Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Practical CUDA Programming for Beginners: Build High-Performance GPU Applications with CUDA C++, Kernels, Parallel Programming, Memory Optimization, S

Practical CUDA Programming for Beginners: Build High-Performance GPU Applications with CUDA C++, Kernels, Parallel Programming, Memory Optimization, S

Ridge, George

Paperback

General ReferenceProgramming

ISBN13: 9798198491915
Publisher: Independently Published
Published: May 24 2026
Pages: 256
Weight: 0.99
Height: 0.54 Width: 7.00 Depth: 10.00
Language: English
Practical CUDA Programming for Beginners

Modern software is under pressure to process larger datasets, accelerate simulations, support AI workloads, analyze images, and deliver faster results without sacrificing accuracy. For developers working with compute-heavy applications, CUDA offers a powerful way to use NVIDIA GPUs for high-performance parallel computing.

Practical CUDA Programming for Beginners introduces CUDA C++ in a clear, hands-on way. It helps readers understand how GPUs work with CPUs, how kernels execute, and how parallel programming can transform performance-focused applications.

This book covers CUDA fundamentals, memory management, streams, profiling, optimization, and real-world GPU projects built around practical development workflows. Instead of relying on scattered code snippets or shallow theory, it teaches readers how to write correct GPU programs, validate results, measure performance honestly, identify bottlenecks, and avoid common CUDA mistakes.

You will learn how to avoid problems such as wrong indexing, poor memory access patterns, missing synchronization, misleading benchmarks, and race conditions. By focusing on correctness before speed, CPU-to-GPU workflow, profiling, and beginner-friendly optimization, this guide gives readers a practical foundation for GPU programming, high-performance computing, AI acceleration, scientific computing, engineering simulation, image processing, and large-scale data analysis.

Inside this book, you will learn how to:

  • Understand CUDA C++ from the ground up, including kernels, threads, blocks, grids, and GPU execution.
  • Set up a CUDA development environment and compile CUDA programs with NVCC.
  • Understand host memory, device memory, unified memory, memory transfers, and why data movement affects performance.
  • Launch GPU threads and map them correctly to real data.
  • Work with arrays, matrices, multidimensional data, reductions, atomics, shared memory, and race-condition prevention.
  • Measure CUDA performance correctly using CPU baselines, GPU timing, workflow comparisons, and profiling techniques.
  • Use CUDA streams, asynchronous execution, coalesced memory access, debugging, and optimization strategies.
  • Build practical CUDA projects involving vector addition, data normalization, matrix operations, optimized matrix multiplication, large dataset statistics, image processing, Monte Carlo simulation, and GPU data-processing pipelines.

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