Using GPUs with Python

Questions

• What is GPU acceleration?

• How to enable GPUs (for instance with CUDA) in Python code?

• How to deploy GPUs at HPC2N, UPPMAX, LUNARC, NSC, PDC and C3SE?

Objectives

• Get an intro to common schemes for GPU code acceleration

• Learn about the GPU nodes at HPC2N, UPPMAX, LUNARC, NSC, PDC, and C3SE

• Learn how to make a batch script asking for GPU nodes at HPC2N, UPPMAX, LUNARC, NSC, PDC, and C3SE

Introduction

In order to understand the capabilities of a GPU, it is instructive to compare a pure CPU architecture with a GPU based architecture. Here, there is a schemematics of the former:

Pure CPU architecture (single node). In the present case there are 256 cores, each with its own cache memory (LX). There is a shared memory (~378 GB/NUMA node) for all these cores. This is an AMD Zen4 node. The base frequency is 2.25 GHz, but it can boost up to 3.1 GHz.

As for the GPU architecture, a GPU card of type Ada Lovelace (like the L40s) looks like this:

Note: The AD102 GPU also includes 288 FP64 Cores (2 per SM) which are not depicted in the above diagram. The FP64 TFLOP rate is 1/64th the TFLOP rate of FP32 operations. The small number of FP64 Cores are included to ensure any programs with FP64 code operate correctly, including FP64 Tensor Core code. This is a single GPU engine of a L40s card. There are 12 Graphics Processing Clusters (GPCs), 72 Texture Processing Clusters (TPCs), 144 Streaming Multiprocessors (SMs), and a 384-bit memory interface with 12 32-bit memory controllers). On the diagram, each green dot represents a CUDA core (single precision), while the yellow are RT cores and the blue Tensor cores. The cores are arranged in the slots called SMs in the figure. Cores in the same SM share some local and fast cache memory.

The GPC is the dominant high-level hardware block. Each GPC includes a dedicated Raster Engine, two Raster Operations (ROPs) partitions, with each partition containing eight individual ROP units, and six TPCs. Each TPC includes one PolyMorph Engine and two SMs. Each SM contain 128 CUDA Cores, one Ada Third-Generation RT Core, four Ada Fourth-Generation Tensor Cores, four Texture Units, a 256 KB Register File, and 128 KB of L1/Shared Memory, which can be configured for different memory sizes depending on the needs of the graphics or compute workload.

In a typical cluster, some GPUs are attached to a single node resulting in a CPU-GPU hybrid architecture. The CPU component is called the host and the GPU part the device. One possible layout (Kebnekaise, AMD Zen4 node with L40s GPU) is as follows:

Schematics of a hybrid CPU-GPU architecture. A GPU L40s card is attached to a NUMA island which in turn contains 24 cores (AMD Zen4 CPU node with 48 cores total). The NUMA island and the GPUs are connected through a PCI-E interconnect which makes the data transfer between both components rather slow.

We can characterize the CPU and GPU performance with two quantities: the latency and the througput.

• Latency refers to the time spent in a sole computation.

• Throughput denotes the number of computations that can be performed in parallel. Then, we can say that a CPU has low latency (able to do fast computations) but low throughput (only a few computations simultaneously).

In the case of GPUs, the latency is high and the throughput is also high. We can visualize the behavior of the CPUs and GPUs with cars as in the figure below. A CPU would be compact road where only a few racing cars can drive whereas a GPU would be a broader road where plenty of slow cars can drive.

Cars and roads analogy for the CPU and GPU behavior. The compact road is analogous to the CPU (low latency, low throughput) and the broader road is analogous to the GPU (high latency, high throughput).

Not every Python program is suitable for GPU acceleration. GPUs process simple functions rapidly, and are best suited for repetitive and highly-parallel computing tasks. GPUs were originally designed to render high-resolution images and video concurrently and fast, but since they can perform parallel operations on multiple sets of data, they are also often used for other, non-graphical tasks. Common uses are machine learning and scientific computation were the GPUs can take advantage of massive parallelism.

Many Python packages are not CUDA aware, but some have been written specifically with GPUs in mind. If you are usually working with for instance NumPy and SciPy, you could optimize your code for GPU computing by using CuPy which mimics most of the NumPy functions. Another option is using Numba, which has bindings to CUDA and lets you write CUDA kernels in Python yourself. This means you can use custom algorithms. This is for NVidia GPUs. On AMD GPUs you would use HIP.

