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"""
Utilities for parameter extraction from data layout.
Parameters include the amount of keys, length of strings (both keys and values),
length of lists, ane more.
"""
from protocol_benchmarks import codegen_for_lib
import numpy as np
import ubjson
def _string_value_length(json):
if type(json) == str:
return len(json)
if type(json) == dict:
return sum(map(_string_value_length, json.values()))
if type(json) == list:
return sum(map(_string_value_length, json))
return 0
# TODO distinguish between int and uint, which is not visible from the
# data value alone
def _int_value_length(json):
if type(json) == int:
if json < 256:
return 1
if json < 65536:
return 2
return 4
if type(json) == dict:
return sum(map(_int_value_length, json.values()))
if type(json) == list:
return sum(map(_int_value_length, json))
return 0
def _string_key_length(json):
if type(json) == dict:
return sum(map(len, json.keys())) + sum(map(_string_key_length, json.values()))
return 0
def _num_keys(json):
if type(json) == dict:
return len(json.keys()) + sum(map(_num_keys, json.values()))
return 0
def _num_of_type(json, wanted_type):
ret = 0
if type(json) == wanted_type:
ret = 1
if type(json) == dict:
ret += sum(map(lambda x: _num_of_type(x, wanted_type), json.values()))
if type(json) == list:
ret += sum(map(lambda x: _num_of_type(x, wanted_type), json))
return ret
def json_to_param(json):
"""Return numeric parameters describing the structure of JSON data."""
ret = dict()
ret['strlen_keys'] = _string_key_length(json)
ret['strlen_values'] = _string_value_length(json)
ret['bytelen_int'] = _int_value_length(json)
ret['num_int'] = _num_of_type(json, int)
ret['num_float'] = _num_of_type(json, float)
ret['num_str'] = _num_of_type(json, str)
return ret
class Protolog:
"""
Loader and postprocessor for raw protobench (protocol-modeling/benchmark.py) data.
Converts data sorted by (arch,lib)/benchmark/index/attribute
to data sorted by (benchmark,index)/arch/lib/attribute.
Once constructed, a class object provides three members:
libraries -- array of library:config elements found in the benchmark results
architectures -- array of multipass architecture names
aggregate -- enriched log data, ordered by benchmark: {
('benchmark name', 'sub-benchmark index') : {
'architecture' : {
'library:options' : {
'attribute' : value (usually int or array)
}
}
}
}
aggregate attributes:
bss_{nop,ser,serdes} : whole-program Block Storage Segment (BSS) size
callcycles_raw : { 'C++ statement' : [CPU cycles for execution] ... }.
Not adjusted for 'nop' cycles -> values are a few cycles higher than true duration
cycles_{ser,des,enc,dec,encser,desdec} : cycles for complete (de)serialization step,
measured using just one counter start/stop (not a sum of callcycles_raw entries).
Adjusted for 'nop' cycles -> should give accurate function call duration
data_{secnop,ser,serdes} : whole-program Data Segment size
heap_{ser,des} : Maximum heap usage during step
serialized_size : Size (Bytes) of serialized data
stack_alloc_{ser,des} : Maximum stack usage (Bytes) during step.
Based on online analysis (comparison of memory dumps)
stack_set_{ser,des} : Number of stack bytes modified during step.
Based on online analysis (comparison of memory dumps), should be
smaller than the corresponding stack_alloc_ value
text_{nop,ser,serdes} : whole-program Text Segment (code/Flash) size
"""
def _median_cycles(data, key):
# There should always be more than just one measurement -- otherwise
# something went wrong
if len(data[key]) <= 1:
return np.nan
for val in data[key]:
# bogus data
if val > 10_000_000:
return np.nan
for val in data['nop']:
# bogus data
if val > 10_000_000:
