def stage_parfor_pass(self):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert self.func_ir
parfor_pass = ParforPass(self.func_ir, self.type_annotation.typemap,
self.type_annotation.calltypes, self.return_type, self.typingctx,
self.flags.auto_parallel, self.flags)
parfor_pass.run()
if config.WARNINGS:
# check the parfor pass worked and warn if it didn't
has_parfor = False
for blk in self.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 self.func_ir.loc.filename == '<string>':
msg = ("parallel=True was specified but no transformation"
" for parallel execution was possible.")
warnings.warn_explicit(
msg,
errors.NumbaWarning,
self.func_id.filename,
self.func_id.firstlineno
)
def stage_parfor_pass(self):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert self.func_ir
parfor_pass = ParforPass(self.func_ir, self.type_annotation.typemap,
self.type_annotation.calltypes, self.return_type, self.typingctx,
self.flags.auto_parallel, self.flags)
parfor_pass.run()
if config.WARNINGS:
# check the parfor pass worked and warn if it didn't
has_parfor = False
for blk in self.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 self.func_ir.loc.filename == '<string>':
msg = ("parallel=True was specified but no transformation"
" for parallel execution was possible.")
warnings.warn_explicit(
msg,
errors.NumbaWarning,
self.func_id.filename,
self.func_id.firstlineno
)
def stage_parfor_pass(self):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert self.func_ir
parfor_pass = ParforPass(self.func_ir, self.type_annotation.typemap,
self.type_annotation.calltypes, self.return_type, self.typingctx,
self.flags.auto_parallel, self.flags, self.parfor_diagnostics)
parfor_pass.run()
# check the parfor pass worked and warn if it didn't
has_parfor = False
for blk in self.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 self.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,
self.func_ir.loc))
def stage_parfor_pass(self):
"""
Convert data-parallel computations into Parfor nodes
"""
# Ensure we have an IR and type information.
assert self.func_ir
parfor_pass = ParforPass(self.func_ir, self.type_annotation.typemap,
self.type_annotation.calltypes,
self.return_type)
parfor_pass.run()
