Sample: Device Query (Python)

Description

Query and display detailed properties of all CUDA-capable devices in your system using the modern cuda.core API.

What You'll Learn

• How to enumerate CUDA devices in the system
• Using the cuda.core API for device management
• Querying comprehensive device properties (compute capability, memory, limits)
• Accessing low-level device attributes via cuda.bindings
• Checking peer-to-peer (P2P) access capabilities between GPUs

Key Libraries

• cuda.core - Modern CUDA Python API
• cuda.bindings - Low-level CUDA bindings for runtime and driver APIs

Key APIs

From cuda.core:

• Device.get_all_devices() - Get tuple of all available Device instances
• Device(device_id) - Get Device object for specific device ID
• system.get_driver_version() - Query CUDA driver version
• Device.set_current() - Set the current device for API calls
• Device.properties - Access comprehensive device properties
• Device.name - Get device name string
• Device.can_access_peer() - Check P2P access to peer device

From cuda.bindings.runtime:

• cudart.cudaRuntimeGetVersion() - Get CUDA runtime version
• cudart.cudaDeviceGetAttribute() - Query specific device attributes

From cuda.bindings.driver:

• cuda.cuMemGetInfo() - Get memory information for current device

Device Properties Queried

Compute Capabilities:

• Compute capability version (major.minor)
• Driver and runtime versions
• Number of multiprocessors and CUDA cores

Memory Information:

• Total global memory
• Memory clock rate and bus width
• L2 cache size
• Constant and shared memory sizes
• Maximum memory pitch

Execution Configuration Limits:

• Maximum threads per block and per multiprocessor
• Maximum block dimensions (x, y, z)
• Maximum grid dimensions (x, y, z)
• Warp size
• Registers per block

Texture Capabilities:

• Maximum texture dimensions (1D, 2D, 3D)
• Maximum layered texture sizes

Feature Support:

• Unified Addressing (UVA)
• Managed Memory
• Compute Preemption
• Cooperative Kernel Launch
• ECC support
• Host page-locked memory mapping
• Concurrent copy and kernel execution

System Information:

• PCI bus information
• Compute mode
• Driver mode (Windows only)
• P2P access matrix (multi-GPU systems)

Requirements

Hardware:

• NVIDIA GPU with CUDA support (any compute capability)
• No specific GPU memory requirement (query only)

Software:

• CUDA Toolkit 13.0 or newer (recommended; matches cuda-python 13.x)
• Python 3.10 or newer
• cuda-python package (>=13.0.0)
• cuda-core package (>=1.0.0)

Installation

Install the required packages from requirements.txt:

cd cuda-samples/python/1_GettingStarted/deviceQuery
pip install -r requirements.txt

The requirements.txt installs:

• cuda-python (>=13.0.0)
• cuda-core (>=1.0.0)

How to Run

Basic usage:

cd cuda-samples/python/1_GettingStarted/deviceQuery
python deviceQuery.py

Skip P2P information:

python deviceQuery.py --no-p2p

Expected Output

[CUDA Device Query using CUDA Core API]
Detected 1 CUDA Capable device(s)

Device 0: <Your GPU Name>
  CUDA Driver Version / Runtime Version          12.4 / 12.6
  CUDA Capability Major/Minor version number:    8.9
  Total amount of global memory:                 24217 MBytes (25393954816 bytes)
  (132) Multiprocessors, (128) CUDA Cores/MP:    16896 CUDA Cores
  GPU Max Clock rate:                            1980 MHz (1.98 GHz)
  Memory Clock rate:                             10501 Mhz
  Memory Bus Width:                              384-bit
  L2 Cache Size:                                 67108864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total shared memory per multiprocessor:        102400 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1536
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z):  (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z):  (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use cudaSetDevice() with device simultaneously) >

Done

Note: Output will vary based on your specific GPU model and system configuration.

For multi-GPU systems, the output will include information for all detected devices and a P2P access matrix showing which GPUs can directly access each other's memory.

Files

• deviceQuery.py - Python implementation using cuda.core API
• requirements.txt - Sample dependencies

Use Cases

• System Diagnostics - Verify CUDA installation and GPU detection
• Hardware Profiling - Understand GPU capabilities before optimization
• Multi-GPU Systems - Identify P2P topology for optimal data placement
• Kernel Development - Determine execution configuration limits
• Compatibility Checks - Verify compute capability requirements

See Also

• CUDA Python Documentation
• cuda.core API Guide
• CUDA Programming Guide - Device Information