From 384c5c5e2e8171a3f82a1593a7957e7d454db9e7 Mon Sep 17 00:00:00 2001
From: Phillip Cloud <417981+cpcloud@users.noreply.github.com>
Date: Thu, 16 Apr 2026 05:51:36 -0400
Subject: [PATCH 1/4] Fix is_managed reporting for pool-allocated managed
 memory

Pool-allocated managed memory via cuMemAllocFromPoolAsync (from a pool
created with CU_MEM_ALLOCATION_TYPE_MANAGED) does not set
CU_POINTER_ATTRIBUTE_IS_MANAGED=1. _query_memory_attrs therefore
classified the allocation as pinned host memory, causing
classify_dl_device to return kDLCUDAHost instead of kDLCUDAManaged.
CCCL's make_tma_descriptor only accepts kDLCUDA or kDLCUDAManaged, so
as_tensor_map() failed with "Device type must be kDLCUDA or
kDLCUDAManaged" on managed buffers.

Buffer.is_device_accessible / is_host_accessible already delegate to
the memory resource when one is attached. Apply the same pattern to
is_managed, and expose is_managed on the MemoryResource base
(defaulting to False) with ManagedMemoryResource overriding it to
True.

Also ignore .claude/settings.local.json in .gitignore.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 .gitignore                                               | 1 +
 cuda_core/cuda/core/_memory/_buffer.pyx                  | 7 +++++++
 cuda_core/cuda/core/_memory/_managed_memory_resource.pyx | 5 +++++
 3 files changed, 13 insertions(+)

diff --git a/.gitignore b/.gitignore
index 9bead862d8f..da6748ddbe3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -196,3 +196,4 @@ cython_debug/
 
 # Cursor
 .cursorrules
+.claude/settings.local.json
diff --git a/cuda_core/cuda/core/_memory/_buffer.pyx b/cuda_core/cuda/core/_memory/_buffer.pyx
index a6147cff6ce..e683c781c7b 100644
--- a/cuda_core/cuda/core/_memory/_buffer.pyx
+++ b/cuda_core/cuda/core/_memory/_buffer.pyx
@@ -390,6 +390,8 @@ cdef class Buffer:
     @property
     def is_managed(self) -> bool:
         """Return True if this buffer is CUDA managed (unified) memory, otherwise False."""
+        if self._memory_resource is not None:
+            return self._memory_resource.is_managed
         _init_mem_attrs(self)
         return self._mem_attrs.is_managed
 
@@ -535,6 +537,11 @@ cdef class MemoryResource:
         """Whether buffers allocated by this resource are host-accessible."""
         raise TypeError("MemoryResource.is_host_accessible must be implemented by subclasses.")
 
+    @property
+    def is_managed(self) -> bool:
+        """Whether buffers allocated by this resource are CUDA managed (unified) memory."""
+        return False
+
     @property
     def device_id(self) -> int:
         """Device ID associated with this memory resource, or -1 if not applicable."""
diff --git a/cuda_core/cuda/core/_memory/_managed_memory_resource.pyx b/cuda_core/cuda/core/_memory/_managed_memory_resource.pyx
index 9562ae1335c..205d3c77545 100644
--- a/cuda_core/cuda/core/_memory/_managed_memory_resource.pyx
+++ b/cuda_core/cuda/core/_memory/_managed_memory_resource.pyx
@@ -121,6 +121,11 @@ cdef class ManagedMemoryResource(_MemPool):
         """Return True. This memory resource provides host-accessible buffers."""
         return True
 
+    @property
+    def is_managed(self) -> bool:
+        """Return True. This memory resource provides managed (unified) memory buffers."""
+        return True
+
 
 IF CUDA_CORE_BUILD_MAJOR >= 13:
     cdef tuple _VALID_LOCATION_TYPES = ("device", "host", "host_numa")

From 27b309f6d4a77a06551020cfd874c561456b7ea0 Mon Sep 17 00:00:00 2001
From: Phillip Cloud <417981+cpcloud@users.noreply.github.com>
Date: Thu, 16 Apr 2026 06:08:59 -0400
Subject: [PATCH 2/4] test(memory): add direct is_managed coverage for
 ManagedMemoryResource

The existing test_managed_buffer_dlpack_roundtrip_device_type uses a
DummyUnifiedMemoryResource backed by cuMemAllocManaged, which sets
CU_POINTER_ATTRIBUTE_IS_MANAGED and so never exercised the pool-allocated
path that surfaced the bug.

