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Advanced CUDA Programming

High Performance Computing with GPUs

Advanced CUDA Programming: High-Performance Computing with GPUs is the ultimate guide to unlocking the full power of modern GPU computing. Whether you're developing AI models, optimizing scientific simulations, or pushing real-time applications to their limits, this book delivers the advanced techniques and expert insights you need to achieve peak CUDA performance.

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NOTICE: "All Code for this book and many more is at github BurstBooksPublishing"

Advanced CUDA Programming: High-Performance Computing with GPUs is the ultimate guide to unlocking the full power of modern GPU computing. Whether you're developing AI models, optimizing scientific simulations, or pushing real-time applications to their limits, this book delivers the advanced techniques and expert insights you need to achieve peak CUDA performance.

GPU programming is no longer optional—it's a necessity in today's world of deep learning, AI acceleration, and high-performance computing. But simply writing CUDA kernels isn’t enough. To truly optimize GPU applications, you need a deep understanding of GPU architecture, memory hierarchies, execution models, and performance tuning strategies. This book takes you beyond the fundamentals and into the world of advanced CUDA programming, where efficiency, scalability, and raw computational power define success.

What You’ll Learn:

  • Deep GPU Architecture Insights – Explore the Ampere and Hopper architectures, including streaming multiprocessors, warp scheduling, and memory controller design.
  • Memory Optimization Techniques – Implement coalesced memory access, shared memory tuning, cache optimizations, and unified memory strategies for peak performance.
  • Asynchronous Execution & CUDA Streams – Master multi-stream processing, event-based synchronization, and pinned memory usage to maximize parallelism.
  • High-Performance Kernel Development – Learn thread block optimization, warp-level programming, and dynamic parallelism for efficient kernel execution.
  • AI & Deep Learning Acceleration – Optimize GEMM, convolution operations, mixed precision training, and inference using tensor cores.
  • Multi-GPU & Distributed Computing – Scale workloads across GPUs with P2P communication, NVLink, workload distribution, and MPI integration.
  • Real-Time Processing & Low-Latency Optimization – Develop real-time applications with deterministic execution, deadline scheduling, and pipeline optimizations.
  • Debugging & Profiling Mastery – Use Nsight Compute, CUDA-GDB, memory checking tools, and roofline analysis to fine-tune CUDA applications.

Why This Book?

This isn’t just another CUDA guide—it’s a masterclass in performance optimization. Packed with real-world case studies, hands-on techniques, and cutting-edge strategies, it delivers everything you need to develop fast, scalable, and production-ready GPU applications.

If you're ready to take your CUDA skills to the next level and maximize GPU performance like never before, this book is your roadmap. Don't leave performance on the table—start optimizing today.

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About the Author

gareth thomas

Gareth Morgan Thomas is a qualified expert with extensive expertise across multiple STEM fields. Holding six university diplomas in electronics, software development, web development, and project management, along with qualifications in computer networking, CAD, diesel engineering, well drilling, and welding, he has built a robust foundation of technical knowledge.

Educated in Auckland, New Zealand, Gareth Morgan Thomas also spent three years serving in the New Zealand Army, where he honed his discipline and problem-solving skills. With years of technical training, Gareth Morgan Thomas is now dedicated to sharing his deep understanding of science, technology, engineering, and mathematics through a series of specialized books aimed at both beginners and advanced learners.

Contents

Table of Contents

Chapter 1. Advanced CUDA Architecture Deep Dive

Section 1. Modern GPU Architecture

  • Ampere/Hopper Architecture Details
  • Streaming Multiprocessor Internals
  • Memory Controller Design

Section 2. Advanced Thread Execution Model

  • Warp Scheduling Mechanisms
  • Branch Prediction and Divergence
  • Instruction-Level Parallelism

Section 3. Memory System Internals

  • Cache Hierarchy Implementation
  • Memory Coalescing Mechanisms
  • L2 Cache Optimization Strategies

Chapter 2. Memory Management and Optimization

Section 1. Advanced Memory Patterns

  • Custom Memory Allocators
  • Memory Pool Implementation
  • Zero-Copy Memory Strategies

Section 2. Unified Memory Programming

  • Page Migration Engines
  • Prefetch Optimization
  • System-Wide Memory Access

Section 3. Memory Access Optimization

  • Bank Conflict Resolution
  • Shared Memory Access Patterns
  • Cache Line Utilization

