def __init__(self, fname, ivar = "TASK-COUNT",
non_decreasing = True,
custom_mapping = None,
per_cpu_task_counts = False,
num_cpus = None):
self.ivar = ivar
self.fields = []
self.custom_mapping = custom_mapping
data = load_column_csv(fname, convert=float)
if not ivar in data.by_name:
raise IOError, "Independent variable column {} is missing".format(ivar)
for field in data.by_name:
if field != ivar:
self.fields.append(field)
points = zip(data.by_name[ivar], data.by_name[field])
if per_cpu_task_counts:
points = [(num_cpus * x, y) for (x, y) in points]
if non_decreasing:
if non_decreasing:
self.__dict__[self.field_name(field)] = monotonic_pwlin(points)
else:
self.__dict__[self.field_name(field)] = piece_wise_linear(points)
def load_approximations(self,
fname,
non_decreasing=True,
custom_fields=None,
per_cpu_task_counts=False,
num_cpus=None):
if custom_fields is None:
custom_fields = []
data = load_column_csv(fname, convert=float)
if not 'TASK-COUNT' in data.by_name:
raise IOError, "TASK-COUNT column is missing"
# initialize custom fields, if any
for (name, field) in custom_fields:
self.__dict__[field] = const(0)
for (name, field) in Overheads.FIELD_MAPPING + custom_fields:
if name in data.by_name:
points = zip(data.by_name['TASK-COUNT'], data.by_name[name])
if per_cpu_task_counts:
points = [(num_cpus * x, y) for (x, y) in points]
if non_decreasing:
self.__dict__[field] = monotonic_pwlin(points)
else:
self.__dict__[field] = piece_wise_linear(points)
def load_approximations(self, fname, non_decreasing=True):
data = load_column_csv(fname, convert=float)
if not 'TASK-COUNT' in data.by_name:
raise IOError, "TASK-COUNT column is missing"
for (name, field) in Overheads.FIELD_MAPPING:
if name in data.by_name:
points = zip(data.by_name['TASK-COUNT'], data.by_name[name])
if non_decreasing:
self.__dict__[field] = monotonic_pwlin(points)
else:
self.__dict__[field] = piece_wise_linear(points)
def load_approximations(self, fname, non_decreasing=True):
data = load_column_csv(fname, convert=float)
if not 'TASK-COUNT' in data.by_name:
raise IOError, "TASK-COUNT column is missing"
for (name, field) in Overheads.FIELD_MAPPING:
if name in data.by_name:
points = zip(data.by_name['TASK-COUNT'], data.by_name[name])
if non_decreasing:
self.__dict__[field] = monotonic_pwlin(points)
else:
self.__dict__[field] = piece_wise_linear(points)
def from_file(fname, non_decreasing=True):
data = load_column_csv(fname, convert=float)
if not 'WSS' in data.by_name:
raise IOError, 'WSS column is missing'
o = CacheDelay()
for idx, name in CacheDelay.MAPPING:
if name in data.by_name:
points = zip(data.by_name['WSS'], data.by_name[name])
if non_decreasing:
o.mem_hierarchy[idx] = monotonic_pwlin(points)
else:
o.mem_hierarchy[idx] = piece_wise_linear(points)
o.__dict__[name] = o.mem_hierarchy[idx]
return o
def from_file(fname, non_decreasing=True):
data = load_column_csv(fname, convert=float)
if not 'WSS' in data.by_name:
raise IOError, 'WSS column is missing'
o = CacheDelay()
for idx, name in CacheDelay.MAPPING:
if name in data.by_name:
points = zip(data.by_name['WSS'], data.by_name[name])
if non_decreasing:
o.mem_hierarchy[idx] = monotonic_pwlin(points)
else:
o.mem_hierarchy[idx] = piece_wise_linear(points)
o.__dict__[name] = o.mem_hierarchy[idx]
return o
def load_approximations(self, fname, non_decreasing=True, custom_fields=None,
per_cpu_task_counts=False, num_cpus=None):
if custom_fields is None:
custom_fields = []
data = load_column_csv(fname, convert=float)
if not 'TASK-COUNT' in data.by_name:
raise IOError, "TASK-COUNT column is missing"
# initialize custom fields, if any
for (name, field) in custom_fields:
self.__dict__[field] = const(0)
for (name, field) in Overheads.FIELD_MAPPING + custom_fields:
if name in data.by_name:
points = zip(data.by_name['TASK-COUNT'], data.by_name[name])
if per_cpu_task_counts:
points = [(num_cpus * x, y) for (x, y) in points]
if non_decreasing:
self.__dict__[field] = monotonic_pwlin(points)
else:
self.__dict__[field] = piece_wise_linear(points)
def setUp(self):
self.f = m.lin(1, 3)
self.c = m.const(123)
self.pwlin = m.monotonic_pwlin([(0, 1), (1, 0), (1, 4), (2, 5)])
def setUp(self):
self.f = m.lin(1, 3)
self.c = m.const(123)
self.pwlin = m.monotonic_pwlin([(0, 1), (1, 0), (1, 4), (2, 5)])
