wolfgpu.tile_packer

Module Contents

class wolfgpu.tile_packer.TilePackingMode

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

REGULAR = 1

TRANSPARENT = 3

wolfgpu.tile_packer._unpack_and_deshuffle_array(a: numpy.ndarray, shape: tuple, height: int, width: int, active_tiles: numpy.ndarray, tile_size: int, tile_indirection_map: numpy.ndarray) → numpy.ndarray

class wolfgpu.tile_packer.TilePacker(nap: numpy.array, tile_size: int, mode: TilePackingMode = TilePackingMode.REGULAR)

tile_reversed_indirection_map()

tile_indirection_map()

The tile indirection map. Its shape is (nb_tiles_y,nb_tiles_x,2). The z-axis contains the indirected coordinates of the bottom-left corner of the tile denoted by the x and y axis values. For example, if you have the tile coordinates (t_i, t_j), then map[t_i, t_j, :] is a 2-tuple containing the coordinates (in meshes) of the bottom-left corner of that tile, on the indirected map.

mode() → TilePackingMode

packed_size()

Size of the arrays after padding them and packing them in tiles, expressed in meshes. Size is a (width, height) tuple.

Note that this size can be very different than the actual computation domain size.

packed_size_in_tiles()

Size of the arrays after padding them and packing them in tiles, expressed in tiles. Size is a (width, height) tuple.

Note that this size can be very different than the actual computation domain size.

size_in_tiles()

Size of the (original, non packed, non tiled) computation domain, in tiles. Not that we count full tiles. So if one dimension of the domain is not a multiple of the tile size, then we round one tile up.

Size is a (width, height) tuple.

tile_size() → int

The tile size. Note that tiles are squared.

active_tiles_ndx()

unpack_and_deshuffle_array(a: numpy.ndarray) → numpy.ndarray

De-shuffle and un-pad an array of tiles that was shuffled and padded.

_unpad_array(a: numpy.array) → numpy.array

Undo _pad_array_to_tiles.

_pad_array_to_tiles(a: numpy.array, neutral_values) → numpy.array

Make an array fit in a given number of tiles (on x and y axis). After this, the array’s dimensions are multiple of the tile_size.

Parameters:

neutral_values – The value used to pad.

shuffle_and_pack_array(a: numpy.array, neutral_values=None, debug=False) → numpy.array

Reorganize an array by moving tiles around to follow the ordering given by self._tile_indirection_map The array is resized in order to be just as large as needed to hold the active tiles plus the “empty” tile.

neutral_values: value to fill the empty tile with.