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import generator as gen
import random
import ecrts14.partition as partition
import schedcat.sched.split_heuristic as split
import ecrts14.ecrts14 as ecrts14
import ecrts14.tests as tests
from ecrts14.ecrts14 import create_pgm_task_set
from ecrts14.ecrts14 import get_overheads
from config.config import FILES,PARAMS
from schedcat.overheads.model import Overheads, CacheDelay, ConsumerOverheads, ProducerOverheads
from schedcat.generator.tasksets import NAMED_UTILIZATIONS
from schedcat.mapping.binpack import DidNotFit
TP_TBASE = """#for $t in $task_set
{} $t.cost $t.period
#end for"""
TP_GLOB_TASK = TP_TBASE.format("")
TP_PART_TASK = TP_TBASE.format("-p $t.cpu")
TP_CLST_TASK = TP_TBASE.format("-p $t.cluster -z $t.cluster_sz")
PARTITION_METHOD = {
'no_cache' : tests.test_partition_no_cache,
'parallel' : tests.test_partition_parallel,
'cache_aware' : tests.test_partition_cache_aware,
'cache_aware_parallel' : tests.test_partition_cache_aware_parallel,
'cache_aware_edges' : tests.test_partition_cache_aware_edges,
'cache_aware_bfs' : tests.test_partition_cache_aware_bfs,
'cache_aware_dfs' : tests.test_partition_cache_aware_dfs,
}
class EdfGenerator(gen.Generator):
'''Creates sporadic task sets with the most common Litmus options.'''
def __init__(self, scheduler, templates, options, params):
super(EdfGenerator, self).__init__(scheduler, templates,
self.__make_options() + options,
params)
def __make_options(self):
'''Return generic EDF options.'''
return [gen.Generator._dist_option('utils', 'uni-medium',
gen.NAMED_UTILIZATIONS,
'Task utilization distributions.'),
gen.Generator._dist_option('periods', 'harmonic',
gen.NAMED_PERIODS,
'Task period distributions.')]
def _create_exp(self, exp_params):
'''Create a single experiment with @exp_params in @out_dir.'''
pdist = self._create_dist('period',
exp_params['periods'],
gen.NAMED_PERIODS)
udist = self._create_dist('utilization',
exp_params['utils'],
gen.NAMED_UTILIZATIONS)
ts = self._create_taskset(exp_params, pdist, udist)
self._customize(ts, exp_params)
self._write_schedule(dict(exp_params.items() + [('task_set', ts)]))
self._write_params(exp_params)
def _customize(self, taskset, exp_params):
'''Configure a generated taskset with extra parameters.'''
pass
class EdfPgmGenerator(gen.Generator):
'''Creates sporadic task sets with the most common Litmus options.'''
def __init__(self, scheduler, templates, options, params):
super(EdfPgmGenerator, self).__init__(scheduler, templates,
self.__make_options() + options,
params)
def __make_options(self):
'''Return generic EDF options.'''
return [gen.Generator._dist_option('period', ['uni-moderate'],
ecrts14.NAMED_PERIODS_US,
'Task period distributions.'),
gen.Generator._dist_option('num_graphs', ['uni-medium'],
ecrts14.NAMED_NUM_GRAPHS,
'Number of graphs.'),
# gen.Generator._dist_option('depth_factor', ['uni-medium'],
gen.Generator._dist_option('depth_factor', ['pipeline'],
ecrts14.NAMED_HEIGHT_FACTORS,
'Depth of graphs.'),
gen.Generator._dist_option('clustering', ['L1', 'L2', 'L3', 'ALL'],
{},
'Clustering configurations'),
gen.Generator._dist_option('partitions', ['no_cache', 'parallel', 'cache_aware', 'cache_aware_parallel'],
PARTITION_METHOD,
'Partition methods.'),
gen.Generator._dist_option('node_placement', ['uniform'],
ecrts14.NAMED_SHAPES,
'The node placement of a graph.'),
# gen.Generator._dist_option('fan_out', ['uniform_3'],
gen.Generator._dist_option('fan_out', ['none'],
ecrts14.NAMED_FAN,
'The number of out edges of a node.'),
gen.Generator._dist_option('fan_in_cap', [3],
{},
'The maximum number of in-edges of a node.'),
# gen.Generator._dist_option('edge_distance', ['uniform_3'],
gen.Generator._dist_option('edge_distance', ['none'],
ecrts14.NAMED_EDGE_HOP,
'The number of hops for an edge.'),
gen.Generator._dist_option('wss', ['uni-light', 'uni-medium', 'uni-heavy'],
ecrts14.NAMED_EDGE_WSS,
'Working set size.'),
gen.Generator._dist_option('task_util', ['uni-medium'],
NAMED_UTILIZATIONS,
'Task utilization.'),
gen.Generator._dist_option('polluters', [False],
{},
'Polluters.'),
gen.Generator._dist_option('job_splitting', [True],
{},
'Job splitting.'),
gen.Generator._dist_option('ovh_type', 'max',
{},
'Overhead type.'),
gen.Generator._dist_option('heur_aggressiveness', 0.75,
{},
'heur_aggressiveness.'),
gen.Generator._dist_option('sys_util', [18.0, 13.0, 8.0],
{},
'Task set utilization.')]
def _create_exp(self, dp, ts, graphs, subts):
'''Create a single experiment with @exp_params in @out_dir.'''