One of the most common use of GPUs with Python is for machine learning or deep learning. For these cases you would use something like Tensorflow or PyTorch libraries which can handle CPU and GPU processing internally without the programmer needing to do so. We will talk more about that later in the course.

GPUs on UPPMAX, HPC2N, LUNARC, NSC, PDC, and C3SE systems

There are generally either not GPUs on the login nodes or they cannot be accessed for computations. To use them you need to either launch an interactive job or submit a batch job.

UPPMAXHPC2NLUNARCNSCPDCC3SE

The new cluster Pelle has GPUs. L40s GPUs (up to 10 GPU cards) and H100 GPUs (up to 2 GPU cards).

You need to use this batch command (number of cards is depending on type):

• for L40s GPUs (up to 10 GPU cards):

#SBATCH -p gpu
#SBATCH --gpus=l40s:<number of GPUs>

• for H100 GPUs (up to 2 GPU cards):

#SBATCH -p gpu
#SBATCH --gpus=h100:<number of GPUs>

Numba example

Numba is installed on some of the centers as a module (HPC2N, LUNARC, and C3SE). For UPPMAX and NSC we’ll use virtual environments.

We are going to use the following program for testing on the machines (minus Dardel). It was taken from a (now absent) linuxhint.com exercise but there are also many great examples at https://numba.readthedocs.io/en/stable/cuda/examples.html):

Python example add-list.py using Numba

import numpy as np
from timeit import default_timer as timer
from numba import vectorize

# This should be a substantially high value.
NUM_ELEMENTS = 100000000

# This is the CPU version.
def vector_add_cpu(a, b):
  c = np.zeros(NUM_ELEMENTS, dtype=np.float32)
  for i in range(NUM_ELEMENTS):
      c[i] = a[i] + b[i]
  return c

# This is the GPU version. Note the @vectorize decorator. This tells
# numba to turn this into a GPU vectorized function.
@vectorize(["float32(float32, float32)"], target='cuda')
def vector_add_gpu(a, b):
  return a + b;

def main():
  a_source = np.ones(NUM_ELEMENTS, dtype=np.float32)
  b_source = np.ones(NUM_ELEMENTS, dtype=np.float32)

  # Time the CPU function
  start = timer()
  vector_add_cpu(a_source, b_source)
  vector_add_cpu_time = timer() - start

  # Time the GPU function
  start = timer()
  vector_add_gpu(a_source, b_source)
  vector_add_gpu_time = timer() - start

  # Report times
  print("CPU function took %f seconds." % vector_add_cpu_time)
  print("GPU function took %f seconds." % vector_add_gpu_time)

  return 0

if __name__ == "__main__":
  main()

As before, we need a batch script to run the code. There are no GPUs on the login node and even if there were we should not run long/heavy jobs there.

Note Type along!

UPPMAXUPPMAX (batch)HPC2NHPC2N (batch)LUNARC (batch)C3SE (batch)NSC: batch

Running a GPU Python code interactively on Pelle.

$[bbrydsoe@pelle2 ~] salloc -A uppmax2025-2-393 -t 00:30:00 -n 2 -p gpu --gpus=l40s:1
salloc: Pending job allocation 406444
salloc: job 406444 queued and waiting for resources
salloc: job 406444 has been allocated resources
salloc: Granted job allocation 406444
salloc: Waiting for resource configuration
salloc: Nodes p202 are ready for job
[bbrydsoe@p202 ~]$ module load Python/3.13.5 foss/2025b CUDA/13.0.2
[bbrydsoe@p202 ~]$ source /sw/arch/local/software/python/venvs/numba-gpu/bin/activate
[bbrydsoe@p202 ~]$ python add-list.py
CPU function took 17.475140 seconds.
GPU function took 0.134774 seconds.

