November 2018
Intermediate to advanced
310 pages
7h 54m
English
Overview
This book dives deep into the practical aspects of GPU programming, combining Python's simplicity with CUDA's power to enable high-performance applications. Readers will be guided step-by-step to understand GPU architecture, utilize CUDA libraries effectively, and optimize code for data-intensive tasks.
What this Book will help me do
- Understand the fundamentals and architecture of GPU programming using CUDA.
- Write efficient and optimized GPU kernels directly from Python.
- Use additional CUDA libraries like cuFFT and cuBLAS for enhanced functionality.
- Debug and profile CUDA code using tools like Nsight and Visual Profiler.
- Develop advanced GPU programming techniques for data science and machine learning.
Author(s)
None Tuomanen is an experienced developer and educator specializing in GPU programming and parallel computing. With a background in engineering and a passion for simplifying complex concepts, they bring clarity and depth to a technical subject through practical demonstrations and examples.
Who is it for?
This book is suited for developers, data scientists, and researchers who wish to harness the power of GPU programming for performance-critical applications. Readers should ideally have foundational knowledge in Python and programming with C-like languages. It also assumes a basic comprehension of mathematical and computational concepts. If you are keen on utilizing GPUs to transform your applications, this book is for you.
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