Sample: Numpy vs. Cupy (Python)

Description

This sample demonstrates performance comparison between NumPy (CPU) and CuPy (GPU) for matrix multiplication operations. It benchmarks the execution time of matrix dot products on both CPU and GPU, showing the performance benefits of GPU acceleration for numerical computations.

What you will learn

• How to set up and use CuPy for GPU-accelerated numerical computations.
• How to benchmark NumPy vs CuPy performance for matrix operations.
• How to transfer data between CPU (NumPy) and GPU (CuPy) memory using cp.asarray().
• How to use CUDA device management with the cuda-core library.
• How to validate computational results between CPU and GPU implementations using np.testing.assert_allclose().
• How to handle GPU warmup to avoid first-run overhead in benchmarking.
• How to create and manage explicit CUDA streams with device.create_stream().
• How to properly cleanup streams with stream.close() in try/finally blocks.
• How to access GPU device information (name, compute capability).
• How to create timing context managers for performance measurement using CUDA events.

Key libraries

• numpy
• cupy
• cuda-core

Key APIs

From cuda.core:

• Device() – Get CUDA device object for specific GPU
• device.create_stream() – Create explicit CUDA stream
• stream.close() – Close and cleanup stream resources

Requirements

1. NVIDIA Graphics Card with CUDA support
2. CUDA Drivers installed on your system
3. CUDA Toolkit installed on your system
4. Python 3.12 or newer

Install packages:

pip install -r requirements.txt

How to run

Basic usage:

# Pre-steps:
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# Run from the Python directory:
cd /path/to/numpyVsCupy/Python
python -m 1_GettingStarted.numpyVsCupy.numpyVsCupy

With custom parameters:

python -m 1_GettingStarted.numpyVsCupy.numpyVsCupy --n_size 5000

Command line arguments

• --n_size, -n: Size of the matrix (n * n) for benchmarking (default: 4096)

Expected Output

Validation PASSED: NumPy and CuPy results match within tolerance
Demo completed successfully!

Files

• numpyVsCupy.py – Python implementation
• README.md – This file
• requirements.txt – Required packages