def simulate(hostname, kernel, args):
filename = f'results/predicted/{hostname}_{kernel}.pkl.gz'
print(hostname, kernel, end=" ")
if os.path.exists(filename):
print(f"exists {filename}")
return
data = []
machine = get_machine_model(hostname)
kc_kernel = get_kc_kernel(kernel)
no_lcp = False
for c, arg in enumerate(args):
# reset kernel state
kc_kernel.clear_state()
for i, a in enumerate(arg[1:]):
kc_kernel.set_constant(f'D{i}', a)
csp = CacheSimulationPredictor(kc_kernel, machine)
csp_infos = csp.get_infos()
lcp_infos = None
if not no_lcp:
try:
lcp = LayerConditionPredictor(kc_kernel, machine)
lcp_infos = lcp.get_infos()
except ValueError:
no_lcp = True
row = {'hostname': hostname, 'kernel': kernel, 'dimensions': arg[1:]}
# Typically ['L1', 'L2', 'L3', 'MEM']
levels = [mh['level'] for mh in machine['memory hierarchy']]
stat_types = ['loads', 'misses', 'stores', 'evicts']
cs_row = {'source': 'pycachesim'}
cs_row.update(row)
for st in stat_types:
for level, stat in zip(levels, getattr(csp, f'get_{st}')()):
if level == "MEM" and st in ['misses', 'evicts']:
continue # makes no sense
cs_row[level + '_' + st] = float(stat)
cs_row['raw'] = csp_infos
data.append(cs_row)
if not no_lcp:
lc_row = {'source': 'layer-conditions'}
lc_row.update(row)
for st in stat_types:
for level, stat in zip(levels, getattr(lcp, f'get_{st}')()):
if level == "MEM" and st in ['misses', 'evicts']:
continue # makes no sense
lc_row[level + '_' + st] = float(stat)
lc_row['raw'] = lcp_infos
data.append(lc_row)
if c % (len(args) // 10) == 0:
print(".", end='', flush=True)
df = pd.DataFrame(data)
os.makedirs('results/predicted', exist_ok=True)
df.to_pickle(f'results/predicted/{hostname}_{kernel}.pkl.gz')
print(f"saved {filename}")
return data
def __init__(self, kernel, machine, args=None, parser=None, cores=1,
cache_predictor=CacheSimulationPredictor, verbose=0):
"""
Create Execcution-Cache-Memory data model from kernel and machine objects.
*kernel* is a Kernel object
*machine* describes the machine (cpu, cache and memory) characteristics
*args* (optional) are the parsed arguments from the comand line
If *args* is None, *cores*, *cache_predictor* and *verbose* are taken into account,
otherwise *args* takes precedence.
"""
self.kernel = kernel
self.machine = machine
self._args = args
self._parser = parser
self.results = None
if args:
self.verbose = self._args.verbose
self.cores = self._args.cores
if self._args.cache_predictor == 'SIM':
self.predictor = CacheSimulationPredictor(self.kernel, self.machine, self.cores)
elif self._args.cache_predictor == 'LC':
self.predictor = LayerConditionPredictor(self.kernel, self.machine, self.cores)
else:
raise NotImplementedError("Unknown cache predictor, only LC (layer condition) and "
"SIM (cache simulation with pycachesim) is supported.")
else:
self.cores = cores
self.predictor = cache_predictor(self.kernel, self.machine, self.cores)
self.verbose = verbose
def calculate_cache_access(self):
"""Dispatch to cache predictor to get cache stats."""
if self._args.cache_predictor == 'SIM':
self.predictor = CacheSimulationPredictor(self.kernel, self.machine, self._args.cores)
elif self._args.cache_predictor == 'LC':
self.predictor = LayerConditionPredictor(self.kernel, self.machine, self._args.cores)
else:
raise NotImplementedError("Unknown cache predictor, only LC (layer condition) and "
"SIM (cache simulation with pycachesim) is supported.")
self.results = {'cycles': [], # will be filled by caclculate_cycles()
'misses': self.predictor.get_misses(),
'hits': self.predictor.get_hits(),
'evicts': self.predictor.get_evicts(),
'verbose infos': self.predictor.get_infos()} # only for verbose outputs
def __init__(self,
kernel,
machine,
args=None,
parser=None,
cores=1,
cache_predictor=LayerConditionPredictor,
verbose=0):
"""
Create roofline model from kernel and machine objects.
*kernel* is a Kernel object
*machine* describes the machine (cpu, cache and memory) characteristics
*args* (optional) are the parsed arguments from the comand line
If *args* is None, *asm_block*, *pointer_increment* and *verbose* will be used, otherwise
*args* takes precedence.
"""
self.kernel = kernel
self.machine = machine
self._args = args
self._parser = parser
self.results = None
if args:
self.verbose = self._args.verbose
self.cores = self._args.cores
if self._args.cache_predictor == 'SIM':
self.predictor = CacheSimulationPredictor(
self.kernel, self.machine, self.cores)
elif self._args.cache_predictor == 'LC':
self.predictor = LayerConditionPredictor(
self.kernel, self.machine, self.cores)
else:
raise NotImplementedError(
"Unknown cache predictor, only LC (layer condition) and "
"SIM (cache simulation with pycachesim) is supported.")
else:
self.cores = cores
self.predictor = cache_predictor(self.kernel, self.machine,
self.cores)
self.verbose = verbose
if sum(self.kernel._flops.values()) == 0:
raise ValueError(
"The Roofline model requires that the sum of FLOPs is non-zero."
