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Low-Latency AI Trading Kernels in C++20: GPU Acceleration, FPGA Prototyping, and Execution Strategies for Deep RL and Ne, (Paperback)

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Management number 238724465 Release Date 2026/07/11 List Price US$20.00 Model Number 238724465
Category

Reactive Publishing<p>Master the engineering of ultra-low latency AI trading systems with this technical deep dive into modern C++20, GPU acceleration, and FPGA prototyping.</p><p>This book explores the complete pipeline for building high-performance trading kernels capable of executing deep reinforcement learning (Deep RL) and neural PDE policies at extreme speeds. You will examine production-grade implementations using C++20 features, CUDA and GPU optimization techniques, and FPGA-based acceleration strategies designed for sub-microsecond decision cycles in live markets.</p><p>Key topics include: </p><ul><li>Modern C++20 architectures for low-latency kernel design</li><li>GPU acceleration patterns for neural network inference in trading</li><li>FPGA prototyping workflows for custom hardware acceleration</li><li>Integration of Deep RL agents and neural PDE solvers into real-time execution engines</li><li>Memory management, concurrency, and deterministic performance optimizations</li></ul><p>Written for quantitative developers, high-frequency trading engineers, and AI systems programmers, this book provides detailed code examples, architectural diagrams, and practical implementation guidance for building next-generation low-latency trading infrastructure.</p><p><b>Ideal for readers with strong backgrounds in C++, GPU programming, and machine learning who want to push the boundaries of execution speed in algorithmic trading.</b></p>

  • Low-Latency AI Trading Kernels in C++20: GPU Acceleration, FPGA Prototyping, and Execution Strategies for Deep RL and Ne, (Paperback)
  • Author: Independently Published
  • ISBN: 9798180331328
  • Format: Paperback
  • Publication Date: 2026-06-06
  • Page Count: 530
Book format Paperback
Fiction/nonfiction Non-Fiction
Genre Business & Investing
Publication date June, 2026
Pages 530
Subgenre Finance
Series title No Series
Number in series 0
Edition 1
Publisher Amazon Digital Services LLC - Kdp
Language English
Is collectible N
Recording time 0 min
Retail packaging Single Piece
Assembled product dimensions (l x w x h) 6.00 x 1.31 x 9.00 in
Assembled product weight 1.39 lb
Bisac subject heading Business & Economics

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