cuda.bindings._v2.driver.graph_add_kernel_node_v2#

cuda.bindings._v2.driver.graph_add_kernel_node_v2( intptr_t h_graph, dependencies, size_t num_dependencies, node_params, ) → intptr_t#

Creates a kernel execution node and adds it to a graph.

Creates a new kernel execution node and adds it to h_graph with num_dependencies dependencies specified via dependencies and arguments specified in node_params. It is possible for num_dependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in ph_graph_node. The CUDA_KERNEL_NODE_PARAMS structure is defined as:. **View CUDA Toolkit Documentation for a C++ code example**. When the graph is launched, the node will invoke kernel func on a (gridDimX x gridDimY x gridDimZ) grid of blocks. Each block contains (blockDimX x blockDimY x blockDimZ) threads. sharedMemBytes sets the amount of dynamic shared memory that will be available to each thread block. Kernel parameters to func can be specified in one of two ways:. 1) Kernel parameters can be specified via kernelParams. If the kernel has N parameters, then kernelParams needs to be an array of N pointers. Each pointer, from kernelParams[0] to kernelParams[N-1], points to the region of memory from which the actual parameter will be copied. The number of kernel parameters and their offsets and sizes do not need to be specified as that information is retrieved directly from the kernel’s image. 2) Kernel parameters for non-cooperative kernels can also be packaged by the application into a single buffer that is passed in via extra. This places the burden on the application of knowing each kernel parameter’s size and alignment/padding within the buffer. The extra parameter exists to allow this function to take additional less commonly used arguments. extra specifies a list of names of extra settings and their corresponding values. Each extra setting name is immediately followed by the corresponding value. The list must be terminated with either NULL or CU_LAUNCH_PARAM_END.

CU_LAUNCH_PARAM_END, which indicates the end of the extra array;.
CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next value in extra will be a pointer to a buffer containing all the kernel parameters for launching kernel func;.
CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next value in extra will be a pointer to a size_t containing the size of the buffer specified with CU_LAUNCH_PARAM_BUFFER_POINTER;.

The error CUDA_ERROR_INVALID_VALUE will be returned if kernel parameters are specified with both kernelParams and extra (i.e. both kernelParams and extra are non-NULL). CUDA_ERROR_INVALID_VALUE will be returned if extra is used for a cooperative kernel. The kernelParams or extra array, as well as the argument values it points to, are copied during this call.

Parameters:

h_graph (intptr_t) – Graph to which to add the node.
dependencies (object) –
Dependencies of the node. It can be:
- an int as the pointer address to the array, or
- a Python sequence of ints (as pointer addresses).
num_dependencies (size_t) – Number of dependencies.
node_params (intptr_t) – Parameters for the GPU execution node.

Returns:

Returns newly created node.

Return type:

intptr_t

Note

Kernels launched using graphs must not use texture and surface references. Reading or writing through any texture or surface reference is undefined behavior. This restriction does not apply to texture and surface objects.