CUDA 13.4 samples update - v13.4-public

Release 13.4 of the CUDA Samples supported by CUDA Toolkit 13.4.
See Changelog for more information.

python/2_CoreConcepts/memoryResources/README.md

@@ -72,15 +72,19 @@ verified on the host.
 
 ### Platform Support
 
-The `ManagedMemoryResource` demo in this sample exercises **concurrent host
-access** to managed allocations while the GPU is active, which requires the
-device property `concurrent_managed_access=True`. This 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`. The `DeviceMemoryResource` + `PinnedMemoryResource` demos in this
-sample would still work on Windows on their own, but to keep the sample
-self-contained the entire script waives when concurrent managed access is
-unavailable.
+This sample needs two device capabilities, and exits with code `2` when
+either is missing:
+
+- `PinnedMemoryResource` is backed by a host memory pool, which requires
+  `host_memory_pools_supported=True`. Windows in TCC mode and older
+  integrated GPUs such as Jetson Orin report `False`.
+- The `ManagedMemoryResource` demo builds host NumPy views over managed
+  allocations, which requires `concurrent_managed_access=True`. Windows
+  (WDDM/MCDM/TCC) and older integrated GPUs such as Jetson Orin report
+  `False`.
+
+The demos are checked up front rather than individually to keep the sample
+self-contained.
 
 ## Installation
 

python/2_CoreConcepts/memoryResources/memoryResources.py

@@ -222,18 +222,23 @@ def main():
     parser.add_argument("--device", type=int, default=0, help="CUDA device id")
     args = parser.parse_args()
 
-    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)
-
     device = Device(args.device)
     device.set_current()
     print_gpu_info(device)
 
+    # PinnedMemoryResource is backed by a host memory pool, which is not
+    # available on every device.
+    if not device.properties.host_memory_pools_supported:
+        print("Host pinned memory pools are not supported on this platform.")
+        return 2
+
+    # 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.")
+        return 2
+
     stream = device.create_stream()
 
     try: