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 __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 = {}
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 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 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 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 __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.")
class ECMData(object):
"""Representation of Data portion of the Execution-Cache-Memory Model."""
name = "Execution-Cache-Memory (data transfers only)"
@classmethod
def configure_arggroup(cls, parser):
"""Configure argument group of parser."""
pass
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."""
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 calculate_cycles(self):
"""
Calculate performance model cycles from cache stats.
calculate_cache_access() needs to have been execute before.
"""
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = float(
self.machine['cacheline size']) // element_size
misses, evicts = (self.predictor.get_misses(),
self.predictor.get_evicts())
for cache_level, cache_info in list(
enumerate(self.machine['memory hierarchy']))[:-1]:
cache_cycles = cache_info['cycles per cacheline transfer']
if cache_cycles is not None:
# only cache cycles count
cycles = (misses[cache_level] +
evicts[cache_level]) * cache_cycles
else:
# Memory transfer
# we use bandwidth to calculate cycles and then add panalty cycles (if given)
# choose bw according to cache level and problem
# first, compile stream counts at current cache level
# write-allocate is allready resolved in cache predictor
read_streams = misses[cache_level]
write_streams = evicts[cache_level]
# second, try to find best fitting kernel (closest to stream seen stream counts):
threads_per_core = 1
bw, measurement_kernel = self.machine.get_bandwidth(
cache_level + 1, read_streams, write_streams,
threads_per_core)
# calculate cycles
cycles = float(misses[cache_level] + evicts[cache_level]) * \
float(elements_per_cacheline) * float(element_size) * \
float(self.machine['clock']) / float(bw)
# add penalty cycles for each read stream
if 'penalty cycles per read stream' in cache_info:
cycles += misses[cache_level] * \
cache_info['penalty cycles per read stream']
self.results.update({
'memory bandwidth kernel': measurement_kernel,
'memory bandwidth': bw
})
self.results['cycles'].append(('{}-{}'.format(
cache_info['level'],
self.machine['memory hierarchy'][cache_level + 1]['level']),
cycles))
# TODO remove the following by makeing testcases more versatile:
self.results['{}-{}'.format(
cache_info['level'],
self.machine['memory hierarchy'][cache_level +
1]['level'])] = cycles
return self.results
def analyze(self):
"""Run complete anaylysis and return results."""
self.calculate_cache_access()
self.calculate_cycles()
return self.results
def conv_cy(self, cy_cl, unit, default='cy/CL'):
"""Convert cycles (cy/CL) to other units, such as FLOP/s or It/s."""
if not isinstance(cy_cl, PrefixedUnit):
cy_cl = PrefixedUnit(cy_cl, '', 'cy/CL')
if not unit:
unit = default
clock = self.machine['clock']
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(
self.machine['cacheline size']) // element_size
if cy_cl != 0:
it_s = clock / cy_cl * elements_per_cacheline
it_s.unit = 'It/s'
else:
it_s = PrefixedUnit('inf It/S')
flops_per_it = sum(self.kernel._flops.values())
performance = it_s * flops_per_it
performance.unit = 'FLOP/s'
cy_it = cy_cl * elements_per_cacheline
cy_it.unit = 'cy/It'
return {
'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance
}[unit]
def report(self, output_file=sys.stdout):
"""Print generated model data in human readable format."""
if self.verbose > 1:
print('{}'.format(pformat(self.results['verbose infos'])),
file=output_file)
for level, cycles in self.results['cycles']:
print('{} = {}'.format(
level, self.conv_cy(float(cycles), self._args.unit)),
file=output_file)
if self.verbose > 1:
if 'memory bandwidth kernel' in self.results:
print('memory cycles based on {} kernel with {}'.format(
self.results['memory bandwidth kernel'],
self.results['memory bandwidth']),
file=output_file)
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
class Roofline(object):
"""
class representation of the Roofline Model
more info to follow...
"""
name = "Roofline"
@classmethod
def configure_arggroup(cls, parser):
pass
def __init__(self, kernel, machine, args=None, parser=None):
"""
*kernel* is a Kernel object
*machine* describes the machine (cpu, cache and memory) characteristics
*args* (optional) are the parsed arguments from the comand line
"""
self.kernel = kernel
self.machine = machine
self._args = args
self._parser = parser
if args:
# handle CLI info
pass
if sum(self.kernel._flops.values()) == 0:
raise ValueError(
"The Roofline model requires that the sum of FLOPs is non-zero."
)
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):
self.calculate_cache_access()
def conv_perf(self, performance, unit, default='FLOP/s'):
'''Convert performance (FLOP/s) to other units, such as It/s or cy/CL'''
if not unit:
unit = default
clock = self.machine['clock']
flops_per_it = sum(self.kernel._flops.values())
it_s = performance / flops_per_it
it_s.unit = 'It/s'
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(float(
self.machine['cacheline size'])) / element_size
cy_cl = clock / it_s * elements_per_cacheline
cy_cl.unit = 'cy/CL'
cy_it = clock / it_s
cy_it.unit = 'cy/It'
return {
'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance
}[unit]
def report(self, output_file=sys.stdout):
precision = 'DP' if self.kernel.datatype == 'double' else 'SP'
max_flops = self.machine['clock']*self._args.cores * \
self.machine['FLOPs per cycle'][precision]['total']
max_flops.unit = "FLOP/s"
if self._args and self._args.verbose >= 3:
print('{}'.format(pformat(self.results)), file=output_file)
if self._args and self._args.verbose >= 1:
print('{}'.format(pformat(self.results['verbose infos'])),
file=output_file)
print('Bottlenecks:', file=output_file)
print(
' level | a. intensity | performance | bandwidth | bandwidth kernel',
file=output_file)
print(
'--------+--------------+-----------------+--------------+-----------------',
file=output_file)
print(' CPU | | {!s:>15} | |'.format(
self.conv_perf(max_flops, self._args.unit)),
file=output_file)
for b in self.results['mem bottlenecks']:
print(
'{level:>7} | {arithmetic intensity:>5.2} FLOP/B | {!s:>15} |'
' {bandwidth!s:>12} | {bw kernel:<8}'.format(
self.conv_perf(b['performance'], self._args.unit),
**b),
file=output_file)
print('', file=output_file)
if self.results['min performance'] > max_flops:
# CPU bound
print('CPU bound with {} cores(s)'.format(self._args.cores),
file=output_file)
print('{!s} due to CPU max. FLOP/s'.format(max_flops),
file=output_file)
else:
# Cache or mem bound
print('Cache or mem bound with {} core(s)'.format(
self._args.cores),
file=output_file)
bottleneck = self.results['mem bottlenecks'][
self.results['bottleneck level']]
print(
'{!s} due to {} transfer bottleneck (bw with from {} benchmark)'
.format(
self.conv_perf(bottleneck['performance'], self._args.unit),
bottleneck['level'], bottleneck['bw kernel']),
file=output_file)
print('Arithmetic Intensity: {:.2f} FLOP/B'.format(
bottleneck['arithmetic intensity']),
file=output_file)
class ECMData(PerformanceModel):
"""Representation of Data portion of the Execution-Cache-Memory Model."""
name = "Execution-Cache-Memory (data transfers only)"
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 = {}
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."""
self.results.update({
'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 calculate_cycles(self):
"""
Calculate performance model cycles from cache stats.
calculate_cache_access() needs to have been execute before.
"""
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = float(self.machine['cacheline size']) // element_size
iterations_per_cacheline = (sympy.Integer(self.machine['cacheline size']) /
sympy.Integer(self.kernel.bytes_per_iteration))
self.results['iterations per cacheline'] = iterations_per_cacheline
cacheline_size = float(self.machine['cacheline size'])
loads, stores = (self.predictor.get_loads(), self.predictor.get_stores())
for cache_level, cache_info in list(enumerate(self.machine['memory hierarchy']))[1:]:
throughput, duplexness = cache_info['non-overlap upstream throughput']
if type(throughput) is str and throughput == 'full socket memory bandwidth':
# Memory transfer
# we use bandwidth to calculate cycles and then add panalty cycles (if given)
# choose bw according to cache level and problem
# first, compile stream counts at current cache level
# write-allocate is allready resolved in cache predictor
read_streams = loads[cache_level]
write_streams = stores[cache_level]
# second, try to find best fitting kernel (closest to stream seen stream counts):
threads_per_core = 1
bw, measurement_kernel = self.machine.get_bandwidth(
cache_level, read_streams, write_streams, threads_per_core)
# calculate cycles
if duplexness == 'half-duplex':
cycles = float(loads[cache_level] + stores[cache_level]) * \
float(elements_per_cacheline) * float(element_size) * \
float(self.machine['clock']) / float(bw)
else: # full-duplex
raise NotImplementedError(
"full-duplex mode is not (yet) supported for memory transfers.")
