 Название: CUDA C++ Optimization: Coding Faster GPU Kernels
Автор: David Spuler
Издательство: Aussie AI Labs Pty Ltd.
Год: October 14, 2024
Страниц: 233
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
Increase the efficiency of CUDA C++ kernels for AI and high-performance computing on the powerful NVIDIA GPUs. Leverage your GPU investment with the power of an efficient software layer.
NVIDIA’s CUDA C++ environment is an incredible platform that allows the programmer to work at a much more productive level, far away from the low-level details of parallel programming on a GPU. But sometimes, you just can’t avoid getting back down into the boiler room to make things even faster! This book examines a variety of techniques for optimizing CUDA C++ programs, from beginner to advanced, along with a catalog of common CUDA C++ slugs to avoid.
CUDA is the software stack for NVIDIA GPUs and CUDA C++ is the programming language used to program them. But CUDA isn’t just a programming environment, but it’s a whole ecosystem of tools, libraries, and platforms for parallel programming. A monumental amount of work has gone into offering an amazing suite of C++ libraries for almost anything you could think of.
Optimizing CUDA C++ has some aspects that are the same as standard C++ efficiency techniques, but then there’s a whole gamut of extra techniques for fast parallelism and vectorization. The main advantage of CUDA C++ is that you can write these highly parallelized “kernels” to run on the GPU in a high-level language based on C++. This offers the benefits not only of speed, but also programmer productivity. CUDA C++ uses a “dual model” of programming whereby you write the two programs inside the same C++ code. There are two main environments that you need to code:
• Host code — runs on the CPU.
• Device code — runs on the GPU.
Both types of code are written in fully high-level C++ statements.
Main Topics:
- Speeding up CUDA C++ kernels
- Parallelization and vectorization
- Compute optimizations
- Memory access optimizations
Who This Book is For:
Anyone programming in CUDA C++ or trying to learn the language will benefit from better optimization skills! This book examines speedups in coding from beginner to advanced, starting with basic optimization techniques. In the later chapters, the book then covers a variety of advanced techniques for faster kernels, and a variety of common “slugs” that slow down CUDA programs.
Contents:
1. Parallel Programming
2. Optimizing CUDA Programs
3. Vectorization
4. AI Kernel Optimization
5. Profiling Tools
6. Compilers and Optimizers
7. Timing CUDA C++ Programs
8. Memory Optimizations
9. Coalescing and Striding
10. Data Transfer Optimizations
11. Heap Memory Allocation
12. Compute Optimizations
13. Warp Divergence
14. Grid Optimizations
15. Compile-Time Optimizations
16. Arithmetic Optimizations
17. Floating-Point Bit Tricks
18. Advanced Techniques
Appendix: CUDA C++ Slugs
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