Exercises

Integration 2D with Numba

An initial implementation of the 2D integration problem with the CUDA support for Numba could be as follows:

integration2d_gpu.py

from __future__ import division
from numba import cuda, float32
import numpy
import math
from time import perf_counter

# grid size
n = 100*1024
threadsPerBlock = 16
blocksPerGrid = int((n+threadsPerBlock-1)/threadsPerBlock)

# interval size (same for X and Y)
h = math.pi / float(n)

@cuda.jit
def dotprod(C):
    tid = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x

    if tid >= n:
        return

    #cummulative variable
    mysum = 0.0
    # fine-grain integration in the X axis
    x = h * (tid + 0.5)
    # regular integration in the Y axis
    for j in range(n):
        y = h * (j + 0.5)
        mysum += math.sin(x + y)

    C[tid] = mysum


# array for collecting partial sums on the device
C_global_mem = cuda.device_array((n),dtype=numpy.float32)

starttime = perf_counter()
dotprod[blocksPerGrid,threadsPerBlock](C_global_mem)
res = C_global_mem.copy_to_host()
integral = h**2 * sum(res)
endtime = perf_counter()

print("Integral value is %e, Error is %e" % (integral, abs(integral - 0.0)))
print("Time spent: %.2f sec" % (endtime-starttime))

Notice the larger size of the grid in the present case (100*1024) compared to the serial case’s size we used previously (10000). Large computations are necessary on the GPUs to get the benefits of this architecture.

One can take advantage of the shared memory in a thread block to write faster code. Here, we wrote the 2D integration example from the previous section where threads in a block write on a shared[] array. Then, this array is reduced (values added) and the output is collected in the array C. The entire code is here:

integration2d_gpu_shared.py

from __future__ import division
from numba import cuda, float32
import numpy
import math
from time import perf_counter

# grid size
n = 100*1024
threadsPerBlock = 16
blocksPerGrid = int((n+threadsPerBlock-1)/threadsPerBlock)

# interval size (same for X and Y)
h = math.pi / float(n)

@cuda.jit
def dotprod(C):
    # using the shared memory in the thread block
    shared = cuda.shared.array(shape=(threadsPerBlock), dtype=float32)

    tid = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
    shrIndx = cuda.threadIdx.x

    if tid >= n:
        return

    #cummulative variable
    mysum = 0.0
    # fine-grain integration in the X axis
    x = h * (tid + 0.5)
    # regular integration in the Y axis
    for j in range(n):
        y = h * (j + 0.5)
        mysum += math.sin(x + y)

    shared[shrIndx] = mysum

    cuda.syncthreads()

    # reduction for the whole thread block
    s = 1
    while s < cuda.blockDim.x:
        if shrIndx % (2*s) == 0:
            shared[shrIndx] += shared[shrIndx + s]
        s *= 2
        cuda.syncthreads()
    # collecting the reduced value in the C array
    if shrIndx == 0:
        C[cuda.blockIdx.x] = shared[0]

# array for collecting partial sums on the device
C_global_mem = cuda.device_array((blocksPerGrid),dtype=numpy.float32)

starttime = perf_counter()
dotprod[blocksPerGrid,threadsPerBlock](C_global_mem)
res = C_global_mem.copy_to_host()
integral = h**2 * sum(res)
endtime = perf_counter()

print("Integral value is %e, Error is %e" % (integral, abs(integral - 0.0)))
print("Time spent: %.2f sec" % (endtime-starttime))

Prepare a batch script to run these two versions of the integration 2D with Numba support and monitor the timings for both cases.

Solution for HPC2N

A template for running the python codes at HPC2N is here:

job-gpu.sh

#!/bin/bash
# Remember to change this to your own project ID after the course!
#SBATCH -A hpc2n2025-151
#SBATCH -t 00:08:00
#SBATCH -N 1
#SBATCH -n 24
#SBATCH -o output_%j.out   # output file
#SBATCH -e error_%j.err    # error messages
#SBATCH --gpus=1
#SBATCH -C l40s
#SBATCH --exclusive

ml purge > /dev/null 2>&1
ml GCC/12.3.0 Python/3.11.3 OpenMPI/4.1.5 SciPy-bundle/2023.07 CUDA/12.1.1 numba/0.58.1
python integration2d_gpu.py
python integration2d_gpu_shared.py

For the integration2d_gpu.py implementation, the time for executing the kernel and doing some postprocessing to the outputs (copying the C array and doing a reduction) was 4.35 sec. which is a much smaller value than the time for the serial numba code of 152 sec obtained previously.

The simulation time for the integration2d_shared.py implementation was 1.87 sec. by using the shared memory trick.

