in the field.
Author: Matt Pharr
Publisher: Addison-Wesley Professional
ISBN: 9780321335593
Category : Computers
Languages : en
Pages : 814
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Book Description
More useful techniques, tips, and tricks for harnessing the power of the new generation of powerful GPUs.
Author: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 1119903858
Category : Mathematics
Languages : en
Pages : 612
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Book Description
MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. readers will also find: Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom Four new chapters, covering topics including Text Mining and Responsible Data Science An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook A guide to JMP Pro®’s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.
Author: Gregory Ruetsch
Publisher: Elsevier
ISBN: 0124169724
Category : Computers
Languages : en
Pages : 339
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Book Description
CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. To help you add CUDA Fortran to existing Fortran codes, the book explains how to understand the target GPU architecture, identify computationally intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance. All of this is done in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison. Leverage the power of GPU computing with PGI’s CUDA Fortran compiler Gain insights from members of the CUDA Fortran language development team Includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches Includes full source code for all the examples and several case studies Download source code and slides from the book's companion website
Author: Volodymyr Kindratenko
Publisher: Springer
ISBN: 3319065483
Category : Computers
Languages : en
Pages : 404
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Book Description
This book brings together research on numerical methods adapted for Graphics Processing Units (GPUs). It explains recent efforts to adapt classic numerical methods, including solution of linear equations and FFT, for massively parallel GPU architectures. This volume consolidates recent research and adaptations, covering widely used methods that are at the core of many scientific and engineering computations. Each chapter is written by authors working on a specific group of methods; these leading experts provide mathematical background, parallel algorithms and implementation details leading to reusable, adaptable and scalable code fragments. This book also serves as a GPU implementation manual for many numerical algorithms, sharing tips on GPUs that can increase application efficiency. The valuable insights into parallelization strategies for GPUs are supplemented by ready-to-use code fragments. Numerical Computations with GPUs targets professionals and researchers working in high performance computing and GPU programming. Advanced-level students focused on computer science and mathematics will also find this book useful as secondary text book or reference.
Author: Jay A. Etchings
Publisher: John Wiley & Sons
ISBN: 1119256186
Category : Medical
Languages : en
Pages : 466
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Book Description
An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.
Author: David B. Kirk
Publisher: Newnes
ISBN: 0123914183
Category : Computers
Languages : en
Pages : 519
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Book Description
Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing
