Overview

GPU Programming with CUDA and Tensor Cores: Harness Parallel Processing for AI, Machine Learning, and High-Performance Applications Unlock the full potential of GPU computing with this hands-on guide to CUDA programming and Tensor Core acceleration. Whether you are an engineer, researcher, data scientist, or advanced developer, this book gives you the knowledge and practical techniques to build high-performance applications that scale. Inside, you'll master the foundations of CUDA programming, learn how to manage threads, warps, and memory hierarchies, and understand how to design efficient parallel algorithms. You'll also discover how Tensor Cores accelerate matrix operations and mixed precision training, enabling breakthroughs in deep learning, AI, and scientific simulation. Key topics include: Writing efficient CUDA kernels and optimizing GPU memory usage Performing reductions, scans, and parallel matrix operations Leveraging cuBLAS, cuDNN, cuFFT, and NCCL libraries for performance Implementing real-world workloads like image classification, object detection, and large-scale scientific simulations Profiling, debugging, and scaling applications across multiple GPUs Benchmarking CPU vs GPU vs Tensor Core performance for critical insights Through detailed explanations, authentic code examples, and real-world case studies, this book bridges the gap between theory and practice giving you the tools to design, implement, and optimize GPU-powered solutions across AI, machine learning, data science, and HPC. If you are serious about mastering GPU acceleration and want to stay ahead in AI and high-performance computing, this is the essential resource you need. Take the next step in your development journey, equip yourself with the skills to build faster, smarter, and more scalable applications. Get your copy today and harness the power of CUDA and Tensor Cores!

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