)
def test_non_variable_accesse(self):
kernel = KernelCode('''
double Y[s][n];
double F[s][n];
double A[s][s];
double y[n];
double h;
for (int l = 0; l < s; ++l)
for (int j = 0; j < n; ++j)
Y[l][j] = A[l][0] * F[0][j] * h + y[j];
''', machine=self.machine)
kernel.set_constant('s', 4)
kernel.set_constant('n', 1000000)
lcp = LayerConditionPredictor(kernel, self.machine)
self.assertEqual(lcp.get_evicts(), [1, 1, 1, 0])
self.assertEqual(lcp.get_misses(), [3, 3, 3, 0])
self.assertEqual(lcp.get_hits(), [0, 0, 0, 3])
def calculate_cache_access(self):
if self._args.cache_predictor == 'SIM':
self.predictor = CacheSimulationPredictor(self.kernel,
self.machine)
elif self._args.cache_predictor == 'LC':
self.predictor = LayerConditionPredictor(self.kernel, self.machine)
else:
raise NotImplementedError(
"Unknown cache predictor, only LC (layer condition) and "
"SIM (cache simulation with pycachesim) is supported.")
self.results = {
'misses': self.predictor.get_misses(),
'hits': self.predictor.get_hits(),
'evicts': self.predictor.get_evicts(),
'verbose infos':
self.predictor.get_infos(), # only for verbose outputs
'bottleneck level': 0,
'mem bottlenecks': []
}
element_size = self.kernel.datatypes_size[self.kernel.datatype]
cacheline_size = float(self.machine['cacheline size'])
elements_per_cacheline = int(cacheline_size // element_size)
total_flops = sum(self.kernel._flops.values()) * elements_per_cacheline
# TODO let user choose threads_per_core:
threads_per_core = 1
# Compile relevant information
# CPU-L1 stats (in bytes!)
# We compile CPU-L1 stats on our own, because cacheprediction only works on cache lines
read_offsets, write_offsets = zip(*list(
self.kernel.compile_global_offsets(
iteration=range(0, elements_per_cacheline))))
read_offsets = set(
[item for sublist in read_offsets for item in sublist])
write_offsets = set(
[item for sublist in write_offsets for item in sublist])
write_streams = len(write_offsets)
read_streams = len(read_offsets) + write_streams # write-allocate
total_loads = read_streams * element_size
total_evicts = write_streams * element_size
bw, measurement_kernel = self.machine.get_bandwidth(
0,
read_streams,
write_streams,
threads_per_core,
cores=self._args.cores)
# Calculate performance (arithmetic intensity * bandwidth with
# arithmetic intensity = flops / bytes loaded )
if total_loads == 0:
# This happens in case of full-caching
arith_intens = None
performance = None
else:
arith_intens = float(total_flops) / total_loads
performance = arith_intens * float(bw)
self.results['mem bottlenecks'].append({
'performance':
PrefixedUnit(performance, 'FLOP/s'),
'level':
self.machine['memory hierarchy'][0]['level'],
'arithmetic intensity':
arith_intens,
'bw kernel':
measurement_kernel,
'bandwidth':
bw,
'bytes transfered':
total_loads
})
if performance <= self.results.get('min performance', performance):
self.results['bottleneck level'] = len(
self.results['mem bottlenecks']) - 1
self.results['min performance'] = performance
# for other cache and memory levels:
for cache_level, cache_info in list(
enumerate(self.machine['memory hierarchy']))[:-1]:
# Compiling stats (in bytes!)
total_misses = self.results['misses'][cache_level] * cacheline_size
total_evicts = self.results['evicts'][cache_level] * cacheline_size
# choose bw according to cache level and problem
# first, compile stream counts at current cache level
# write-allocate is allready resolved above
read_streams = self.results['misses'][cache_level]
write_streams = self.results['evicts'][cache_level]
# second, try to find best fitting kernel (closest to stream seen stream counts):
bw, measurement_kernel = self.machine.get_bandwidth(
cache_level + 1,
read_streams,
write_streams,
threads_per_core,
cores=self._args.cores)
# Calculate performance (arithmetic intensity * bandwidth with
# arithmetic intensity = flops / bytes transfered)
bytes_transfered = total_misses + total_evicts
if bytes_transfered == 0:
# This happens in case of full-caching
arith_intens = float('inf')
performance = float('inf')
else:
arith_intens = float(total_flops) / bytes_transfered
performance = arith_intens * float(bw)
self.results['mem bottlenecks'].append({
'performance':
PrefixedUnit(performance, 'FLOP/s'),
'level':
(self.machine['memory hierarchy'][cache_level + 1]['level']),
'arithmetic intensity':
arith_intens,
'bw kernel':
measurement_kernel,
'bandwidth':
bw,
'bytes transfered':
bytes_transfered
})
if performance < self.results.get('min performance', performance):
self.results['bottleneck level'] = len(
self.results['mem bottlenecks']) - 1
self.results['min performance'] = performance
return self.results
def analyze(self):
"""Run complete analysis."""
lcp = LayerConditionPredictor(self.kernel, self.machine, symbolic=True)
self.results = lcp.results