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Version: 1.0
For the complete Mojo documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /docs/manual/basics.md).

ld_matrix

ld_matrix[dtype: DType, //, simd_width: Int, *, transpose: Bool = False](ptr: UnsafePointer[Scalar[dtype], address_space=ptr.address_space]) -> SIMD[dtype, simd_width]

Loads a matrix from shared memory into registers in a format suitable for tensor core operations.

This function performs a warp-synchronized load from shared memory to registers, formatting the data to be directly usable by tensor core Matrix Multiply-Accumulate (MMA) instructions.

Note:

  • All threads in a warp must execute this operation together.
  • For transposed loads, only half precision (float16) is supported.
  • The register width is fixed at 4 bytes (32 bits).
  • Supported configurations:
    • x1: One 32-bit register per thread.
    • x2: Two 32-bit registers per thread.
    • x4: Four 32-bit registers per thread.

Example:

from std.gpu.compute.mma import ld_matrix
from std.memory import UnsafePointer, alloc

var ptr = alloc[Scalar[DType.float16]](8)

# Load 8x8 matrix of float16 values
var data = ld_matrix[simd_width=8](ptr)

# Load transposed matrix
var transposed = ld_matrix[simd_width=8, transpose=True](ptr)

ptr.free()

Parameters:

  • ​dtype (DType): The data type of the matrix elements (e.g. float16, float32).
  • ​simd_width (Int): The width of the SIMD vector to load.
  • ​transpose (Bool): Whether to transpose the matrix during load (only supported for half precision).

Args:

Returns:

SIMD[dtype, simd_width]: SIMD vector containing the loaded matrix data, properly formatted for MMA operations.