# add penalty cycles for each read stream
if 'penalty cycles per read stream' in cache_info:
cycles += stores[cache_level] * \
cache_info['penalty cycles per read stream']
self.results.update({
'memory bandwidth kernel': measurement_kernel,
'memory bandwidth': bw})
else:
# since throughput is given in B/cy, and we need CL/cy:
throughput = float(throughput) / cacheline_size
# only cache cycles count
if duplexness == 'half-duplex':
cycles = (loads[cache_level] + stores[cache_level]) / float(throughput)
elif duplexness == 'full-duplex':
cycles = max(loads[cache_level] / float(throughput),
stores[cache_level] / float(throughput))
else:
raise ValueError("Duplexness of cache throughput may only be 'half-duplex'"
"or 'full-duplex', found {} in {}.".format(
duplexness, cache_info['name']))
self.results['cycles'].append((cache_info['level'], cycles))
self.results[cache_info['level']] = cycles
return self.results
def analyze(self):
"""Run complete anaylysis and return results."""
self.calculate_cache_access()
self.calculate_cycles()
self.results['flops per iteration'] = sum(self.kernel._flops.values())
return self.results
def conv_cy(self, cy_cl):
"""Convert cycles (cy/CL) to other units, such as FLOP/s or It/s."""
if not isinstance(cy_cl, PrefixedUnit):
cy_cl = PrefixedUnit(cy_cl, '', 'cy/CL')
clock = self.machine['clock']
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(self.machine['cacheline size']) // element_size
if cy_cl != 0:
it_s = clock/cy_cl*elements_per_cacheline
it_s.unit = 'It/s'
else:
it_s = PrefixedUnit('inf It/S')
flops_per_it = sum(self.kernel._flops.values())
performance = it_s*flops_per_it
performance.unit = 'FLOP/s'
cy_it = cy_cl*elements_per_cacheline
cy_it.unit = 'cy/It'
return {'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance}
def report_data_transfers(self):
cacheline_size = float(self.machine['cacheline size'])
r = "Data Transfers:\nLevel | Loads | Store |\n"
loads, stores = (self.predictor.get_loads(), self.predictor.get_stores())
for cache_level, cache_info in list(enumerate(self.machine['memory hierarchy']))[1:]:
r += ("{:>7} | {:>3.0f} B/CL | {:>3.0f} B/CL |\n".format(
self.machine['memory hierarchy'][cache_level-1]['level']+'-'+cache_info['level'],
loads[cache_level] * cacheline_size,
stores[cache_level] * cacheline_size))
return r
def report(self, output_file=sys.stdout):
"""Print generated model data in human readable format."""
if self.verbose > 1:
print('{}'.format(pprint.pformat(self.results['verbose infos'])), file=output_file)
for level, cycles in self.results['cycles']:
print('{} = {}'.format(
level, self.conv_cy(cycles)[self._args.unit]), file=output_file)
if self.verbose > 1:
if 'memory bandwidth kernel' in self.results:
print('memory cycles based on {} kernel with {}'.format(
self.results['memory bandwidth kernel'],
self.results['memory bandwidth']),
file=output_file)
if self.verbose > 1:
print(file=output_file)
print(self.report_data_transfers(), file=output_file)
class ECMData(PerformanceModel):
"""Representation of Data portion of the Execution-Cache-Memory Model."""
name = "Execution-Cache-Memory (data transfers only)"
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 = {}
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."""
self.results.update({
'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 calculate_cycles(self):
"""
Calculate performance model cycles from cache stats.
calculate_cache_access() needs to have been execute before.
"""
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = float(
self.machine['cacheline size']) // element_size
cacheline_size = float(self.machine['cacheline size'])
loads, stores = (self.predictor.get_loads(),
self.predictor.get_stores())
for cache_level, cache_info in list(
enumerate(self.machine['memory hierarchy']))[1:]:
throughput, duplexness = cache_info[
'non-overlap upstream throughput']
if type(throughput
) is str and throughput == 'full socket memory bandwidth':
# Memory transfer
# we use bandwidth to calculate cycles and then add panalty cycles (if given)
# choose bw according to cache level and problem
# first, compile stream counts at current cache level
# write-allocate is allready resolved in cache predictor
read_streams = loads[cache_level]
write_streams = stores[cache_level]
# second, try to find best fitting kernel (closest to stream seen stream counts):
threads_per_core = 1
bw, measurement_kernel = self.machine.get_bandwidth(
cache_level, read_streams, write_streams, threads_per_core)
# calculate cycles
if duplexness == 'half-duplex':
cycles = float(loads[cache_level] + stores[cache_level]) * \
float(elements_per_cacheline) * float(element_size) * \
float(self.machine['clock']) / float(bw)
else: # full-duplex
raise NotImplementedError(
"full-duplex mode is not (yet) supported for memory transfers."
