cuda-samples/python/2_CoreConcepts/memoryResources

Sample: Memory Resources and Buffers (Python)

Description

This sample demonstrates the cuda.core memory management model: a MemoryResource owns a pool of memory and hands out Buffer objects that can be passed to kernels, copied between resources with Buffer.copy_to(), and viewed as NumPy or CuPy arrays through DLPack. The script exercises three common resources side-by-side:

1. DeviceMemoryResource - device-local GPU memory. Every Device exposes a default pool via Device.memory_resource, and applications can create additional pools explicitly.
2. PinnedMemoryResource - page-locked host memory, used here as the input and output staging buffers around a GPU kernel (the canonical pinned-H2D / compute / pinned-D2H pattern).
3. ManagedMemoryResource - unified memory that the driver migrates between host and device on demand; host views see the GPU's writes without an explicit copy.

The same scale_and_bias kernel runs on each resource and every result is verified on the host.

What You'll Learn

• Creating and using DeviceMemoryResource, PinnedMemoryResource, and ManagedMemoryResource
• Allocating Buffer objects from a resource with a bound stream
• Copying between buffers across resources with Buffer.copy_to()
• Taking zero-copy NumPy or CuPy views of a Buffer via DLPack
• Releasing buffers with stream-ordered close(stream) semantics

Key Libraries

• cuda.core - Pythonic access to CUDA runtime, programs, and memory resources
• cupy - GPU array views of device buffers
• numpy - host array views of pinned and managed buffers

Key APIs

From cuda.core

• Device.memory_resource - default memory pool attached to a device
• DeviceMemoryResource, PinnedMemoryResource, ManagedMemoryResource - allocate buffers of the corresponding memory kind
• MemoryResource.allocate(nbytes, stream=...) - returns a Buffer
• Buffer.copy_to(dst_buffer, stream=...) - async, stream-ordered copy
• Buffer.close(stream) - stream-ordered deallocation
• Buffer supports __dlpack__ for zero-copy views

From CuPy and NumPy

• cp.from_dlpack() / np.from_dlpack() - zero-copy array view of a Buffer

From cuda_samples_utils

• print_gpu_info() - print device name and compute capability

Requirements

Hardware

• NVIDIA GPU with Compute Capability 7.0 or higher
• Managed memory support (most discrete GPUs on Linux and Windows)

Software

• CUDA Toolkit 13.0 or newer (matches cuda-python 13.x)
• Python 3.10 or newer
• cuda-python (>=13.0.0)
• cuda-core (>=0.6.0)
• cupy-cuda13x (>=13.0.0)

Installation

Install the required packages from requirements.txt:

cd /path/to/cuda-samples/python/2_CoreConcepts/memoryResources
pip install -r requirements.txt

The requirements.txt installs:

• cuda-python (>=13.0.0)
• cuda-core (>=0.6.0)
• cupy-cuda13x (>=13.0.0)

How to Run

Basic usage

cd cuda-samples/python/2_CoreConcepts/memoryResources
python memoryResources.py

With custom parameters

# Larger buffer size
python memoryResources.py --elements 1048576

# Use a specific GPU
python memoryResources.py --device 1

Expected Output

Device: <Your GPU Name>
Compute Capability: <X.Y>

[1] DeviceMemoryResource + PinnedMemoryResource (staging)
  Pinned staging, device kernel, and copy_to verified

[2] ManagedMemoryResource (unified memory)
  GPU writes observed directly through the host-visible mapping

[3] Explicit DeviceMemoryResource
  Explicit DeviceMemoryResource allocation verified

All memory resource demos passed.

Note: Device name and compute capability will vary based on your GPU.

Files

• memoryResources.py - Python implementation using cuda.core memory resources
• README.md - This file
• requirements.txt - Sample dependencies
• ../../Utilities/cuda_samples_utils.py - Common utilities (imported by this sample)

See Also

• CUDA Python Documentation
• cuda.core memory API
• Upstream cuda.core example: memory_ops.py
• Upstream cuda.core example: memory_pool_resources.py