cuda.bindings._v2.driver.kernel_get_attribute#

cuda.bindings._v2.driver.kernel_get_attribute(int attrib, intptr_t kernel, int dev) → int#

Returns information about a kernel.

Returns in *pi the integer value of the attribute attrib for the kernel kernel for the requested device dev. The supported attributes are:.

CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threads per block, beyond which a launch of the kernel would fail. This number depends on both the kernel and the requested device.
CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of statically- allocated shared memory per block required by this kernel. This does not include dynamically-allocated shared memory requested by the user at runtime.
CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated constant memory required by this kernel.
CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory used by each thread of this kernel.
CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread of this kernel.
CU_FUNC_ATTRIBUTE_PTX_VERSION: The PTX virtual architecture version for which the kernel was compiled. This value is the major PTX version * 10.
the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.
CU_FUNC_ATTRIBUTE_BINARY_VERSION: The binary architecture version for which the kernel was compiled. This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.
CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the kernel has been compiled with user specified option “-Xptxas –dlcm=ca” set.
CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of dynamically-allocated shared memory.
CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1 cache split ratio in percent of total shared memory.
CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET: If this attribute is set, the kernel must launch with a valid cluster size specified.
CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in blocks.
CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in blocks.
CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in blocks.
CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether the function can be launched with non-portable cluster size. 1 is allowed, 0 is disallowed. A non-portable cluster size may only function on the specific SKUs the program is tested on. The launch might fail if the program is run on a different hardware platform. CUDA API provides cudaOccupancyMaxActiveClusters to assist with checking whether the desired size can be launched on the current device. A portable cluster size is guaranteed to be functional on all compute capabilities higher than the target compute capability. The portable cluster size for sm_90 is 8 blocks per cluster. This value may increase for future compute capabilities. The specific hardware unit may support higher cluster sizes that’s not guaranteed to be portable.
CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block scheduling policy of a function. The value type is CUclusterSchedulingPolicy.
CU_FUNC_ATTRIBUTE_SHARED_MEMORY_MODE: The shared memory mode of a function. The value type is CUsharedMemoryMode / cudaSharedMemoryMode.

Parameters:

attrib (FunctionAttribute) – Attribute requested.
kernel (intptr_t) – Kernel to query attribute of.
dev (int) – Device to query attribute of.

Returns:

Returned attribute value.

Return type:

int

Note

If another thread is trying to set the same attribute on the same device using kernel_set_attribute() simultaneously, the attribute query will give the old or new value depending on the interleavings chosen by the OS scheduler and memory consistency.