return np.nan
# All measurements in data[key] cover the same instructions, so they
# should be identical.
return max(0, int(np.median(data[key][1:]) - np.median(data['nop'][1:])))
def _median_callcycles(data):
ret = dict()
for line in data.keys():
ret[line] = np.median(data[line])
return ret
idem = lambda x: x
datamap = [
['bss_nop', 'bss_size_nop', idem],
['bss_ser', 'bss_size_ser', idem],
['bss_serdes', 'bss_size_serdes', idem],
['callcycles_raw', 'callcycles', idem],
['callcycles_median', 'callcycles', _median_callcycles],
['cycles_ser', 'cycles', lambda x: Protolog._median_cycles(x, 'ser')],
['cycles_des', 'cycles', lambda x: Protolog._median_cycles(x, 'des')],
['cycles_enc', 'cycles', lambda x: Protolog._median_cycles(x, 'enc')],
['cycles_dec', 'cycles', lambda x: Protolog._median_cycles(x, 'dec')],
#['cycles_ser_arr', 'cycles', lambda x: np.array(x['ser'][1:]) - np.mean(x['nop'][1:])],
#['cycles_des_arr', 'cycles', lambda x: np.array(x['des'][1:]) - np.mean(x['nop'][1:])],
#['cycles_enc_arr', 'cycles', lambda x: np.array(x['enc'][1:]) - np.mean(x['nop'][1:])],
#['cycles_dec_arr', 'cycles', lambda x: np.array(x['dec'][1:]) - np.mean(x['nop'][1:])],
['data_nop', 'data_size_nop', idem],
['data_ser', 'data_size_ser', idem],
['data_serdes', 'data_size_serdes', idem],
['heap_ser', 'heap_usage_ser', idem],
['heap_des', 'heap_usage_des', idem],
['serialized_size', 'serialized_size', idem],
['stack_alloc_ser', 'stack_online_ser', lambda x: x['allocated']],
['stack_set_ser', 'stack_online_ser', lambda x: x['used']],
['stack_alloc_des', 'stack_online_des', lambda x: x['allocated']],
['stack_set_des', 'stack_online_des', lambda x: x['used']],
['text_nop', 'text_size_nop', idem],
['text_ser', 'text_size_ser', idem],
['text_serdes', 'text_size_serdes', idem],
]
def __init__(self, logfile):
"""
Load and enrich raw protobench log data.
The enriched data can be accessed via the .aggregate class member,
see the class documentation for details.
"""
with open(logfile, 'rb') as f:
self.data = ubjson.load(f)
self.libraries = set()
self.architectures = set()
self.aggregate = dict()
for arch_lib in self.data.keys():
arch, lib, libopts = arch_lib.split(':')
library = lib + ':' + libopts
for benchmark in self.data[arch_lib].keys():
for benchmark_item in self.data[arch_lib][benchmark].keys():
subv = self.data[arch_lib][benchmark][benchmark_item]
for aggregate_label, data_label, getter in Protolog.datamap:
try:
self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, aggregate_label, data_label, getter)
except KeyError:
pass
except TypeError as e:
print('TypeError in {} {} {} {}: {} -> {}'.format(
arch_lib, benchmark, benchmark_item, aggregate_label,
subv[data_label]['v'], str(e)))
pass
codegen = codegen_for_lib(lib, libopts.split(','), subv['data'])
if codegen.max_serialized_bytes != None:
self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: codegen.max_serialized_bytes)
else:
self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: 0)
#self.aggregate[(benchmark, benchmark_item)][arch][lib][aggregate_label] = getter(value[data_label]['v'])
for key in self.aggregate.keys():
for arch in self.aggregate[key].keys():
for lib, val in self.aggregate[key][arch].items():
try:
val['cycles_encser'] = val['cycles_enc'] + val['cycles_ser']
except KeyError:
pass
try:
val['cycles_desdec'] = val['cycles_des'] + val['cycles_dec']
except KeyError:
pass
try:
val['total_dmem_ser'] = val['stack_alloc_ser']
val['total_dmem_ser'] += val['heap_ser']
except KeyError:
pass
try:
val['total_dmem_des'] = val['stack_alloc_des']
val['total_dmem_des'] += val['heap_des']
except KeyError:
pass
try:
val['total_dmem_serdes'] = max(val['total_dmem_ser'], val['total_dmem_des'])
except KeyError:
pass
try:
val['text_ser_delta'] = val['text_ser'] - val['text_nop']
val['text_serdes_delta'] = val['text_serdes'] - val['text_nop']
except KeyError:
pass
try:
val['bss_ser_delta'] = val['bss_ser'] - val['bss_nop']
val['bss_serdes_delta'] = val['bss_serdes'] - val['bss_nop']
except KeyError:
pass
try:
val['data_ser_delta'] = val['data_ser'] - val['data_nop']
val['data_serdes_delta'] = val['data_serdes'] - val['data_nop']
except KeyError:
pass
try:
val['allmem_ser'] = val['text_ser'] + val['data_ser'] + val['bss_ser'] + val['total_dmem_ser'] - val['buffer_size']
val['allmem_serdes'] = val['text_serdes'] + val['data_serdes'] + val['bss_serdes'] + val['total_dmem_serdes'] - val['buffer_size']
except KeyError:
pass
def add_datapoint(self, arch, lib, key, value, aggregate_label, data_label, getter):
"""
Set self.aggregate[key][arch][lib][aggregate_Label] = getter(value[data_label]['v']).
Additionally, add lib to self.libraries and arch to self.architectures
key usually is ('benchmark name', 'sub-benchmark index').
"""
if data_label in value and 'v' in value[data_label]:
self.architectures.add(arch)
self.libraries.add(lib)
if not key in self.aggregate:
self.aggregate[key] = dict()
if not arch in self.aggregate[key]:
self.aggregate[key][arch] = dict()
if not lib in self.aggregate[key][arch]:
self.aggregate[key][arch][lib] = dict()
self.aggregate[key][arch][lib][aggregate_label] = getter(value[data_label]['v'])
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