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CUDA 13.4 samples update - v13.4-public

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Release 13.4 of the CUDA Samples supported by CUDA Toolkit 13.4.
See Changelog for more information.
This commit is contained in:
Dheemanth
2026-09-09 15:07:08 -07:00
committed by GitHub
parent b7c5481c55
commit 5443602d89
871 changed files with 6843 additions and 321849 deletions
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14
python/1_GettingStarted/blurImageUnifiedMemory/README.md
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@@ -112,12 +112,12 @@ When returning a zero-copy view, the caller must close the buffers after use (e.
### Platform Support:
This sample relies on `ManagedMemoryResource` with **concurrent host access**
to managed allocations while GPU kernels are in flight. That behavior
requires the device property `concurrent_managed_access=True`, which is only
supported on Linux with HMM (Pascal and newer). On Windows (WDDM/MCDM/TCC)
the property is `False`, so the sample exits early with a waive message and
exit code `2` instead of attempting a run that would crash the process.
This sample builds host NumPy views over managed allocations, which requires
the device property `concurrent_managed_access=True`. Devices that report
`False` — Windows (WDDM/MCDM/TCC), and older integrated GPUs such as Jetson
Orin — keep managed allocations GPU-exclusive, so the sample reports the
missing capability and exits with code `2` instead of attempting a run that
would crash the process.
## Installation
@@ -174,4 +174,4 @@ Verifying result...
- [cuda.core Documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/)
- [cuda.core.Program](https://nvidia.github.io/cuda-python/cuda-core/latest/generated/cuda.core.Program.html)
- [cuda.core.ManagedMemoryResource](https://nvidia.github.io/cuda-python/cuda-core/latest/generated/cuda.core.ManagedMemoryResource.html)
- [CUDA Managed Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-unified-memory-programming-hd)
- [CUDA Managed Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/unified-memory.html#unified-memory)

15
python/1_GettingStarted/blurImageUnifiedMemory/blurImageUnifiedMemory.py
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@@ -197,14 +197,6 @@ def main():
3. Unified memory with cuda.core.ManagedMemoryResource
4. Kernel launch with cuda.core.launch and LaunchConfig
"""
if sys.platform == "win32":
print(
"This sample relies on ManagedMemoryResource with concurrent host "
"access, which is not supported on Windows "
"(concurrent_managed_access=False). Waiving this sample."
)
sys.exit(2)
print("=" * 60)
print("Image Blur with Unified Memory (cuda.core)")
print("=" * 60)
@@ -213,6 +205,13 @@ def main():
device = Device(0)
device.set_current()
# This sample builds host NumPy views over managed memory. Devices without
# concurrent managed access keep managed allocations GPU-exclusive while
# the GPU is active, so those host views fault.
if not device.properties.concurrent_managed_access:
print("Concurrent managed memory access is not supported on this platform.")
sys.exit(2)
print(f"\nDevice: {device.name}")
print(f"Compute Capability: sm_{device.arch}")