Solution for UPPMAX

A template for running the python codes at UPPMAX is here:

job-gpu.sh

#!/bin/bash
# Remember to change this to your own project ID after the course!
#SBATCH -A uppmax2025-2-393
# We are asking for 10 minutes
#SBATCH --time=00:10:00
# Asking for one GPU
#SBATCH -p gpu
#SBATCH --gpus=l40s:1
#SBATCH -o output_%j.out   # output file
#SBATCH -e error_%j.err    # error messages

# Remove any loaded modules and load the ones we need
ml purge > /dev/null 2>&1
module load Python/3.13.5 foss/2025b CUDA/13.0.2
source /sw/arch/local/software/python/venvs/numba-gpu/bin/activate

# Run your Python script

python integration2d_gpu.py
python integration2d_gpu_shared.py

Solution for LUNARC

A template for running the python codes at LUNARC is here:

job-gpu.sh

#!/bin/bash
# Remember to change this to your own project ID after the course!
#SBATCH -A lu2025-7-106
#SBATCH -t 00:15:00
#SBATCH -n 24
#SBATCH -o output_%j.out   # output file
#SBATCH -e error_%j.err    # error messages
#SBATCH --ntasks-per-node=1
#SBATCH -p gpua100
#SBATCH --gres=gpu:1
#SBATCH --exclusive

ml purge > /dev/null 2>&1
module load GCC/12.2.0  OpenMPI/4.1.4 Python/3.10.8 SciPy-bundle/2023.02 CUDA/12.1.1 numba/0.58.0

python integration2d_gpu.py
python integration2d_gpu_shared.py

Solution for NSC

A template for running the python codes at NSC is here:

job-gpu.sh

#!/bin/bash
# Remember to change this to your own project ID after the course!
#SBATCH -A naiss2025-22-934
#SBATCH -t 00:20:00
#SBATCH -n 24
#SBATCH --gpus-per-task=1
#SBATCH -o output_%j.out   # output file
#SBATCH -e error_%j.err    # error messages
#SBATCH --exclusive

ml purge > /dev/null 2>&1
ml Python/3.11.5-bare-hpc1-gcc-2023b-eb
ml buildenv-gcccuda/12.9.1-gcc11-hpc1

# If you are running this during the course, you can use the venv we have created for you
source /proj/courses-fall-2025/numba-gpu/bin/activate
# Otherwise, you have to create a virtual environment to use, then comment out the above
# Create a virtual environment to use. Do this before submitting the job
# cd /proj/courses-fall-2025/users/<mydir>
# module load Python/3.11.5-bare-hpc1-gcc-2023b-eb
# module load buildenv-gcccuda/12.9.1-gcc11-hpc1
# python3 -m venv numba-gpu
# source numba-gpu/bin/activate
# pip3 install --upgrade pip setuptools wheel
# pip3 install numba-cuda\[cu13\] numpy
# Then in the batch script, you load it
# source /proj/courses-fall-2025/users/<mydir>/numba-gpu/bin/activate
# Remove the comment of the above line if you created your own venv

python integration2d_gpu.py
python integration2d_gpu_shared.py

Solution for C3SE

A template for running the python codes at C3SE is here:

job-gpu.sh

#!/bin/bash
# Remember to change this to your own project ID!
#SBATCH -A naiss2025-22-934
#SBATCH -t 00:15:00
#SBATCH -p alvis
#SBATCH -N 1 --gpus-per-node=T4:4
#SBATCH -o output_%j.out   # output file
#SBATCH -e error_%j.err    # error messages

ml purge > /dev/null 2>&1
ml numba-cuda/0.20.0-foss-2025b-CUDA-12.9.1

python integration2d_gpu.py
python integration2d_gpu_shared.py

Keypoints

• You deploy GPU nodes via SLURM, either in interactive mode or batch

• In Python the numba package is handy

Important

• Of course, interactive mode could also be from inside Desktop on Demand, Jupyter, VScode, spyder …

• CUDA does not work directly on AMD GPUs, there hip is used instead.

• We will use GPUs more in the ML/DL section!

Additional information

• Numba documentation examples

• New York University CUDA/Numba lesson

• Hands-On GPU Programming with Python and CUDA : Explore High-Performance Parallel Computing with CUDA, Brian Tuomanen. Packt publishing.

• Parallel and High Performance Computing, Robert Robey and Yuliana Zamora. Manning publishing.