Add two targeted tests:

- test_managed_memory_resource_buffer_dlpack_device_type: allocates from
  ManagedMemoryResource (cuMemAllocFromPoolAsync on a managed pool) and
  asserts is_managed and kDLCUDAManaged through Buffer and view.
- test_non_managed_resources_report_not_managed: parametrized smoke test
  ensuring DeviceMemoryResource and PinnedMemoryResource still report
  is_managed=False so the new MemoryResource.is_managed default does not
  silently misclassify non-managed resources.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 cuda_core/tests/test_memory.py | 45 ++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/cuda_core/tests/test_memory.py b/cuda_core/tests/test_memory.py
index a8e44a7946f..9ad95825244 100644
--- a/cuda_core/tests/test_memory.py
+++ b/cuda_core/tests/test_memory.py
@@ -605,6 +605,51 @@ def test_managed_buffer_dlpack_roundtrip_device_type():
     assert view.__dlpack_device__() == (int(DLDeviceType.kDLCUDAManaged), 0)
 
 
+def test_managed_memory_resource_buffer_dlpack_device_type():
+    """Verify that pool-allocated managed memory reports kDLCUDAManaged.
+
+    Allocations from ManagedMemoryResource go through cuMemAllocFromPoolAsync
+    on a CU_MEM_ALLOCATION_TYPE_MANAGED pool, which does not set
+    CU_POINTER_ATTRIBUTE_IS_MANAGED.  The Buffer must still report itself as
+    managed via its memory resource so that DLPack consumers (e.g. CCCL's
+    make_tma_descriptor) accept the buffer.
+    """
+    device = Device()
+    device.set_current()
+    skip_if_managed_memory_unsupported(device)
+    mr = create_managed_memory_resource_or_skip(
+        ManagedMemoryResourceOptions(preferred_location=device.device_id)
+    )
+    buf = mr.allocate(1024)
+
+    assert mr.is_managed
+    assert buf.is_managed
+    assert buf.__dlpack_device__() == (DLDeviceType.kDLCUDAManaged, 0)
+
+    view = StridedMemoryView.from_any_interface(buf, stream_ptr=-1)
+    assert view.__dlpack_device__() == (int(DLDeviceType.kDLCUDAManaged), 0)
+
+
+@pytest.mark.parametrize(
+    ("mr_factory", "expected_managed"),
+    [
+        (lambda dev: DeviceMemoryResource(dev), False),
+        (lambda dev: PinnedMemoryResource(), False),
+    ],
+)
+def test_non_managed_resources_report_not_managed(mr_factory, expected_managed):
+    """Non-managed memory resources must report is_managed=False."""
+    device = Device()
+    device.set_current()
+    if not device.properties.memory_pools_supported:
+        pytest.skip("Device does not support mempool operations")
+    mr = mr_factory(device)
+    assert mr.is_managed is expected_managed
+    buf = mr.allocate(1024)
+    assert buf.is_managed is expected_managed
+    buf.close()
+
+
 @pytest.mark.parametrize("use_device_object", [True, False])
 def test_device_memory_resource_initialization(use_device_object):
     """Test that DeviceMemoryResource can be initialized successfully.

From a933a88a6495d00521b5444bca834819eb97eeb1 Mon Sep 17 00:00:00 2001
From: Phillip Cloud <417981+cpcloud@users.noreply.github.com>
Date: Thu, 16 Apr 2026 06:44:32 -0400
Subject: [PATCH 3/4] Fix Buffer.is_managed to merge driver and resource
 signals

Previous fix unconditionally delegated Buffer.is_managed to
_memory_resource.is_managed, which returns False for any
MemoryResource subclass that does not opt in.  That broke
DummyUnifiedMemoryResource (and any user-defined MR wrapping
cuMemAllocManaged) where the driver pointer attribute correctly
reports IS_MANAGED=1 but the resource does not override is_managed.