ret, ts = self._customize(ts, graphs, subts, dp)
if ret:
self._write_pgm_schedule(dict(dp.items() + [('task_set', ts)] + [('graphs', graphs)] + [('sub_task_set', subts)]))
self._write_params(dict(dp.items() + [('num_tasks', len(ts)), ('num_graphs', len(graphs))]))
return ret
def _create_tasks(self, dp):
'''Create a task set.'''
ts, graphs, subts = create_pgm_task_set(dp)
return ts, graphs, subts
def _customize(self, taskset, exp_params):
'''Configure a generated taskset with extra parameters.'''
pass
class PartitionedGenerator(EdfGenerator):
def __init__(self, scheduler, templates, options, params):
super(PartitionedGenerator, self).__init__(scheduler,
templates + [TP_PART_TASK], options, params)
def _customize(self, taskset, exp_params):
cpus = exp_params['cpus']
start = 0
if exp_params['release_master']:
cpus -= 1
start = 1
# Partition using worst-fit for most even distribution
utils = [0]*cpus
tasks = [0]*cpus
for t in taskset:
t.cpu = utils.index(min(utils))
utils[t.cpu] += t.utilization()
tasks[t.cpu] += 1
# Increment by one so release master has no tasks
t.cpu += start
class PedfGenerator(PartitionedGenerator):
def __init__(self, params={}):
super(PedfGenerator, self).__init__("PSN-EDF", [], [], params)
class CedfGenerator(PartitionedGenerator):
TP_CLUSTER = "plugins/C-EDF/cluster{$level}"
CLUSTER_OPTION = gen.GenOption('level', ['L2', 'L3', 'ALL'], 'L2',
'Cache clustering level.',)
def __init__(self, params={}):
super(CedfGenerator, self).__init__("C-EDF",
[CedfGenerator.TP_CLUSTER],
[CedfGenerator.CLUSTER_OPTION],
params)
class CflSplitGenerator(EdfGenerator):
TP_CLUSTER = "plugins/C-FL-split/cluster{$level}"
CLUSTER_OPTION = gen.GenOption('level', ['L1', 'L2', 'L3', 'ALL'], 'L2',
'Cache clustering level.',)
def __init__(self, params={}):
super(CflSplitGenerator, self).__init__("C-FL-split",
[CflSplitGenerator.TP_CLUSTER, TP_CLST_TASK],
[CflSplitGenerator.CLUSTER_OPTION],
params)
def _customize(self, taskset, exp_params):
cpus = int(exp_params['cpus'])
if exp_params['level'] == 'L1':
cluster_sz = 1
elif exp_params['level'] == 'L2':
cluster_sz = 2
elif exp_params['level'] == 'L3':
cluster_sz = 6
elif exp_params['level'] == 'ALL':
cluster_sz = 24
else:
assert False
num_clusters = cpus / cluster_sz
assert num_clusters * cluster_sz == cpus
utils = [0]*num_clusters
tasks = [0]*num_clusters
for t in taskset:
t.cluster = utils.index(min(utils))
t.cluster_sz = cluster_sz
utils[t.cluster] += t.utilization()
tasks[t.cluster] += 1
class GedfGenerator(EdfGenerator):
def __init__(self, params={}):
super(GedfGenerator, self).__init__("GSN-EDF", [TP_GLOB_TASK],
[], params)
class CflSplitPgmGenerator(EdfPgmGenerator):
TP_CLUSTER = "plugins/C-FL-split/cluster{$level}"
# CLUSTER_OPTION = gen.GenOption('level', ['L1', 'L2', 'L3', 'ALL'], 'L2',
# 'Cache clustering level.',)
def __init__(self, params={}):
super(CflSplitPgmGenerator, self).__init__("C-FL-split",
[CflSplitPgmGenerator.TP_CLUSTER],
[],
params)
def _customize(self, ts, graphs, subts, dp):
exp_params = dict(dp.items())
cpus = int(exp_params['cpus'])
if exp_params['level'] == 'L1':
cluster_sz = 1
elif exp_params['level'] == 'L2':
cluster_sz = 2
elif exp_params['level'] == 'L3':
cluster_sz = 6
elif exp_params['level'] == 'ALL':
cluster_sz = 24
else:
assert False
exp_params['fan_in_cap'] = int(exp_params['fan_in_cap'])
dp.nr_clusters = cpus / cluster_sz
assert dp.nr_clusters * cluster_sz == cpus
overheads = get_overheads(dp, dp.system)
# do the partition here
ts = partition.clear_partitioning(ts)
if overheads.consumer is not None:
for t in ts:
overheads.consumer.place_production(t)
# the test will compute job splits if it is in the desgin point.
is_sched, ts = PARTITION_METHOD[exp_params['partitions']](ts, graphs, subts, dp, overheads)
if exp_params['level'] == 'ALL':
# kludge: assume global task sets are always schedulable
is_sched = True
if is_sched:
# compute the minimum time to produce/consume, so this can be discounted
# from the execution time during runtime
for ti in ts:
consume_amount = ti.wss
produce_amount = sum([e.wss for e in ti.node.outEdges])
consume_time = overheads.read(consume_amount)
produce_time = overheads.write(produce_amount)
ti.cost_discount = consume_time + produce_time
return is_sched, ts
|
