def _specialize_iet(self, iet, **kwargs):
warning("The OPS backend is still work-in-progress")
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
ops_block = OpsBlock('block')
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for section, trees in find_affine_trees(iet).items():
dims.append(len(trees[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(trees[0].root))
symbols -= set(FindSymbols('defines').visit(trees[0].root))
to_dat |= symbols
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(create_ops_dat(f, name_to_ops_dat, ops_block))
for n, (section, trees) in enumerate(find_affine_trees(iet).items()):
pre_loop, ops_kernel = opsit(trees, n)
pre_time_loop.extend(pre_loop)
self._ops_kernels.append(ops_kernel)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block,
namespace['ops_decl_block'](dims[0], Literal('"block"')))))
self._headers.append(namespace['ops_define_dimension'](dims[0]))
self._includes.append('stdio.h')
body = [
ops_init, ops_block_init, *pre_time_loop, ops_partition, iet,
ops_exit
]
return List(body=body)
def make_efunc(name,
iet,
dynamic_parameters=None,
retval='void',
prefix='static'):
"""
Create an ElementalFunction from (a sequence of) perfectly nested Iterations.
"""
# Arrays are by definition (vector) temporaries, so if they are written
# within `iet`, they can also be declared and allocated within the `efunc`
items = FindSymbols().visit(iet)
local = [
i.write for i in FindNodes(Expression).visit(iet) if i.write.is_Array
]
external = [i for i in items if i.is_Tensor and i not in local]
# Insert array casts
casts = [ArrayCast(i) for i in external]
iet = List(body=casts + [iet])
# Insert declarations
iet = iet_insert_C_decls(iet, external)
# The Callable parameters
params = [i for i in derive_parameters(iet) if i not in local]
return ElementalFunction(name, iet, retval, params, prefix,
dynamic_parameters)
def _specialize_iet(self, iet, **kwargs):
warning("The OPS backend is still work-in-progress")
affine_trees = find_affine_trees(iet).items()
# If there is no affine trees, then there is no loop to be optimized using OPS.
if not affine_trees:
return iet
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for _, tree in affine_trees:
dims.append(len(tree[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(tree[0].root))
symbols -= set(FindSymbols('defines').visit(tree[0].root))
to_dat |= symbols
# Create the OPS block for this problem
ops_block = OpsBlock('block')
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block,
namespace['ops_decl_block'](dims[0], Literal('"block"')))))
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
after_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(
list(create_ops_dat(f, name_to_ops_dat, ops_block)))
# To return the result to Devito, it is necessary to copy the data
# from the dat object back to the CPU memory.
after_time_loop.extend(
create_ops_fetch(f, name_to_ops_dat,
self.time_dimension.extreme_max))
# Generate ops kernels for each offloadable iteration tree
mapper = {}
for n, (_, tree) in enumerate(affine_trees):
pre_loop, ops_kernel, ops_par_loop_call = opsit(
tree, n, name_to_ops_dat, ops_block, dims[0])
pre_time_loop.extend(pre_loop)
self._func_table[namespace['ops_kernel_file'](ops_kernel.name)] = \
MetaCall(ops_kernel, False)
mapper[tree[0].root] = ops_par_loop_call
mapper.update({i.root: mapper.get(i.root)
for i in tree}) # Drop trees
iet = Transformer(mapper).visit(iet)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
self._headers.append(namespace['ops_define_dimension'](dims[0]))
self._includes.extend(['stdio.h', 'ops_seq.h'])
body = [
ops_init, ops_block_init, *pre_time_loop, ops_partition, iet,
*after_time_loop, ops_exit
]
return List(body=body)
def make_ops_kernels(iet):
warning("The OPS backend is still work-in-progress")
affine_trees = find_affine_trees(iet).items()