)
# add penalty cycles for each read stream
if 'penalty cycles per read stream' in cache_info:
cycles += stores[cache_level] * \
cache_info['penalty cycles per read stream']
self.results.update({
'memory bandwidth kernel': measurement_kernel,
'memory bandwidth': bw
})
else:
# since throughput is given in B/cy, and we need CL/cy:
throughput = float(throughput) / cacheline_size
# only cache cycles count
if duplexness == 'half-duplex':
cycles = (loads[cache_level] +
stores[cache_level]) / float(throughput)
elif duplexness == 'full-duplex':
cycles = max(loads[cache_level] / float(throughput),
stores[cache_level] / float(throughput))
else:
raise ValueError(
"Duplexness of cache throughput may only be 'half-duplex'"
"or 'full-duplex', found {} in {}.".format(
duplexness, cache_info['name']))
self.results['cycles'].append((cache_info['level'], cycles))
self.results[cache_info['level']] = cycles
return self.results
def analyze(self):
"""Run complete anaylysis and return results."""
self.calculate_cache_access()
self.calculate_cycles()
self.results['flops per iteration'] = sum(self.kernel._flops.values())
return self.results
def conv_cy(self, cy_cl):
"""Convert cycles (cy/CL) to other units, such as FLOP/s or It/s."""
if not isinstance(cy_cl, PrefixedUnit):
cy_cl = PrefixedUnit(cy_cl, '', 'cy/CL')
clock = self.machine['clock']
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(
self.machine['cacheline size']) // element_size
if cy_cl != 0:
it_s = clock / cy_cl * elements_per_cacheline
it_s.unit = 'It/s'
else:
it_s = PrefixedUnit('inf It/S')
flops_per_it = sum(self.kernel._flops.values())
performance = it_s * flops_per_it
performance.unit = 'FLOP/s'
cy_it = cy_cl * elements_per_cacheline
cy_it.unit = 'cy/It'
return {
'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance
}
def report_data_transfers(self):
cacheline_size = float(self.machine['cacheline size'])
r = "Data Transfers:\nLevel | Loads | Store |\n"
loads, stores = (self.predictor.get_loads(),
self.predictor.get_stores())
for cache_level, cache_info in list(
enumerate(self.machine['memory hierarchy']))[1:]:
r += ("{:>7} | {:>3.0f} B/CL | {:>3.0f} B/CL |\n".format(
self.machine['memory hierarchy'][cache_level - 1]['level'] +
'-' + cache_info['level'], loads[cache_level] * cacheline_size,
stores[cache_level] * cacheline_size))
return r
def report(self, output_file=sys.stdout):
"""Print generated model data in human readable format."""
if self.verbose > 1:
print('{}'.format(pprint.pformat(self.results['verbose infos'])),
file=output_file)
for level, cycles in self.results['cycles']:
print('{} = {}'.format(level,
self.conv_cy(cycles)[self._args.unit]),
file=output_file)
if self.verbose > 1:
if 'memory bandwidth kernel' in self.results:
print('memory cycles based on {} kernel with {}'.format(
self.results['memory bandwidth kernel'],
self.results['memory bandwidth']),
file=output_file)
if self.verbose > 1:
print(file=output_file)
print(self.report_data_transfers(), file=output_file)
class Roofline(PerformanceModel):
"""
Representation of the Roofline model based on simplistic FLOP analysis.
more info to follow...
"""
name = "Roofline"
@classmethod
def configure_arggroup(cls, parser):
"""Configure argument parser."""
pass
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
def calculate_cache_access(self):
"""Apply cache prediction to generate cache access behaviour."""
