It was good that you explained in detail how to solve the problem when the calculation speed is slower than expected even when calculating in parallel using CUDA, so that I could think about how to deal with CUDA in depth.

...So please limit the course period...ㅠ

hooha1207

✅ Out of the entire series (1) ~ (6), (3) CUDA memory hierarchy optimization ✅ NVIDIA GPU + CUDA programming is explained step by step from the basics. ✅ It processes arrays/matrices/image processing/statistical processing/sorting, etc. very quickly with parallel computing in C++/C language.

onemoresipofcoffee

CUDA Programming (3) - C/C++/GPU Parallel Computing - Memory Structure

커널

nvidia

입문

초급

중급이상

쿠다

CUDA

그래픽 처리 장치

Parallel Processing

C언어

16. matrix addition - adding matrices together

17. cuda Malloc 2D - CUDA-specific 2D memory allocation function

18. 3D filter - Apply 3D filter

19. memory hierarchy - CUDA memory hierarchy

20. adjacent differences - Finding the differences between adjacent elements

21. adjacent differences again - Finding differences between adjacent elements, added

00. Preparation before the lecture (duplicated with the end of Part 0)