Introduction to accelerated computing

Set-up instructions

All you need to do is to login on one of the gpu-enabled lxplus9 machines :

lxplus901 / lxplus902 / lxplus903 / lxplus904 / lxplus905

ssh <username>@<systemname>.cern.ch

Then you can clone the repository :

git clone https://github.com/ckoraka/icsc-Intro-to-accel-comp.git
cd icsc-Intro-to-accel-comp

Getting familiar with the GPU

Lets check how many / what type of GPUs are available in the system. To do this, simply run :

nvidia-smi

Lets try to answer some questions :

1. How many GPUs does the system have?
2. What type of GPUs does the system have?
3. What is the GPUs global memory?

Exercise 1: "Hello world" with CUDA

During the lecture we saw a "Hello World" CUDA kernel. Lets try and run it ourselves! To compile and run the cuda_hello.cu CUDA script simply do :

nvcc cuda_hello.cu -o cuda_hello
./cuda_hello

Lets try and answer some questions :

1. What do you observe?
2. Why is this happening?
3. What can we do to fix this?

If you get stuck you can take a look at the cuda_hello.cu script in the solutions directory.

Exercise 2 : Matrix multiplication on the GPU

Goal of this excersise is to write a CUDA kernel that performs a 2-dimensional square matrix multiplication on the GPU.

• Start by taking a look at the file matrix_multiplication.cu.

• We can respresent the 2-D matrix in 1-D as shown in the image below. This will make copying the matrix from the host to device and from device to host easier. The size of each matrix is DSIZE*DSIZE.

• Find and properly update all parts of the script denoted with FIXME.

To compile and run the CUDA script you can do :

nvcc matrix_multiplication.cu -o matrix_multiplication
./matrix_multiplication

Lets try and answer some questions :

• Compare the time it takes to perform the matrix multiplication on the CPU and the GPU.
  • What do you observe? (Keep in mind that you are running on shared resources. You can try invoking the program a few times and then select the best run.)
• Try changing the size of the matrix by mutiplying DSIZE by 2,4, and 8.
  • What do you observe now? How does the CPU and GPU time scale?

If you get stuck you can take a look at the matrix_multiplication.cu script in the solutions directory.