The document discusses Compute Unified Device Architecture (CUDA) and how graphics processing units (GPUs) can be used for general-purpose parallel computing. It notes that CPUs have limitations for parallel processing while GPUs are well-suited for parallel workloads due to their high number of threads. The document provides an example of a basic "Hello World" GPU program to illustrate GPU programming and architecture concepts like grids, blocks, and threads. It also mentions several programming languages and libraries that can be used to develop CUDA applications.

1. Compute Unified Device Architecture
Christophe Marchal | Software Architect

3. General-Purpose
Computation on Graphics
Hardware

5. Problem

7. Why is CPU not enough?

8. CPU Parallelism limitation
Low number of threads
High Frequency
Best for sequential computation

9. GPU
60 FPS
1980 x 1080 pixels
2 Multiprocessors: 384 CUDA Cores
Maximum number of threads: 4096

10. GPU parallelism
High number of threads
Low frequency
Best for PARALLEL computation

11. Adapt Algorithms

12. GPU Hello World
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1 2 3 4 5 6 7 8 9 10 11 12

13. Grid1
Block (0,0)
Thread (0,0,0) Thread (0,1,0)
Thread (1,0,0) Thread (1,1,0)
Block (0,1)
Thread (0,1,0)
Thread (0,0,0)
Thread (1,0,0) Thread (1,1,0)
Block (1,1)
Thread (0,1,0)
Thread (0,0,0)
Thread (1,0,0)
Thread (1,1,0)
Block (1,0)
Thread (0,0,0) Thread (0,1,0)
Thread (1,0,0) Thread (1,1,0)

14. Streaming
Multiprocessor
Streaming
Multiprocessor
Streaming
Multiprocessor
GPU
Block1
Block2
Block3
Block4

15. C++ Code

16. Global Computer Architecture
CPU
Memory RAM
GPU
GPU Memory

17. C++ Code

19. Other Programming languages

20. Python Code

21. Python Code

22. Libraries
Deep Neural Network
Interfaces para C, C++, Fortran, Java
and R
High Performance
Linear Algebra in R

23. Christophe Marchal | Software Architect