Low-Latency AI Trading Kernels in C++20 : GPU Acceleration, FPGA Prototyping, and Execution Strategies for Deep RL and Neural PDE Policies

James Preston

ISBN 13: 9798180331328

Published by Independently Published Jun 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798180331328

Bibliographic Details

• Title: Low-Latency AI Trading Kernels in C++20 : GPU Acceleration, FPGA Prototyping, and Execution Strategies for Deep RL and Neural PDE Policies
• Publisher: Independently Published Jun 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798180331328
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 33D millimeters 229 millimeters width by 152 millimeters height by 33 millimeters depth
• Weight: 634 grams

About this title

Synopsis

Reactive Publishing

Master the engineering of ultra-low latency AI trading systems with this technical deep dive into modern C++20, GPU acceleration, and FPGA prototyping.

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.

Key topics include:

• Modern C++20 architectures for low-latency kernel design
• GPU acceleration patterns for neural network inference in trading
• FPGA prototyping workflows for custom hardware acceleration
• Integration of Deep RL agents and neural PDE solvers into real-time execution engines
• Memory management, concurrency, and deterministic performance optimizations

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.

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.