Query the driver first; only fall back to the memory resource when
the driver does not report IS_MANAGED (the pool-allocated managed
memory path).  This keeps both old-style cuMemAllocManaged buffers
and ManagedMemoryResource pool allocations correctly classified.

Also rework the regression test parametrization to skip the pinned
case when PinnedMemoryResource is unavailable (CUDA < 13.0), and pick
up the ruff-format reflow of the helper call site.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 cuda_core/cuda/core/_memory/_buffer.pyx |  8 +++++---
 cuda_core/tests/test_memory.py          | 24 ++++++++++--------------
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/cuda_core/cuda/core/_memory/_buffer.pyx b/cuda_core/cuda/core/_memory/_buffer.pyx
index e683c781c7b..bb6fd97df6f 100644
--- a/cuda_core/cuda/core/_memory/_buffer.pyx
+++ b/cuda_core/cuda/core/_memory/_buffer.pyx
@@ -390,10 +390,12 @@ cdef class Buffer:
     @property
     def is_managed(self) -> bool:
         """Return True if this buffer is CUDA managed (unified) memory, otherwise False."""
-        if self._memory_resource is not None:
-            return self._memory_resource.is_managed
         _init_mem_attrs(self)
-        return self._mem_attrs.is_managed
+        if self._mem_attrs.is_managed:
+            return True
+        # Pool-allocated managed memory does not set CU_POINTER_ATTRIBUTE_IS_MANAGED,
+        # so fall back to the memory resource when it advertises managed allocations.
+        return self._memory_resource is not None and self._memory_resource.is_managed
 
     @property
     def is_mapped(self) -> bool:
diff --git a/cuda_core/tests/test_memory.py b/cuda_core/tests/test_memory.py
index 9ad95825244..57de22bb9a0 100644
--- a/cuda_core/tests/test_memory.py
+++ b/cuda_core/tests/test_memory.py
@@ -617,9 +617,7 @@ def test_managed_memory_resource_buffer_dlpack_device_type():
     device = Device()
     device.set_current()
     skip_if_managed_memory_unsupported(device)
-    mr = create_managed_memory_resource_or_skip(
-        ManagedMemoryResourceOptions(preferred_location=device.device_id)
-    )
+    mr = create_managed_memory_resource_or_skip(ManagedMemoryResourceOptions(preferred_location=device.device_id))
     buf = mr.allocate(1024)
 
     assert mr.is_managed
@@ -630,23 +628,21 @@ def test_managed_memory_resource_buffer_dlpack_device_type():
     assert view.__dlpack_device__() == (int(DLDeviceType.kDLCUDAManaged), 0)
 
 
-@pytest.mark.parametrize(
-    ("mr_factory", "expected_managed"),
-    [
-        (lambda dev: DeviceMemoryResource(dev), False),
-        (lambda dev: PinnedMemoryResource(), False),
-    ],
-)
-def test_non_managed_resources_report_not_managed(mr_factory, expected_managed):
+@pytest.mark.parametrize("mr_kind", ["device", "pinned"])
+def test_non_managed_resources_report_not_managed(mr_kind):
     """Non-managed memory resources must report is_managed=False."""
     device = Device()
     device.set_current()
     if not device.properties.memory_pools_supported:
         pytest.skip("Device does not support mempool operations")
-    mr = mr_factory(device)
-    assert mr.is_managed is expected_managed
+    if mr_kind == "device":
+        mr = DeviceMemoryResource(device)
+    else:
+        skip_if_pinned_memory_unsupported(device)
+        mr = PinnedMemoryResource()
+    assert mr.is_managed is False
     buf = mr.allocate(1024)
-    assert buf.is_managed is expected_managed
+    assert buf.is_managed is False
     buf.close()
 
 

From 7c7c755b48b66be00882e68d25b5347c10d132f1 Mon Sep 17 00:00:00 2001
From: Phillip Cloud <417981+cpcloud@users.noreply.github.com>
Date: Thu, 16 Apr 2026 07:43:23 -0400
Subject: [PATCH 4/4] chore: refresh pixi.lock for upstream package updates