self.results = {
'loads': self.predictor.get_loads(),
'stores': self.predictor.get_stores(),
'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 if sublist is not None
for item in sublist
])
write_offsets = set([
item for sublist in write_offsets if sublist is not None
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,
0, # we do not consider stores to L1
threads_per_core,
cores=self.cores)
# Calculate performance (arithmetic intensity * bandwidth with
# arithmetic intensity = Iterations / bytes loaded
if total_loads == 0:
# This happens in case of full-caching
arith_intens = None
it_s = None
else:
arith_intens = 1.0 / (total_loads / elements_per_cacheline)
it_s = PrefixedUnit(float(bw) * arith_intens, 'It/s')
self.results['mem bottlenecks'].append({
'performance':
self.conv_perf(it_s),
'level':
self.machine['memory hierarchy'][0]['level'],
'arithmetic intensity':
arith_intens,
'bw kernel':
measurement_kernel,
'bandwidth':
bw,
'bytes transfered':
total_loads
})
self.results['bottleneck level'] = len(
self.results['mem bottlenecks']) - 1
self.results['min performance'] = self.conv_perf(it_s)
# for other cache and memory levels:
for cache_level, cache_info in list(
enumerate(self.machine['memory hierarchy']))[:-1]:
# Compiling stats (in bytes!)
total_loads = self.results['loads'][cache_level +
1] * cacheline_size
total_stores = self.results['stores'][cache_level +
1] * 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['loads'][cache_level + 1]
write_streams = self.results['stores'][cache_level + 1]
# 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.cores)
# Calculate performance (arithmetic intensity * bandwidth with
# arithmetic intensity = flops / bytes transfered)
bytes_transfered = total_loads + total_stores
if bytes_transfered == 0:
# This happens in case of full-caching
arith_intens = float('inf')
it_s = PrefixedUnit(float('inf'), 'It/s')
else:
arith_intens = 1 / (bytes_transfered / elements_per_cacheline)
it_s = PrefixedUnit(float(bw) * arith_intens, 'It/s')
self.results['mem bottlenecks'].append({
'performance':
self.conv_perf(it_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 it_s < self.results.get('min performance',
{'It/s': it_s})['It/s']:
self.results['bottleneck level'] = len(
self.results['mem bottlenecks']) - 1
self.results['min performance'] = self.conv_perf(it_s)
return self.results
def analyze(self):
"""Run analysis."""
precision = 'DP' if self.kernel.datatype == 'double' else 'SP'
self.calculate_cache_access()
self.results['max_perf'] = self.conv_perf(self.machine['clock'] * self.cores * \
self.machine['FLOPs per cycle'][precision]['total'])
def conv_perf(self, it_s):
"""Convert performance (It/s) to other units, such as FLOP/s or cy/CL."""
clock = self.machine['clock']
flops_per_it = sum(self.kernel._flops.values())
performance = it_s * flops_per_it
performance.unit = 'FLOP/s'
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(float(
self.machine['cacheline size'])) / element_size
cy_cl = clock / it_s * elements_per_cacheline
cy_cl.unit = 'cy/CL'
cy_it = clock / it_s
cy_it.unit = 'cy/It'
return {
'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance
}
def report(self, output_file=sys.stdout):
"""Report analysis outcome in human readable form."""
max_perf = self.results['max_perf']
if self._args and self._args.verbose >= 3:
print('{}'.format(pformat(self.results)), file=output_file)
if self._args and self._args.verbose >= 1:
print('{}'.format(pformat(self.results['verbose infos'])),
file=output_file)
print('Bottlenecks:', file=output_file)
print(
' level | a. intensity | performance | peak bandwidth | peak bandwidth kernel',
file=output_file)
print(
'--------+--------------+-----------------+-------------------+----------------------',
file=output_file)
print(' CPU | | {!s:>15} | |'.
format(max_perf[self._args.unit]),
file=output_file)
for b in self.results['mem bottlenecks']:
print(
'{level:>7} | {arithmetic intensity:>7.2} It/B | {0!s:>15} |'
' {bandwidth!s:>17} | {bw kernel:<8}'.format(
b['performance'][self._args.unit], **b),
file=output_file)
print('', file=output_file)
if self.results['min performance']['FLOP/s'] > max_perf['FLOP/s']:
# CPU bound
print('CPU bound. {!s} due to CPU max. FLOP/s'.format(max_perf),
file=output_file)
else:
# Cache or mem bound
print('Cache or mem bound.', file=output_file)
bottleneck = self.results['mem bottlenecks'][
self.results['bottleneck level']]
print(
'{!s} due to {} transfer bottleneck (with bw from {} benchmark)'
.format(bottleneck['performance'][self._args.unit],
bottleneck['level'], bottleneck['bw kernel']),
file=output_file)
print('Arithmetic Intensity: {:.2f} It/B'.format(
bottleneck['arithmetic intensity']),
file=output_file)
if any([
'_Complex' in var_info[0]
for var_info in self.kernel.variables.values()
]):
print(
"WARNING: FLOP counts are probably wrong, because complex flops are counted\n"
" as single flops. All other units should not be affected.\n",
file=sys.stderr)
class Roofline(PerformanceModel):
"""
Representation of the Roofline model based on simplistic FLOP analysis.
more info to follow...
