def optimize_for_cpu(sdfg: dace.SDFG, m: int, n: int, k: int):
""" Optimize the matrix multiplication example for multi-core CPUs. """
# Ensure integers are 32-bit by default
dace.Config.set('compiler', 'default_data_types', value='C')
# Fuse the map and reduce nodes
sdfg.apply_transformations(MapReduceFusion)
# Find multiplication map
entry = find_map_by_param(sdfg, 'k')
# Create a tiling strategy
divides_evenly = (m % 32 == 0) and (n % 32 == 0) and (k % 256 == 0)
xfutil.tile(sdfg, entry, divides_evenly, False, k=256, i=32, j=32)
xfutil.tile(sdfg, entry, divides_evenly, divides_evenly, j=16, i=4)
# Reorder internal map to "k,i,j"
xfutil.permute_map(entry, [2, 0, 1])
# Add local storage for B in j tile: we apply InLocalStorage with a
# parameter "array" named B, between the two maps of j and i
regtile_j = find_map_by_param(sdfg, 'tile1_j')
regtile_i = find_map_by_param(sdfg, 'tile1_i')
InLocalStorage.apply_to(sdfg,
dict(array='B'),
node_a=regtile_j,
node_b=regtile_i)
if divides_evenly:
# Add local storage for C
exit_inner = find_mapexit_by_param(sdfg, 'k')
exit_rti = find_mapexit_by_param(sdfg, 'tile1_i')
AccumulateTransient.apply_to(sdfg,
dict(array='C', identity=0),
map_exit=exit_inner,
outer_map_exit=exit_rti)
# Vectorize microkernel map
postamble = n % 4 != 0
entry_inner, inner_state = find_map_and_state_by_param(sdfg, 'k')
Vectorization.apply_to(inner_state.parent,
dict(vector_len=4,
preamble=False,
postamble=postamble),
_map_entry=entry_inner)
# Mark outer tile map as sequential to remove atomics
find_map_by_param(sdfg,
'tile_k').map.schedule = dace.ScheduleType.Sequential
# Collapse maps for more parallelism
find_map_by_param(sdfg, 'o0').map.collapse = 2
tile_i = find_map_by_param(sdfg, 'tile_i')
tile_j = find_map_by_param(sdfg, 'tile_j')
MapCollapse.apply_to(sdfg,
_outer_map_entry=tile_i,
_inner_map_entry=tile_j)
tile_ij = find_map_by_param(sdfg, 'tile_i') # Find newly created map
tile_ij.map.schedule = dace.ScheduleType.CPU_Multicore
tile_ij.map.collapse = 2
def optimize_for_gpu(sdfg: dace.SDFG, m: int, n: int, k: int):
""" Optimize the matrix multiplication example for GPUs. """
# Ensure integers are 32-bit by default
dace.Config.set('compiler', 'default_data_types', value='C')
# Fuse the map and reduce nodes
sdfg.apply_transformations(MapReduceFusion)
# Apply GPU transformation
sdfg.apply_gpu_transformations()
# Find multiplication map
entry = find_map_by_param(sdfg, 'k')
# Create a tiling strategy
divides_evenly = (m % 64 == 0) and (n % 64 == 0) and (k % 8 == 0)
xfutil.tile(sdfg, entry, divides_evenly, True, i=64, j=64, k=8)
xfutil.tile(sdfg, entry, divides_evenly, True, i=8, j=4)
# Create kernel schedule by collapsing and reordering maps
gtile_i = find_map_by_param(sdfg, 'tile_i')
gtile_j = find_map_by_param(sdfg, 'tile_j')
btile_i = find_map_by_param(sdfg, 'tile1_i')
btile_j = find_map_by_param(sdfg, 'tile1_j')
MapCollapse.apply_to(sdfg, outer_map_entry=gtile_i, inner_map_entry=gtile_j, permissive=True)
MapCollapse.apply_to(sdfg, outer_map_entry=btile_i, inner_map_entry=btile_j, permissive=True)
btile = find_map_by_param(sdfg, 'tile1_i')
btile.map.schedule = dace.ScheduleType.GPU_ThreadBlock