Pick up cuda-nvrtc 13.2.78, libcufile 1.17.1.22, and other transitive
package updates from conda-forge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 cuda_bindings/pixi.lock |  82 +++++++--
 cuda_core/pixi.lock     | 366 ++++++++++++++++++++++------------------
 2 files changed, 274 insertions(+), 174 deletions(-)

diff --git a/cuda_bindings/pixi.lock b/cuda_bindings/pixi.lock
index 5224450b6e3..c0a897611c7 100644
--- a/cuda_bindings/pixi.lock
+++ b/cuda_bindings/pixi.lock
@@ -567,7 +567,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-13.2.51-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-13.2.51-h376f20c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-13.2.51-h376f20c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.2.51-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.2.78-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-13.2.51-h69a702a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-13.2.51-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-13.2.51-h4bc722e_0.conda
@@ -612,7 +612,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h4a7cf45_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.77-h3ff7636_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_h0358290_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.17.0.44-h85c024f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.17.1.22-h85c024f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda
@@ -763,7 +763,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-static-13.2.51-h8f3c8d4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-aarch64-13.2.51-h8f3c8d4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-aarch64-13.2.51-h8f3c8d4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.51-h8f3c8d4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.78-h8f3c8d4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvvm-13.2.51-he9431aa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-aarch64-13.2.51-h579c4fd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvvm-impl-13.2.51-h7b14b0b_0.conda
@@ -805,7 +805,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-5_haddc8a3_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcap-2.77-h68e9139_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-5_hd72aa62_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.0.44-h4243460_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.1.22-h4243460_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.125-he30d5cf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libegl-1.7.0-hd24410f_2.conda
@@ -946,7 +946,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-static-13.2.51-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_win-64-13.2.51-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_win-64-13.2.51-hac47afa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.51-hac47afa_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.78-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-13.2.51-h719f0c7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_win-64-13.2.51-h57928b3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-impl-13.2.51-h2466b09_0.conda
@@ -1468,7 +1468,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-static-13.2.51-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_win-64-13.2.51-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_win-64-13.2.51-hac47afa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.51-hac47afa_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.78-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-13.2.51-h719f0c7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_win-64-13.2.51-h57928b3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-impl-13.2.51-h2466b09_0.conda
@@ -3025,7 +3025,7 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - vc >=14.3,<15
@@ -3079,11 +3079,11 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - libcufile
-  - libcufile >=1.17.0.44,<2.0a0
+  - libcufile >=1.17.1.22,<2.0a0
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
@@ -3135,11 +3135,11 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - libcufile
-  - libcufile >=1.17.0.44,<2.0a0
+  - libcufile >=1.17.1.22,<2.0a0
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
@@ -3763,6 +3763,17 @@ packages:
   purls: []
   size: 35736655
   timestamp: 1773100338749
+- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.2.78-hecca717_0.conda
+  sha256: 73fbc9d15c062c3ea60891e8183002f6b055fa6638402d17581677af0aaa20d8
+  md5: 66623d882c42506fa3f1780b90841400
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - cuda-version >=13.2,<13.3.0a0
+  - libgcc >=14
