17-19th May 2017, at CSC - IT Center for Science
| Day 1 | |
|---|---|
| 9:00 - 9:30 | Introduction to GPUs |
| 9:00 - 9:30 | Introduction to GPU programming |
| 9:30 - 10:30 | CUDA Programming 1 |
| 10:30 - 10:45 | Coffee Break |
| 10:45 - 11:15 | Exercises |
| 11:15 - 12:00 | Tools |
| 12:00 - 12:45 | Lunch |
| 12:45 - 13:15 | Exercises |
| 13:15 - 14:00 | Cuda Programming II |
| 14:00 - 14:30 | Exercises |
| 14:30 - 14:45 | Coffee |
| 14:00 - 16:00 | Exercises |
| Day 2 | |
|---|---|
| 9:00 - 10:00 | CUDA Programming III |
| 10:00 - 10:15 | Coffee |
| 10:15 - 12:00 | Exercises |
| 12:00 - 12:45 | Lunch |
| 12:45 - 13:45 | Kernel optimization |
| 13:45 - 14:30 | Exercises |
| 14:30 - 14:45 | Coffee Break |
| 14:45 - 15:15 | CUDA Programming IV |
| 15:15 - 16:00 | Exercises |
| Day 3 | |
|---|---|
| 9:00 - 9:45 | Dynamic parallelism |
| 9:45 - 10:15 | Coffee |
| 10:15 - 11:30 | Exercises |
| 11:30 - 12:00 | Multi-GPU programming |
| 12:00 - 13:00 | Lunch |
| 13:00 - 14:30 | Exercises |
| 14:30 - 15:00 | MPI and CUDA |
| 15:00 - 16:00 | Exercises |
All exercises except the multi-GPU and MPI labs can be done using the local classroom workstations. You may also use the Taito-GPU partition of Taito cluster.
The exercises are in this repository in two different folders. In exercises are a set of exercises prepared by CSC.
To get a local copy of the exercises you can clone the repository
git clone https://github.com/csc-training/cuda.git
This command will create a folder called CUDA where all the
materials are located. If the repository is updated during the course
you can update your local copy of it with a git pull command.
Classroom workstations have Quadro K600 GPUs and CUDA SDK 8.0. The GPUs have a compute capability of 3.0
Use nvcc -arch=sm_30 <source>.cu to compile your program.
Useful flags:
| Flag | Description |
|---|---|
-arch=sm_xx |
Shorthand for comping for specific architecure, replace xx with the compute capability targeted |
-lineinfo |
Add the source code into the ptx output, enables mapping from ptx to kernel code, needed for better debugging and profiling |
-Xptxas="-v" |
Make the ptx assembler output the register and shared memory usage for the compiled file |
-Xptxas="-dlcm=ca" |
Will enable loads through L1 cache for the entire compiled file |
-use_fast_math |
Replaces some single precision math functions with faster lower precision ones |
-rdc=true |
Compiles relocatable device code, needed for among other things dynamic parallelism |
-lcudart |
Links the cuda runtime, use for when linking nvcc compiled code with other compilers than nvcc |
You can log into Taito-GPU
front-end node using ssh command ssh -Y [email protected]
where XXX is the number of your training account. You can also
use your own CSC account if you have one.
Login node does not have a GPU, so all CUDA programs have to be run
on the compute nodes. Serial jobs can be run with srun command like this
srun -n1 -pgpu --gres=gpu:1 ./my_program
You can extend the default time limit for you job with -t option.
For example, option -t15 will allow you job to run for 15 minutes.
This couse has a resource reservation that can be used for the exercises.
You can run your job within the reservation with --reservation option,
such as srun --reservation=cuda_wed -n1 -pgpu --gres=gpu:1 ./my_program.
Names of the reservations for Wednesday, Thursday and Friday are cuda_wed,
cuda_thu and cuda_fri respectively.