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aeab82ff30
- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
131 lines
3.4 KiB
Markdown
131 lines
3.4 KiB
Markdown
# Sample: Vector Addition (Python)
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## Description
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Run your first GPU kernel: add two vectors element-wise on the GPU using the [`cuda.core`](https://nvidia.github.io/cuda-python/cuda-core/latest/) API with runtime compilation.
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## What You'll Learn
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- Writing CUDA kernels in C++ with template support
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- Runtime compilation of CUDA kernels from Python
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- Using `cuda.core` for device management, programs, and launches
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- Configuring and launching kernels with grid and block dimensions
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- Using CuPy for GPU memory management
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- Verifying GPU results against CPU computation
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## Key Libraries
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- [`cuda.core`](https://nvidia.github.io/cuda-python/cuda-core/latest/) — Pythonic access to CUDA runtime and compilation
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- `cupy` — GPU array library for Python
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## Key APIs
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### From `cuda.core`
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- `Device` — Initialize and manage CUDA device
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- `Program` — Create program from kernel source code
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- `ProgramOptions` — Set compilation options (C++ standard, architecture)
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- `LaunchConfig` — Configure kernel launch parameters
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- `launch` — Execute kernel on specified stream
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Import stable symbols from the top-level package (not `cuda.core.experimental`). See the [cuda.core documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/).
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### From CuPy
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- `cp.random.rand()` — Generate random arrays on GPU
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- `cp.empty()` — Allocate uninitialized GPU arrays
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- `cp.allclose()` — Verify results with tolerance
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### From `cuda_samples_utils`
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- `verify_array_result()` — Verify computation results
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## Kernel Techniques
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- **1D Grid-Stride Loop** — Handle arbitrary array sizes with fixed grid
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- **Template Programming** — Generic kernel for different data types
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- **Bounds Checking** — Prevent out-of-bounds memory access
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## Requirements
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### Hardware
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- NVIDIA GPU with Compute Capability 7.0 or higher
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- Minimum GPU memory: 512 MB
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### Software
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- CUDA Toolkit 13.0 or newer (matches `cuda-python` 13.x)
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- Python 3.10 or newer
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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- `cupy-cuda13x` (>=13.0.0)
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## Installation
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Install the required packages from requirements.txt:
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```bash
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cd /path/to/cuda-samples/python/1_GettingStarted/vectorAdd
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pip install -r requirements.txt
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```
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The requirements.txt installs:
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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- `cupy-cuda13x` (>=13.0.0)
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## How to Run
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### Basic usage
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```bash
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cd samples/python/1_GettingStarted/vectorAdd
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python vectorAdd.py
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```
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### With custom parameters
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```bash
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# Custom vector size
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python vectorAdd.py --elements 1000000
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# Use specific GPU
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python vectorAdd.py --device 1
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# Skip verification for benchmarking
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python vectorAdd.py --no-verify
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```
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## Expected Output
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```
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[Vector addition using CUDA Core API]
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Device: <Your GPU Name>
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Compute Capability: sm_<XX>
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Compiling kernel 'vectorAdd<float>'...
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Kernel compiled successfully
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[Vector addition of 50000 elements]
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CUDA kernel launch with 196 blocks of 256 threads
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Verifying result...
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Test PASSED
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Done
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```
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**Note:** Device name and compute capability will vary based on your GPU.
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## Files
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- `vectorAdd.py` — Python implementation using cuda.core API
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- `README.md` — This file
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- `requirements.txt` — Sample dependencies
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- `../../Utilities/cuda_samples_utils.py` — Common utilities (imported by this sample)
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## See Also
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- [CUDA Python Documentation](https://nvidia.github.io/cuda-python/)
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- [cuda.core API](https://nvidia.github.io/cuda-python/cuda-core/latest/)
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- [CuPy Documentation](https://docs.cupy.dev/)
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