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FPGA Based Accelerators for Financial Applications

  • Book
  • © 2015

Overview

  • Provides a single-source reference for understanding benefits, risks and other effects of using FPGAs in finance
  • Describes a holistic approach to user requirements, algorithmic needs, system integration issues and low-level architectural details
  • Enables interdisciplinary solutions that typically require expertise from hardware engineers, software architects and IT managers to create a successful platform that is usable, flexible, fast and energy efficient

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Table of contents (11 chapters)

Keywords

About this book

This book covers the latest approaches and results from reconfigurable computing architectures employed in the finance domain. So-called field-programmable gate arrays (FPGAs) have already shown to outperform standard CPU- and GPU-based computing architectures by far, saving up to 99% of energy depending on the compute tasks. Renowned authors from financial mathematics, computer architecture and finance business introduce the readers into today’s challenges in finance IT, illustrate the most advanced approaches and use cases and present currently known methodologies for integrating FPGAs in finance systems together with latest results. The complete algorithm-to-hardware flow is covered holistically, so this book serves as a hands-on guide for IT managers, researchers and quants/programmers who think about integrating FPGAs into their current IT systems.

Editors and Affiliations

  • University of Kaiserslautern, Kaiserslautern, Germany

    Christian De Schryver

About the editor

Dr.-Ing. Christian De Schryver graduated in Information Technology in 2008 and received a PhD in Electrical Engineering in 2014, both from the University in Kaiserslautern, Germany. At this place, he is currently a Post-Doc and Senior Member of the Customized High Performance Computing (CHPC) research team within the Microelectronic Systems Design Research Group headed by Prof. Dr. Norbert Wehn. His research interests are design methodologies for application-tailored heterogeneous execution platforms, hardware accelerators for supercomputing applications (in particular finance and big data processing) and system-level design flows.

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