# Add local storage (shared memory) for A and B on GPU
ktile = find_map_by_param(sdfg, 'tile_k')
smem_a = InLocalStorage.apply_to(sdfg, dict(array='A'), node_a=ktile, node_b=btile)
smem_b = InLocalStorage.apply_to(sdfg, dict(array='B'), node_a=ktile, node_b=btile)
sdfg.arrays[smem_a.data].storage = dace.StorageType.GPU_Shared
sdfg.arrays[smem_b.data].storage = dace.StorageType.GPU_Shared
# Add local storage (registers) for A and B
ttile = find_map_by_param(sdfg, 'k')
warptile, ttile = xfutil.extract_map_dims(sdfg, ttile, [2])
InLocalStorage.apply_to(sdfg, dict(array='trans_gpu_A'), node_a=warptile, node_b=ttile)
InLocalStorage.apply_to(sdfg, dict(array='trans_gpu_B'), node_a=warptile, node_b=ttile)
# Add local storage (registers) for C
state = next(s for s in sdfg.nodes() if warptile in s.nodes())
warptile_exit = state.exit_node(warptile)
btile_exit = state.exit_node(btile)
AccumulateTransient.apply_to(sdfg, map_exit=warptile_exit, outer_map_exit=btile_exit)
# Set C tile to zero on allocation
c_access = next(n for n in state.data_nodes() if n.data == 'trans_gpu_C')
c_access.setzero = True
# Unroll microkernel maps
ttile.map.unroll = True
# Apply double-buffering on shared memory
DoubleBuffering.apply_to(sdfg, map_entry=ktile, transient=smem_a)
def apply(self, sdfg: SDFG) -> None:
graph: SDFGState = sdfg.nodes()[self.state_id]
inner_map_entry: nodes.MapEntry = graph.nodes()[self.subgraph[
GPUMultiTransformMap._map_entry]]
number_of_gpus = self.number_of_gpus
ngpus = Config.get("compiler", "cuda", "max_number_gpus")
if (number_of_gpus == None):
number_of_gpus = ngpus
if number_of_gpus > ngpus:
raise ValueError(
'Requesting more gpus than specified in the dace config')
# Avoiding import loops
from dace.transformation.dataflow import (StripMining, InLocalStorage,
OutLocalStorage,
AccumulateTransient)
# The user has responsibility for the implementation of a Library node.
scope_subgraph = graph.scope_subgraph(inner_map_entry)
for node in scope_subgraph.nodes():
if isinstance(node, nodes.LibraryNode):
warnings.warn(
'Node %s is a library node, make sure to manually set the '
'implementation to a GPU compliant specialization.' % node)
# Tile map into number_of_gpus tiles
outer_map: nodes.Map = StripMining.apply_to(
sdfg,
dict(dim_idx=-1,
new_dim_prefix=self.new_dim_prefix,
tile_size=number_of_gpus,
tiling_type=dtypes.TilingType.NumberOfTiles),
_map_entry=inner_map_entry)
outer_map_entry: nodes.MapEntry = graph.scope_dict()[inner_map_entry]
inner_map_exit: nodes.MapExit = graph.exit_node(inner_map_entry)
outer_map_exit: nodes.MapExit = graph.exit_node(outer_map_entry)
# Change map schedules
inner_map_entry.map.schedule = dtypes.ScheduleType.GPU_Device
outer_map.schedule = dtypes.ScheduleType.GPU_Multidevice
symbolic_gpu_id = outer_map.params[0]
# Add the parameter of the outer map
for node in graph.successors(inner_map_entry):
if isinstance(node, nodes.NestedSDFG):
map_syms = inner_map_entry.range.free_symbols
for sym in map_syms:
symname = str(sym)
if symname not in node.symbol_mapping.keys():
node.symbol_mapping[symname] = sym
node.sdfg.symbols[symname] = graph.symbols_defined_at(
node)[symname]
