CUDA Programming with Julia

High-Performance GPU Computing for Scientific and Engineering Applications
Erscheinungsjahr 2027
ISBN: 978-3-032-44738-8
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This book introduces GPU computing using CUDA from the contemporary perspective of the Julia programming language. It is designed to empower scientists, engineers, and computational researchers to harness the massive parallelism of NVIDIA GPUs for solving high-performance numerical problems.

Readers will gain both theoretical and practical understanding of GPU architectures, CUDA kernels, memory hierarchies, and performance tuning strategies using the expressive and high-level syntax of Julia.

Main Emphasis:

• Practical GPU programming in Julia using CUDA.jl
• Deep integration of Julia’s scientific computing ecosystem with GPU workloads
• Performance optimization, memory tuning, and debugging techniques
• Real-world examples from numerical computing, image and signal processing, simulation, and machine learning

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Lektauers, Arnis

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Part I. Foundations of GPU Computing in Julia .- Chapter 1. Introduction to GPU Computing.- Chapter 2. Essential Julia Language Features for GPU Programming.- Chapter 3. GPU Arrays and High-Level Abstractions.- Chapter 4. Understanding the CUDA Programming Model.- Part II. Core GPU Programming Techniques .- Chapter 5. Writing Simple Julia CUDA Kernels.- Chapter 6. GPU Memory Management in Julia.- Chapter 7. Performance Tuning and Optimization.- Part III. Advanced CUDA and Portability.- Chapter 8. Advanced CUDA Features in Julia.- Chapter 9. Portable GPU Programming.- Chapter 10. Multi-GPU Programming.- Part IV Julia Ecosystem and Applications .- Chapter 11. Integrating CUDA Workflows with the Julia Ecosystem.- Chapter 12. Case Studies in Scientific and Engineering Applications.- Chapter 13. Writing Reusable CUDA.jl Libraries.- Appendices.- Index.

Über Autor(innen)

Arnis Lektauers is an Associate Professor and Senior Researcher at the Institute of Information Technology of Riga Technical University, Latvia. His research focuses on high-performance interactive hybrid simulation and its application to the analysis of complex systems in industrial, economic, ecological and sustainable development contexts.