"""
name = "Roofline"
@classmethod
def configure_arggroup(cls, parser):
"""Configure argument parser."""
pass
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 calculate_cache_access(self):
"""Apply cache prediction to generate cache access behaviour."""
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 if sublist is not None
for item in sublist])
write_offsets = set([item for sublist in write_offsets if sublist is not None
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, # no write-allocate in L1
write_streams,
threads_per_core,
cores=self.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 = PrefixedUnit(arith_intens * float(bw), 'FLOP/s')
self.results['mem bottlenecks'].append({
'performance': self.conv_perf(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})
self.results['bottleneck level'] = len(self.results['mem bottlenecks'])-1
self.results['min performance'] = self.conv_perf(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.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 = PrefixedUnit(float('inf'), 'FLOP/s')
else:
arith_intens = float(total_flops)/bytes_transfered
performance = PrefixedUnit(arith_intens * float(bw), 'FLOP/s')
self.results['mem bottlenecks'].append({
'performance': self.conv_perf(performance),
'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', {'FLOP/s': performance})['FLOP/s']:
self.results['bottleneck level'] = len(self.results['mem bottlenecks'])-1
self.results['min performance'] = self.conv_perf(performance)
return self.results
def analyze(self):
"""Run analysis."""
precision = 'DP' if self.kernel.datatype == 'double' else 'SP'
self.calculate_cache_access()
self.results['max_perf'] = self.conv_perf(self.machine['clock'] * self.cores * \
self.machine['FLOPs per cycle'][precision]['total'])
def conv_perf(self, performance):
"""Convert performance (FLOP/s) to other units, such as It/s or cy/CL."""
clock = self.machine['clock']
flops_per_it = sum(self.kernel._flops.values())
it_s = performance/flops_per_it
it_s.unit = 'It/s'
element_size = self.kernel.datatypes_size[self.kernel.datatype]
elements_per_cacheline = int(float(self.machine['cacheline size'])) / element_size
cy_cl = clock/it_s*elements_per_cacheline
cy_cl.unit = 'cy/CL'
cy_it = clock/it_s
cy_it.unit = 'cy/It'
return {'It/s': it_s,
'cy/CL': cy_cl,
'cy/It': cy_it,
'FLOP/s': performance}
def report(self, output_file=sys.stdout):
"""Report analysis outcome in human readable form."""
max_perf = self.results['max_perf']
if self._args and self._args.verbose >= 3:
print('{}'.format(pformat(self.results)), file=output_file)
if self._args and self._args.verbose >= 1:
print('{}'.format(pformat(self.results['verbose infos'])), file=output_file)
print('Bottlenecks:', file=output_file)
print(' level | a. intensity | performance | peak bandwidth | peak bandwidth kernel',
file=output_file)
print('--------+--------------+-----------------+-------------------+----------------------',
file=output_file)
print(' CPU | | {!s:>15} | |'.format(
max_perf[self._args.unit]),
file=output_file)
for b in self.results['mem bottlenecks']:
print('{level:>7} | {arithmetic intensity:>5.2} FLOP/B | {0!s:>15} |'
' {bandwidth!s:>17} | {bw kernel:<8}'.format(
b['performance'][self._args.unit], **b),
file=output_file)
print('', file=output_file)
if self.results['min performance']['FLOP/s'] > max_perf['FLOP/s']:
# CPU bound
print('CPU bound. {!s} due to CPU max. FLOP/s'.format(max_perf), file=output_file)
else:
# Cache or mem bound
print('Cache or mem bound.', file=output_file)
bottleneck = self.results['mem bottlenecks'][self.results['bottleneck level']]
print('{!s} due to {} transfer bottleneck (with bw from {} benchmark)'.format(
bottleneck['performance'][self._args.unit],
bottleneck['level'],
bottleneck['bw kernel']),
file=output_file)
print('Arithmetic Intensity: {:.2f} FLOP/B'.format(bottleneck['arithmetic intensity']),
file=output_file)
def analyze(self):
"""Run complete analysis."""
lcp = LayerConditionPredictor(self.kernel, self.machine, symbolic=True)
self.results = lcp.results