# If there is no affine trees, then there is no loop to be optimized using OPS.
if not affine_trees:
return iet, {}
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for _, tree in affine_trees:
dims.append(len(tree[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(tree[0].root))
symbols -= set(FindSymbols('defines').visit(tree[0].root))
to_dat |= symbols
# Create the OPS block for this problem
ops_block = OpsBlock('block')
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block, namespace['ops_decl_block'](dims[0],
Literal('"block"')))))
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
after_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(
list(create_ops_dat(f, name_to_ops_dat, ops_block)))
# Copy data from device to host
after_time_loop.extend(
create_ops_fetch(f, name_to_ops_dat, f.grid.time_dim.extreme_max))
# Generate ops kernels for each offloadable iteration tree
mapper = {}
ffuncs = []
for n, (_, tree) in enumerate(affine_trees):
pre_loop, ops_kernel, ops_par_loop_call = opsit(
tree, n, name_to_ops_dat, ops_block, dims[0])
pre_time_loop.extend(pre_loop)
ffuncs.append(ops_kernel)
mapper[tree[0].root] = ops_par_loop_call
mapper.update({i.root: mapper.get(i.root) for i in tree}) # Drop trees
iet = Transformer(mapper).visit(iet)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
iet = iet._rebuild(body=flatten([
ops_init, ops_block_init, pre_time_loop, ops_partition, iet.body,
after_time_loop, ops_exit
]))
return iet, {
'includes': ['stdio.h', 'ops_seq.h'],
'ffuncs': ffuncs,
'headers': [namespace['ops_define_dimension'](dims[0])]
}
def _make_thread_func(name, iet, root, threads, sregistry):
sid = SharedData._symbolic_id
sdeviceid = SharedData._symbolic_deviceid
# Create the SharedData, that is the data structure that will be used by the
# main thread to pass information dows to the child thread(s)
required, parameters, dynamic_parameters = diff_parameters(iet, root, [sid])
parameters = sorted(parameters, key=lambda i: i.is_Function) # Allow casting
sdata = SharedData(name=sregistry.make_name(prefix='sdata'), npthreads=threads.size,
fields=required, dynamic_fields=dynamic_parameters)
# Create a Callable to initialize `sdata` with the known const values
sbase = sdata.symbolic_base
iname = 'init_%s' % sdata.dtype._type_.__name__
ibody = [DummyExpr(FieldFromPointer(i._C_name, sbase), i._C_symbol)
for i in parameters]
ibody.extend([
BlankLine,
DummyExpr(FieldFromPointer(sdata._field_id, sbase), sid),
DummyExpr(FieldFromPointer(sdata._field_deviceid, sbase), sdeviceid),
DummyExpr(FieldFromPointer(sdata._field_flag, sbase), 1)
])
iparameters = parameters + [sdata, sid, sdeviceid]
isdata = Callable(iname, ibody, 'void', iparameters, 'static')
# Prepend the SharedData fields available upon thread activation
preactions = [DummyExpr(i, FieldFromPointer(i.name, sbase))
for i in dynamic_parameters]
# Append the flag reset
postactions = [List(body=[
BlankLine,
DummyExpr(FieldFromPointer(sdata._field_flag, sbase), 1)
])]
iet = List(body=preactions + [iet] + postactions)
# The thread has work to do when it receives the signal that all locks have
# been set to 0 by the main thread
iet = Conditional(CondEq(FieldFromPointer(sdata._field_flag, sbase), 2), iet)
# The thread keeps spinning until the alive flag is set to 0 by the main thread
iet = WhileAlive(CondNe(FieldFromPointer(sdata._field_flag, sbase), 0), iet)
# pthread functions expect exactly one argument, a void*, and must return void*
tretval = 'void*'
tparameter = VoidPointer('_%s' % sdata.name)
# Unpack `sdata`
symbol_names = {i.name for i in FindSymbols('free-symbols').visit(iet)}
unpack = [PointerCast(sdata, tparameter), BlankLine]
for i in parameters:
if i.is_AbstractFunction:
unpack.append(Dereference(i, sdata))
if i.name in symbol_names:
unpack.append(PointerCast(i))
else:
unpack.append(DummyExpr(i, FieldFromPointer(i.name, sbase)))
unpack.append(DummyExpr(sid, FieldFromPointer(sdata._field_id, sbase)))
unpack.append(DummyExpr(sdeviceid, FieldFromPointer(sdata._field_deviceid, sbase)))
unpack.append(BlankLine)
iet = List(body=unpack + [iet, BlankLine, Return(Macro('NULL'))])
tfunc = ThreadFunction(name, iet, tretval, tparameter, 'static')
return tfunc, isdata, sdata