# Add transient Data leading to the inner map
prefix = self.new_transient_prefix
for node in graph.predecessors(outer_map_entry):
# Only AccessNodes are relevant
if (isinstance(node, nodes.AccessNode)
and not (self.skip_scalar
and isinstance(node.desc(sdfg), Scalar))):
if self.use_p2p and node.desc(
sdfg).storage is dtypes.StorageType.GPU_Global:
continue
in_data_node = InLocalStorage.apply_to(sdfg,
dict(array=node.data,
prefix=prefix),
verify=False,
save=False,
node_a=outer_map_entry,
node_b=inner_map_entry)
in_data_node.desc(sdfg).location['gpu'] = symbolic_gpu_id
in_data_node.desc(sdfg).storage = dtypes.StorageType.GPU_Global
wcr_data: Dict[str, Any] = {}
# Add transient Data leading to the outer map
for edge in graph.in_edges(outer_map_exit):
node = graph.memlet_path(edge)[-1].dst
if isinstance(node, nodes.AccessNode):
data_name = node.data
# Transients with write-conflict resolution need to be
# collected first as AccumulateTransient creates a nestedSDFG
if edge.data.wcr is not None:
dtype = sdfg.arrays[data_name].dtype
redtype = operations.detect_reduction_type(edge.data.wcr)
# Custom reduction can not have an accumulate transient,
# as the accumulation from the transient to the outer
# storage is not defined.
if redtype == dtypes.ReductionType.Custom:
warnings.warn(
'Using custom reductions in a GPUMultitransformed '
'Map only works for a small data volume. For large '
'volume there is no guarantee.')
continue
identity = dtypes.reduction_identity(dtype, redtype)
wcr_data[data_name] = identity
elif (not isinstance(node.desc(sdfg), Scalar)
or not self.skip_scalar):
if self.use_p2p and node.desc(
sdfg).storage is dtypes.StorageType.GPU_Global:
continue
# Transients without write-conflict resolution
if prefix + '_' + data_name in sdfg.arrays:
create_array = False
else:
create_array = True
out_data_node = OutLocalStorage.apply_to(
sdfg,
dict(array=data_name,
prefix=prefix,
create_array=create_array),
verify=False,
save=False,
node_a=inner_map_exit,
node_b=outer_map_exit)
out_data_node.desc(sdfg).location['gpu'] = symbolic_gpu_id
out_data_node.desc(
sdfg).storage = dtypes.StorageType.GPU_Global
# Add Transients for write-conflict resolution
if len(wcr_data) != 0:
nsdfg = AccumulateTransient.apply_to(
sdfg,
options=dict(array_identity_dict=wcr_data, prefix=prefix),
map_exit=inner_map_exit,
outer_map_exit=outer_map_exit)
nsdfg.schedule = dtypes.ScheduleType.GPU_Multidevice
nsdfg.location['gpu'] = symbolic_gpu_id
for transient_node in graph.successors(nsdfg):
if isinstance(transient_node, nodes.AccessNode):
transient_node.desc(sdfg).location['gpu'] = symbolic_gpu_id
transient_node.desc(
sdfg).storage = dtypes.StorageType.GPU_Global
nsdfg.sdfg.arrays[
transient_node.label].location['gpu'] = symbolic_gpu_id
nsdfg.sdfg.arrays[
transient_node.
label].storage = dtypes.StorageType.GPU_Global
infer_types.set_default_schedule_storage_types_and_location(
nsdfg.sdfg, dtypes.ScheduleType.GPU_Multidevice,
symbolic_gpu_id)
# Remove the parameter of the outer_map from the sdfg symbols,
# as it got added as a symbol in StripMining.
if outer_map.params[0] in sdfg.free_symbols:
sdfg.remove_symbol(outer_map.params[0])
