def run_pass(self, state):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert state.func_ir
parfor_pass = _parfor_ParforPass(
state.func_ir,
state.type_annotation.typemap,
state.type_annotation.calltypes,
state.return_type,
state.typingctx,
state.targetctx,
state.flags.auto_parallel,
state.flags,
state.metadata,
state.parfor_diagnostics,
)
parfor_pass.run()
if config.DEBUG or config.DUMP_IR:
name = state.func_ir.func_id.func_qualname
print(("IR DUMP: %s" % name).center(80, "-"))
state.func_ir.dump()
return True
def run_pass(self, state):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert state.func_ir
parfor_pass = _parfor_ParforPass(
state.func_ir,
state.type_annotation.typemap,
state.type_annotation.calltypes,
state.return_type,
state.typingctx,
state.flags.auto_parallel,
state.flags,
state.parfor_diagnostics,
)
parfor_pass.run()
remove_dels(state.func_ir.blocks)
# check the parfor pass worked and warn if it didn't
has_parfor = False
for blk in state.func_ir.blocks.values():
for stmnt in blk.body:
if isinstance(stmnt, Parfor):
has_parfor = True
break
else:
continue
break
if not has_parfor:
# parfor calls the compiler chain again with a string
if not (
config.DISABLE_PERFORMANCE_WARNINGS
or state.func_ir.loc.filename == "<string>"
):
url = (
"http://numba.pydata.org/numba-doc/latest/user/"
"parallel.html#diagnostics"
)
msg = (
"\nThe keyword argument 'parallel=True' was specified "
"but no transformation for parallel execution was "
"possible.\n\nTo find out why, try turning on parallel "
"diagnostics, see %s for help."
% url
)
warnings.warn(errors.NumbaPerformanceWarning(msg, state.func_ir.loc))
# Add reload function to initialize the parallel backend.
state.reload_init.append(_reload_parfors)
return True