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 35670504
+  timestamp: 1776109867257
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-12.9.86-h8f3c8d4_1.conda
   sha256: e7f8d835d7bf993dcad9fba6db5af89c35b2b4f0282799b729bf6ad2c3bd896d
   md5: 48187c09673a42f9930764e8170b8787
@@ -3786,6 +3797,17 @@ packages:
   purls: []
   size: 33927374
   timestamp: 1773100385281
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.78-h8f3c8d4_0.conda
+  sha256: dcc9a9890d04850ffecc59748d546dcde9c80d3040b726cf3839b8191c6d3548
+  md5: b6632980124d529a2b87f68831c58743
+  depends:
+  - arm-variant * sbsa
+  - cuda-version >=13.2,<13.3.0a0
+  - libgcc >=14
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 33929238
+  timestamp: 1776109887303
 - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-12.9.86-hac47afa_1.conda
   sha256: d90ef446ac859db26286a5d39d39333c4e4cee31ba5042b5c7922bd25de531f6
   md5: d68b5d96a53c80dc3dbbd8f7c3b8106d
@@ -3809,6 +3831,17 @@ packages:
   purls: []
   size: 31221551
   timestamp: 1773100427009
+- conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.78-hac47afa_0.conda
+  sha256: 5e2b53023ceafae1f7f4d83e83ebf175befb04f50fa131ba7c78d0cc6b299477
+  md5: 3c8fa05aa0fe9d3cb4638e8bd7bb84e3
+  depends:
+  - cuda-version >=13.2,<13.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 31226924
+  timestamp: 1776110029365
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diff --git a/cuda_core/pixi.lock b/cuda_core/pixi.lock
index d48619be4aa..4b7d2809cf1 100644
--- a/cuda_core/pixi.lock
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.51-h8f3c8d4_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.0.44-h4243460_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.1.22-h4243460_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20250104-pl5321h976ea20_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.51-hac47afa_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-13.2.51-h376f20c_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-13.2.20-hecca717_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.17.0.44-h85c024f_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.51-h8f3c8d4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-nvrtc-13.2.78-h8f3c8d4_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.0.44-h4243460_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.1.22-h4243460_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcurand-10.4.2.51-he38c790_0.conda
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@@ -2696,7 +2696,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.51-hac47afa_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-13.2.78-hac47afa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-13.2.51-h719f0c7_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvvm-impl-13.2.51-h2466b09_0.conda
@@ -3474,7 +3474,7 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - vc >=14.3,<15
@@ -3500,11 +3500,11 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - libcufile
-  - libcufile >=1.17.0.44,<2.0a0
+  - libcufile >=1.17.1.22,<2.0a0
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
@@ -3528,11 +3528,11 @@ packages:
   - cuda-pathfinder
   - libnvjitlink
   - cuda-nvrtc
-  - cuda-nvrtc >=13.2.51,<14.0a0
+  - cuda-nvrtc >=13.2.78,<14.0a0
   - cuda-nvvm
   - libnvfatbin
   - libcufile
-  - libcufile >=1.17.0.44,<2.0a0
+  - libcufile >=1.17.1.22,<2.0a0
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
@@ -3673,8 +3673,8 @@ packages:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - cuda-nvrtc >=13.2.51,<14.0a0
   - python_abi 3.14.* *_cp314
+  - cuda-nvrtc >=13.2.78,<14.0a0
   license: Apache-2.0
 - conda: .
   name: cuda-core
@@ -3718,9 +3718,9 @@ packages:
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
-  - cuda-cudart >=13.2.51,<14.0a0
-  - cuda-nvrtc >=13.2.51,<14.0a0
   - python_abi 3.14.* *_cp314
+  - cuda-nvrtc >=13.2.78,<14.0a0
+  - cuda-cudart >=13.2.75,<14.0a0
   license: Apache-2.0
 - conda: .
   name: cuda-core
@@ -3762,9 +3762,9 @@ packages:
   - libgcc >=15
   - libgcc >=15
   - libstdcxx >=15
-  - cuda-cudart >=13.2.51,<14.0a0
-  - cuda-nvrtc >=13.2.51,<14.0a0
   - python_abi 3.14.* *_cp314
+  - cuda-nvrtc >=13.2.78,<14.0a0
+  - cuda-cudart >=13.2.75,<14.0a0
   license: Apache-2.0
 - conda: .
   name: cuda-core
@@ -3892,19 +3892,19 @@ packages:
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   size: 23242
   timestamp: 1749218416505
-- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-13.2.51-hecca717_0.conda
-  sha256: 9cc44fd4914738a32cf5c801925a08c61ce45b5534833cf1df1621236a9a321d
-  md5: 29f5b46965bd82b0e9cc27a96d13f2bd
+- conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-13.2.75-hecca717_0.conda
+  sha256: 633bc9ba458a12a20a42776bf3fa25cecfddc65a22e4ed207fe09b9adcd9de58
+  md5: 9b7dcd83f8a965efcf7377dc54203619
   depends:
   - __glibc >=2.17,<3.0.a0
-  - cuda-cudart_linux-64 13.2.51 h376f20c_0
+  - cuda-cudart_linux-64 13.2.75 h376f20c_0
   - cuda-version >=13.2,<13.3.0a0
   - libgcc >=14
   - libstdcxx >=14
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   purls: []
-  size: 24534
-  timestamp: 1773104357094
+  size: 24542
+  timestamp: 1776110472025
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-12.9.79-h3ae8b8a_0.conda
   sha256: 3d6699fc27ffabf28a9d359b48e7b88437e4d945844718a58608627998db5d1b
   md5: df78e19e5fe656631d1470aa0fcf6ced
@@ -3917,19 +3917,19 @@ packages:
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   size: 23466
   timestamp: 1749218349235
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-13.2.51-h8f3c8d4_0.conda
-  sha256: 29be349d467aa2d2a8b6ccc738fca46e7a34ae30d220b6a70a2c99116f904801
-  md5: 9dd153714cb42e271205d8b91d6da397
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-13.2.75-h8f3c8d4_0.conda
+  sha256: bfcf3a00ee2ac1632409564c5c6369e4bd385101da7cf3bce7a044ea6ae660dc
+  md5: 200ffa107a11ece7e72b4931f5e40650
   depends:
   - arm-variant * sbsa
-  - cuda-cudart_linux-aarch64 13.2.51 h8f3c8d4_0
+  - cuda-cudart_linux-aarch64 13.2.75 h8f3c8d4_0
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   - libstdcxx >=14
   constrains:
   - arm-variant * sbsa
-  - cuda-nvrtc-static >=13.2.51
+  - cuda-nvrtc-static >=13.2.78
   license: LicenseRef-NVIDIA-End-User-License-Agreement
-  size: 36381
-  timestamp: 1773100508513
+  size: 36316
+  timestamp: 1776109996052
 - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-dev-12.9.86-hac47afa_1.conda
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   size: 35214
   timestamp: 1760724506186
-- conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-dev-13.2.51-hac47afa_0.conda
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-  md5: 4993115c33f07ff3b338992a8e4008f2
+- conda: https://conda.anaconda.org/conda-forge/win-64/cuda-nvrtc-dev-13.2.78-hac47afa_0.conda
+  sha256: 2b40a8ce9b1496b45232d20e15d8cb6e797792d9f7990feeb3149a4f089a2d8b
+  md5: f5a4101ba76f052b882a83f957298252
   depends:
-  - cuda-nvrtc 13.2.51 hac47afa_0
+  - cuda-nvrtc 13.2.78 hac47afa_0
   - cuda-version >=13.2,<13.3.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   license: LicenseRef-NVIDIA-End-User-License-Agreement
-  size: 35405
-  timestamp: 1773101046259
+  size: 35456
+  timestamp: 1776110679144
 - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-13.2.20-hecca717_0.conda
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@@ -7339,9 +7379,9 @@ packages:
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   size: 969845
   timestamp: 1761098818759
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.17.0.44-h85c024f_0.conda
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-  md5: 6d8ed8335d144ec7303b8d3587b2205c
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.17.1.22-h85c024f_0.conda
+  sha256: a24ad0ca488aa3e237049cd5b5c6d7fe3d2d4330682ed329203064e332ea1d74
+  md5: 056a67706108efd1f9c24682ba8d3685
   depends:
   - __glibc >=2.28,<3.0.a0
   - cuda-version >=13.2,<13.3.0a0
@@ -7350,8 +7390,8 @@ packages:
   - rdma-core >=61.0
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   purls: []
-  size: 1085341
-  timestamp: 1773100191342
+  size: 1082447
+  timestamp: 1776110053053
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.14.1.1-had8bf56_1.conda
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   md5: ee136db5a5409dddc78eaf7658fccffe
@@ -7365,9 +7405,9 @@ packages:
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   size: 909365
   timestamp: 1761098964619
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.0.44-h4243460_0.conda
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-  md5: 62177c2a0b2d8ab2cfa065df0ef656b7
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcufile-1.17.1.22-h4243460_0.conda
+  sha256: a55f0380307d719ea6edfdc92a69b80c9a149d9cc1f2c70cd91c26d56469d793
+  md5: 7edb7b0865c4d4309a0a337783022a3c
   depends:
   - __glibc >=2.28,<3.0.a0
   - arm-variant * sbsa
@@ -7379,8 +7419,8 @@ packages:
   - arm-variant * sbsa
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   purls: []
-  size: 973639
-  timestamp: 1773100202181
+  size: 965937
+  timestamp: 1776110037973
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurand-10.4.2.51-h676940d_0.conda
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