Chapter 3. CUDA Streams and Asynchronous Programming

Section 1. Advanced Stream Management

  • Multi-Stream Scheduling
  • Stream Priority Control
  • Event-Based Synchronization

Section 2. Asynchronous Memory Operations

  • Overlapping Data Transfers
  • Pinned Memory Usage
  • Asynchronous Prefetching

Section 3. Advanced Synchronization

  • Inter-Stream Dependencies
  • CPU-GPU Synchronization
  • Multi-GPU Coordination

Chapter 4. Advanced Kernel Development

Section 1. Thread Block Optimization

  • Dynamic Block Sizing
  • Occupancy-Driven Design
  • Resource Utilization

Section 2. Warp-Level Programming

  • Warp Primitives
  • Cooperative Groups
  • Shuffle Instructions

Section 3. Dynamic Parallelism

  • Recursive Kernel Launch
  • Parent-Child Synchronization
  • Resource Management

Chapter 5. Performance Optimization Techniques

Section 1. Instruction-Level Optimization

  • Assembly Analysis
  • PTX Optimization
  • Register Pressure Management

Section 2. Memory-Bound Optimization

  • Memory Access Patterns
  • Texture Memory Usage
  • Constant Memory Optimization

Section 3. Compute-Bound Optimization

  • Arithmetic Intensity
  • Thread Coarsening
  • Loop Unrolling Strategies

Chapter 6. Advanced Data Structures for GPUs

Section 1. GPU-Optimized Containers

  • Lock-Free Data Structures
  • Concurrent Hash Tables
  • Priority Queues

Section 2. Custom Memory Management

  • Slab Allocators
  • Memory Pools
  • Defragmentation Techniques

Section 3. Sparse Data Structures

  • Compressed Formats
  • Dynamic Updates
  • Efficient Traversal

Chapter 7. Scientific Computing Applications

Section 1. Linear Algebra Implementations

  • Custom BLAS Operations
  • Sparse Matrix Operations
  • Eigenvalue Solvers

Section 2. Numerical Methods

  • FFT Implementation
  • Differential Equations
  • Monte Carlo Methods

Section 3. Optimization Algorithms

  • Parallel Sort Implementation
  • Graph Algorithms
  • Numerical Optimization

Chapter 8. Machine Learning and AI Acceleration

Section 1. Deep Learning Primitives

  • Custom GEMM Implementation
  • Convolution Optimization
  • Tensor Core Programming

Section 2. Training Optimization

  • Mixed Precision Training
  • Memory-Efficient Training
  • Multi-GPU Training

Section 3. Inference Optimization

  • Quantization Techniques
  • Kernel Fusion
  • Batch Processing

Chapter 9. Multi-GPU Programming

Section 1. Multi-GPU Communication

  • P2P Communication
  • NVLink Optimization
  • Remote Memory Access

Section 2. Workload Distribution

  • Load Balancing Strategies
  • Memory Distribution
  • Synchronization Methods

Section 3. Distributed Computing

  • MPI Integration
  • Multi-Node Systems
  • Cluster Programming

Chapter 10. Advanced Debugging and Profiling

Section 1. Performance Analysis

  • Nsight Compute Usage
  • Roofline Analysis
  • Memory Access Patterns

Section 2. Advanced Debugging

  • CUDA-GDB Techniques
  • Memory Checking Tools
  • Race Detection

Section 3. Optimization Tools

  • Metrics Collection
  • Visual Profiler
  • Custom Profiling

Chapter 11. Real-time Processing

Section 1. Low-Latency Techniques

  • Kernel Scheduling
  • Memory Management
  • Pipeline Optimization

Section 2. Real-time Constraints

  • Deterministic Execution
  • Deadline Scheduling
  • Resource Management

Section 3. Stream Processing

  • Data Pipeline Design
  • Continuous Processing
  • Buffer Management

Chapter 12. Advanced Topics and Future Directions

Section 1. Emerging Technologies

  • Ray Tracing Cores
  • New Memory Technologies
  • Next-Gen Architecture

Section 2. Advanced Programming Models

  • Graph Programming
  • Quantum Simulation
  • Domain-Specific Languages

Section 3. Industry Applications

  • Case Studies
  • Performance Analysis
  • Best Practices

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