The part that deals with measuring the program's time was very helpful because it can be applied directly to other programs besides CUDA. The AXPY problems that follow are actually routines provided by BLAS. I thought that I could accelerate the codes I wrote with BLAS. It was a fun lecture!

wayfarecru0581

✅ Among the series of (1) to (6), (2) Simultaneous addition of vectors (1D arrays) in parallel ✅ 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

인프런 10주년! 사랑주간 전체 할인

CUDA Programming (2) - C/C++/GPU Parallel Computing - Vector Addition

Hello. 🌞 Thank you for your good review. I'm glad it helped you. Thank you. 🍀

커널

nvidia

입문

초급

중급이상

쿠다

CUDA

그래픽 처리 장치

Parallel Processing

C언어

10. elapsed time - measuring the execution time of a program

11. CUDA kernel launch - Execution of CUDA kernel function

12. vector addition - Adding 1-dimensional arrays (vectors)

13. Giga Addition - Giga Unit Vector Addition

14. AXPY and FMA - AXPY routines and FMA operations

15. thread and GPU - Thread and GPU structure

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