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Mojo struct

Attribute

@register_passable(trivial) struct Attribute

Aliases​

  • MAX_THREADS_PER_BLOCK = Attribute(SIMD(0)): The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
  • SHARED_SIZE_BYTES = Attribute(SIMD(1)): The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
  • CONST_SIZE_BYTES = Attribute(SIMD(2)): The size in bytes of user-allocated constant memory required by this function.
  • LOCAL_SIZE_BYTES = Attribute(SIMD(3)): The size in bytes of local memory used by each thread of this function.
  • NUM_REGS = Attribute(SIMD(4)): The number of registers used by each thread of this function.
  • PTX_VERSION = Attribute(SIMD(5)): The PTX virtual architecture version for which the function 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..
  • BINARY_VERSION = Attribute(SIMD(6)): The binary architecture version for which the function 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..
  • CACHE_MODE_CA = Attribute(SIMD(7)): The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set .
  • MAX_DYNAMIC_SHARED_SIZE_BYTES = Attribute(SIMD(8)): The maximum size in bytes of dynamically-allocated shared memory that can be used by this function. If the user-specified dynamic shared memory size is larger than this value.
  • PREFERRED_SHARED_MEMORY_CARVEOUT = Attribute(SIMD(9)): On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory.
  • CLUSTER_SIZE_MUST_BE_SET = Attribute(SIMD(10)): If this attribute is set, the kernel must launch with a valid cluster size specified.
  • REQUIRED_CLUSTER_WIDTH = Attribute(SIMD(11)): The required cluster width in blocks. The values must either all be 0 or all be positive. The validity of the cluster dimensions is otherwise checked at launch time.
  • REQUIRED_CLUSTER_HEIGHT = Attribute(SIMD(12)): The required cluster height in blocks. The values must either all be 0 or all be positive. The validity of the cluster dimensions is otherwise checked at launch time.
  • REQUIRED_CLUSTER_DEPTH = Attribute(SIMD(13)): The required cluster depth in blocks. The values must either all be 0 or all be positive. The validity of the cluster dimensions is otherwise checked at launch time.
  • NON_PORTABLE_CLUSTER_SIZE_ALLOWED = Attribute(SIMD(14)): 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.Portable Cluster SizeA 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.
  • CLUSTER_SCHEDULING_POLICY_PREFERENCE = Attribute(SIMD(15)): The block scheduling policy of a function. The value type is CUclusterSchedulingPolicy / cudaClusterSchedulingPolicy.

Fields​

  • ​code (SIMD[int32, 1]):

Implemented traits​

AnyType, Copyable, ExplicitlyCopyable, Movable, UnknownDestructibility

Methods​

__eq__​

__eq__(self, other: Self) -> Bool

__ne__​

__ne__(self, other: Self) -> Bool

__is__​

__is__(self, other: Self) -> Bool

__isnot__​

__isnot__(self, other: Self) -> Bool