Merge remote-tracking branch 'upstream/main' into coverage

Author: mdboom

README.md

@@ -2,15 +2,19 @@
 
 CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It consists of multiple components:
 
-* [cuda.core](https://nvidia.github.io/cuda-python/cuda-core/latest): Pythonic access to CUDA Runtime and other core functionalities
+* [cuda.core](https://nvidia.github.io/cuda-python/cuda-core/latest): Pythonic access to CUDA Runtime and other core functionality
 * [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest): Low-level Python bindings to CUDA C APIs
 * [cuda.pathfinder](https://nvidia.github.io/cuda-python/cuda-pathfinder/latest): Utilities for locating CUDA components installed in the user's Python environment
-* [cuda.cccl.cooperative](https://nvidia.github.io/cccl/python/cooperative): A Python module providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
-* [cuda.cccl.parallel](https://nvidia.github.io/cccl/python/parallel): A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc. that are callable on the *host*
-* [numba.cuda](https://nvidia.github.io/numba-cuda/): Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
-* [nvmath-python](https://docs.nvidia.com/cuda/nvmath-python/latest): Pythonic access to NVIDIA CPU & GPU Math Libraries, with both [*host*](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#host-apis) and [*device* (nvmath.device)](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#device-apis) APIs. It also provides low-level Python bindings to host C APIs ([nvmath.bindings](https://docs.nvidia.com/cuda/nvmath-python/latest/bindings/index.html)).
-
-CUDA Python is currently undergoing an overhaul to improve existing and introduce new components. All of the previously available functionalities from the `cuda-python` package will continue to be available, please refer to the [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest) documentation for installation guide and further detail.
+* [cuda.coop](https://nvidia.github.io/cccl/python/coop): A Python module providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
+* [cuda.compute](https://nvidia.github.io/cccl/python/compute): A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc. that are callable on the *host*
+* [numba.cuda](https://nvidia.github.io/numba-cuda/): A Python DSL that exposes CUDA **SIMT** programming model and compiles a restricted subset of Python code into CUDA kernels and device functions
+* [cuda.tile](https://docs.nvidia.com/cuda/cutile-python/): A new Python DSL that exposes CUDA **Tile** programming model and allows users to write NumPy-like code in CUDA kernels
+* [nvmath-python](https://docs.nvidia.com/cuda/nvmath-python/latest): Pythonic access to NVIDIA CPU & GPU Math Libraries, with [*host*](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#host-apis), [*device*](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#device-apis), and [*distributed*](https://docs.nvidia.com/cuda/nvmath-python/latest/distributed-apis/index.html) APIs. It also provides low-level Python bindings to host C APIs ([nvmath.bindings](https://docs.nvidia.com/cuda/nvmath-python/latest/bindings/index.html)).
+* [nvshmem4py](https://docs.nvidia.com/nvshmem/api/api/language_bindings/python/index.html): Pythonic interface to the NVSHMEM library, enabling Python applications to leverage NVSHMEM's high-performance PGAS (Partitioned Global Address Space) programming model for GPU-accelerated computing
+* [Nsight Python](https://docs.nvidia.com/nsight-python/index.html): Python kernel profiling interface that automates performance analysis across multiple kernel configurations using NVIDIA Nsight Tools
+* [CUPTI Python](https://docs.nvidia.com/cupti-python/): Python APIs for creation of profiling tools that target CUDA Python applications via the CUDA Profiling Tools Interface (CUPTI)
+
+CUDA Python is currently undergoing an overhaul to improve existing and introduce new components. All of the previously available functionality from the `cuda-python` package will continue to be available, please refer to the [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest) documentation for installation guide and further detail.
 
 ## cuda-python as a metapackage
 

cuda_bindings/cuda/bindings/_bindings/cydriver.pxd.in

@@ -1609,6 +1609,31 @@ cdef CUresult _cuGraphNodeFindInClone(CUgraphNode* phNode, CUgraphNode hOriginal
 cdef CUresult _cuGraphNodeGetType(CUgraphNode hNode, CUgraphNodeType* typename) except ?CUDA_ERROR_NOT_FOUND nogil
 {{endif}}
 
+{{if 'cuGraphNodeGetContainingGraph' in found_functions}}
+
+cdef CUresult _cuGraphNodeGetContainingGraph(CUgraphNode hNode, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
+{{if 'cuGraphNodeGetLocalId' in found_functions}}
+
+cdef CUresult _cuGraphNodeGetLocalId(CUgraphNode hNode, unsigned int* nodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
+{{if 'cuGraphNodeGetToolsId' in found_functions}}
+
+cdef CUresult _cuGraphNodeGetToolsId(CUgraphNode hNode, unsigned long long* toolsNodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
+{{if 'cuGraphGetId' in found_functions}}
+
+cdef CUresult _cuGraphGetId(CUgraph hGraph, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
+{{if 'cuGraphExecGetId' in found_functions}}
+
+cdef CUresult _cuGraphExecGetId(CUgraphExec hGraphExec, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
 {{if 'cuGraphGetNodes' in found_functions}}
 
 cdef CUresult _cuGraphGetNodes(CUgraph hGraph, CUgraphNode* nodes, size_t* numNodes) except ?CUDA_ERROR_NOT_FOUND nogil
@@ -2174,6 +2199,11 @@ cdef CUresult _cuGreenCtxGetDevResource(CUgreenCtx hCtx, CUdevResource* resource
 cdef CUresult _cuDevSmResourceSplitByCount(CUdevResource* result, unsigned int* nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, unsigned int minCount) except ?CUDA_ERROR_NOT_FOUND nogil
 {{endif}}
 
+{{if 'cuDevSmResourceSplit' in found_functions}}
+
+cdef CUresult _cuDevSmResourceSplit(CUdevResource* result, unsigned int nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, CU_DEV_SM_RESOURCE_GROUP_PARAMS* groupParams) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
 {{if 'cuDevResourceGenerateDesc' in found_functions}}
 
 cdef CUresult _cuDevResourceGenerateDesc(CUdevResourceDesc* phDesc, CUdevResource* resources, unsigned int nbResources) except ?CUDA_ERROR_NOT_FOUND nogil
@@ -2204,6 +2234,11 @@ cdef CUresult _cuGreenCtxStreamCreate(CUstream* phStream, CUgreenCtx greenCtx, u
 cdef CUresult _cuGreenCtxGetId(CUgreenCtx greenCtx, unsigned long long* greenCtxId) except ?CUDA_ERROR_NOT_FOUND nogil
 {{endif}}
 
+{{if 'cuStreamGetDevResource' in found_functions}}
+
+cdef CUresult _cuStreamGetDevResource(CUstream hStream, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+{{endif}}
+
 {{if 'cuLogsRegisterCallback' in found_functions}}
 
 cdef CUresult _cuLogsRegisterCallback(CUlogsCallback callbackFunc, void* userData, CUlogsCallbackHandle* callback_out) except ?CUDA_ERROR_NOT_FOUND nogil