nvmath.device.BlasOptions¶

class nvmath.device.BlasOptions(size, precision, data_type, *, code_type=None, block_size=None, block_dim=None, leading_dimension=None, transpose_mode=('non_transposed', 'non_transposed'), function='MM', execution='Block')[source]¶

A class that encapsulates a partial BLAS device function. A partial device function can be queried for available or optimal values for the some knobs (such as leading_dimension or block_dim). It does not contain a compiled, ready-to-use, device function until finalized using create().

Parameters:

size – A sequence of integers denoting the three dimensions (m, n, k) for the matrix multiplication problem.
precision – The computation precision specified as a numpy float dtype, currently supports numpy.float16, numpy.float32 and numpy.float64.
data_type – The data type of the input matrices, can be either 'real' or 'complex'.
code_type (CodeType) – The target GPU code and compute-capability.
block_size (int) – The total block size, optional. If not provided or set to 'suggested', will be set to a suggested value for 1D block dim.
block_dim (Dim3) – The block dimension for launching the CUDA kernel, optional. If not provided or set to 'suggested', will be set to a suggested value. Can’t not be used when block_size is explicitly specified.
leading_dimension (LeadingDimension) – The leading dimensions for the input matrices, optional. If not provided, will be set to match the matrix row/column dimension. Alternatively, if provided as 'suggested', will be set to a suggested value for optimal performance.
transpose_mode (TransposeMode) – The transpose mode for all input matrices. If not provided, no transposition by default.
function (str) – A string specifying the name of the function. Currently supports 'MM' (default) for matrix multiplication.
execution (str) – A string specifying the execution method, can be 'Block' or 'Thread'.