GPU computing

Video

Watch this in our courses: 2022 February, 2021 January

Abstract

  • Request a GPU with the Slurm option --gres=gpu:1 (some clusters need -p gpu or similar)

  • Do not use srun in your batch script, there’s a bug that prevents job step’s access to the GPU.

  • If you use Python, generally don’t load your own CUDA module unless you know you need this. Instead, install what you need through anaconda.

  • Select a certain type of GPU with e.g. --constraint='kepler' (see the quick reference for names).

  • Monitor GPU performance with sacct -j JOBID -o comment -p.

  • For development, run jobs of 4 hours or less, and they can run quickly in the gpushort queue.

  • If you aren’t fully sure of how to scale up, contact us Research Software Engineers early.

Introduction

GPUs, short for graphical processing unit, are massively-parallel processors that are optimized to perform parallel operations. Computations that might take days to run on CPUs, take substantially less time on GPUs. This speed-up specially comes in handy when dealing with large amounts of data, e.g. in machine learning/deep learning tasks, which is why GPUs have become an indispensable tool in the research community.

The programs we normally write in common programming languages, e.g. C++ are executed by the CPU. We need to explicitly communicate with the GPU if we want GPU to execute the program. That is, upload the program and the input data to the GPU, and transfer the result from the GPU to the main memory. What enable this procedure are programming environments designed to communicate with GPUs in such a manner. An example of such an API is CUDA which is the native programming interface for NVIDIA GPUs.

On Triton, we have a large number of NVIDIA GPU cards from different generations and currently only support CUDA. Triton GPUs are not the typical desktop GPUs, but specialized research-grade server GPUs with large memory, high bandwidth and specialized instructions, that are constantly increasing in number. For scientific purposes, they generally outperform the best desktop GPUs.

See also

Please ensure you have read Interactive jobs and Serial Jobs before you proceed with this tutorial.

GPU jobs

To request GPUs on Slurm, you should use the --gres option either in your batch script or as a command-line argument to your interactive job. Used with a SBATCH directive in a batch script, exactly one GPU is requested as follows. :

#SBATCH --gres=gpu:1

You can request as many GPUs as you’d like using #SBATCH --gres=gpu:N wherein N denotes the number of the requested GPUs.

Note

Most of the time, using more than one GPU isn’t worth it, unless you specially optimize, because communication takes too much time. It’s better to parallelize by splitting tasks into different jobs.

You can restrict yourself to a certain type of GPU card by using using the --constraint option. For example, to restrict to Kepler generation (K80s), use --constraint='kepler' or only Pascal or Volta generations with --constraint='pascal|volta' (Remember to use the quotes since | is the shell pipe)

There is a gpushort partition with a time limit of 4 hours that often has space (like with other partitions, this is automatically selected for short jobs). As of early 2022, it has four Tesla P100 cards in it (view with slurm partitions | grep gpushort). If you are doing testing and development and these GPUs meet your needs, you may be able to test much faster here.

Available machine learning frameworks

We support many common machine learning frameworks out of the box:

  • Tensorflow: module load anaconda. See the Tensorflow page for info on older versions.

  • Keras: module load anaconda

  • PyTorch:module load anaconda

Please note that most of the pre-installed softwares have CUDA already present. Thus you do not need to load CUDA as a seperate module when loading these. See the application list for more details.

Compiling CUDA-based code

To compile CUDA-based code for GPUs, you need to load the relevant cuda module. You can see what versions of CUDA is available using module spider:

$ module spider cuda

When submitting a batch script, you need to load the cuda module, compile your code, and subsequently run the executable. An example of such a submission script is shown below wherein the output of the code is written to a file named helloworld.out in the current directory:

#!/bin/bash
#SBATCH --time=00:05:00
#SBATCH --job-name=helloworld
#SBATCH --mem-per-cpu=500M
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --output=helloworld.out

module load cuda
nvcc helloworld.cu -o helloworld
./helloworld

Note

If you ever get libcuda.so.1: cannot open shared object file: No such file or directory, this means you are attempting to use a CUDA program on a node without a GPU. This especially happens if you try to test GPU code on the login node, and happens (for example) even if you try to import the GPU tensorflow module in Python on the login node.

Examples

Simple Tensorflow/Keras model

Let’s run the MNIST example from Tensorflow’s tutorials:

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

The full code for the example is in tensorflow_mnist.py. One can run this example with srun:

wget https://raw.githubusercontent.com/AaltoSciComp/scicomp-docs/master/triton/examples/tensorflow/tensorflow_mnist.py
module load anaconda
srun --time=00:15:00 --gres=gpu:1 python tensorflow_mnist.py

or with sbatch by submitting tensorflow_mnist.sh:

#!/bin/bash
#SBATCH --gres=gpu:1
#SBATCH --time=00:15:00

module load anaconda

python tensorflow_mnist.py

Do note that by default Keras downloads datasets to $HOME/.keras/datasets.

Simple PyTorch model

Let’s run the MNIST example from PyTorch’s tutorials:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

The full code for the example is in tensorflow_mnist.py. One can run this example with srun:

wget https://raw.githubusercontent.com/AaltoSciComp/scicomp-docs/master/triton/examples/pytorch/pytorch_mnist.py
module load anaconda
srun --time=00:15:00 --gres=gpu:1 python pytorch_mnist.py

or with sbatch by submitting pytorch_mnist.sh:

#!/bin/bash
#SBATCH --gres=gpu:1
#SBATCH --time=00:15:00

module load anaconda

python pytorch_mnist.py

The Python-script will download the MNIST dataset to data folder.

Monitoring efficient use of GPUs

When running a GPU job, you should check that the GPU is being fully utilized.

When your job has started, you can ssh to the node and run nvidia-smi. You can find your process by e.g. using htop and inspect the GPU-Util column. It should be close to 100%.

Once the job has finished, you can use slurm history to obtain the jobID and run:

$ sacct -j JOBID -o comment -p

This also shows the GPU utilization.

Note

There are factors to be considered regarding efficient use of GPUs. For instance, is your code itself efficient enough? Are you using the framework pipelines in the intended fashion? Is it only using GPU for a small portion of the entire task? Amdahl’s law of parallelization speedup is relevant here.

If the GPU utilization of your job is low, you should check whether its CPU utilization is close to 100% with seff JOBID. This can indicate that the CPUs are trying to keep the GPU occupied with calculations, but the lack of CPU performance will cause a bottleneck on the GPU utilization.

Please keep in mind that when using a GPU, you need to also request enough CPUs to supply the data to the process. So, you can increase the number of CPUs you request so that enough data is provided for the GPU. However, you shouldn’t request too many: There wouldn’t be enough CPUs for everyone to use the GPUs, and they would go to waste (all of our nodes have 4-6 CPUs for each GPU).

Input/output

Deep learning work is intrinsically very data-hungry. Remember what we said about storage and input/output being important before (Data storage)? This matter becomes very important when working with GPUs. In fact, faster memory bandwidth is the main improvement of our server-grade GPUs compared to desktop models.

If you are loading big amounts of data, you should package the data into a container format first; lots of small files are your worst enemy. Each framework has a way to do this efficiently in a whole pipeline.

See also

Please refer to the small files page for more detailed information.

If your data consists of individual files that are not too big, it is a good idea to have the data stored in one file, which is then copied to nodes ramdisk /dev/shm or temporary disk /tmp.

If your data is too big to fit in the disk, we recommend that you contact us for efficient data handling models.

Available GPUs and architectures

Card

Slurm feature name (--constraint=)

Slurm gres name (--gres=gpu:NAME:n)

total amount

nodes

architecture

compute threads per GPU

memory per card

CUDA compute capability

Tesla K80*

kepler

teslak80

12

gpu[20-22]

Kepler

2x2496

2x12GB

3.7

Tesla P100

pascal

teslap100

20

gpu[23-27]

Pascal

3854

16GB

6.0

Tesla V100

volta

v100

40

gpu[1-10]

Volta

5120

32GB

7.0

Tesla V100

volta

v100

40

gpu[28-37]

Volta

5120

32GB

7.0

Tesla V100

volta

v100

16

dgx[1-7]

Volta

5120

16GB

7.0

Tesla A100

ampere

a100

28

gpu[11-17]

Ampere

7936

80GB

8.0

AMD MI100 (testing)

mi100

Use -p gpu-amd only, no --gres

gpuamd[1]

Exercises

The scripts you need for the following exercises can be found in this git repository: hpc-examples. You can clone the repository by running git clone https://github.com/AaltoSciComp/hpc-examples.git. This repository will be used for most of the tutorial exercises.

GPU-1: Test nvidia-smi

Run nvidia-smi on a GPU node with srun. Use slurm history to check which GPU node you ended up on. Try setting a constraint to force a different GPU architecture.

GPU-2: Running a script

Run one of the samples given above. Try using sbatch as well.

GPU-3: Test compiling CUDA

Load cuda and gcc (version less than 9) modules and compile the gpu/pi.cu example using nvcc. Run it. Does it say zero? Try running it with a GPU and see what happens.

(advanced) GPU-4: Local job files

(Advanced) The PyTorch example will try to load datasets from a folder called data in a local folder. Modify the Slurm script so that the script:

  1. Creates an unique folder in /dev/shm or $TMPDIR before running the Python code.

  2. Moves to this folder when job is running.

  3. Runs the PyTorch-example from this location. Verify that the datasets are stored in the local disk.

HINT: Check out mktemp --help, command output substitutions section from our Linux shell tutorial and the API page for Python’s os.environ.

See also

What’s next?

We go on to Parallel computing.