diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/dfatool | 116 | ||||
| -rwxr-xr-x | bin/merge.py | 98 |
2 files changed, 186 insertions, 28 deletions
diff --git a/bin/dfatool b/bin/dfatool index 00b4196..881d04b 100755 --- a/bin/dfatool +++ b/bin/dfatool @@ -331,12 +331,47 @@ __END__ =head1 NAME +dfatool manages energy-aware DFA drivers for kratos and generates and runs +test programs to assess a device's energy usage. + =head1 SYNOPSIS +B<dfatool> [I<options>] enable|disable|maketest|rmtest|log|loop I<driver.xml> + +B<dfatool> [I<options>] analyze|validate|crossvalidate|ls|list|show +I<driver.xml> I<data.tar> [I<moredata.tar ...>] + =head1 VERSION =head1 DESCRIPTION +TODO WARNING: rel_energy_next is unusable for transitions which frequently are +the last transition in a run + +=head1 COMMANDS + +=over + +=item B<enable> I<driver.xml> + +Instruments the driver noted in I<driver.xml> for energy accounting and state +and transition logging. Unconditionally uses static model attributes and only +relative energy values. Attributes whose power or energy values are not yet +known are set to zero. + +=item B<disable> I<driver.xml> + +Removes accounting and logging instrumentation, thus turning the driver back +into an energy-unaware one. By default, each state may be visited up to two +times... + +=item B<maketest> I<driver.xml> + +Creates a kratos application containing a test program for the driver. By +default, + +=back + =head1 OPTIONS =over @@ -349,18 +384,93 @@ __END__ None. -=head1 DEPENDENCIES +=head1 SOFTWARE DEPENDENCIES + +As of 2017-04, the following packages (and some more) are required on Debian +Sid: + + apt install libcarp-assert-more-perl libclass-accessor-perl libdatetime-perl + libdevice-serialport-perl libfile-slurp-perl libipc-run-perl libjson-perl + liblist-moreutils-perl libmath-cartesian-product-perl + libparse-recdescent-perl libtime-progress-perl libxml-libxml-perl + python3-matplotlib python3-numpy python3-scipy + +Required non-standard perl modules: =over +=item * Carp::Assert::More + +=item * DateTIme + +=item * Device::SerialPort + +=item * File::Slurp + +=item * IPC::Run + +=item * List::MoreUtils + +=item * Math::Cartesian::Product + +=item * Parse::RecDescent + +=item * Time::Progress + +=item * XML::LibXML + +=back + +For analysis, the following programs and modules are required: + +=over + +=item * Python 3.5 with matplotlib, numpy and scipy modules + +=item * parallel (either GNU parallel or the one provided by moreutils) + +=back + +=head1 HARDWARE DEPENDENCIES + +Normal driver usage and analyses of generated data requires no external +hardware. For data collection (C<< dfatool loop >> and C<< dfatool log >>), the +following pieces are required: + +=over + +=item * An MSP430 Launchpad with the Device Under Test attached + +=item * MIMOSA + +=item * MIMOSA-Autocal + =back =head1 BUGS AND LIMITATIONS +To be determined. + =head1 AUTHOR -Copyright (C) 2016 by Daniel Friesel E<lt>derf@finalrewind.orgE<gt> +Copyright (C) 2016-2017 by Daniel Friesel E<lt>daniel.friesel@udo.eduE<gt> =head1 LICENSE - 0. You just DO WHAT THE FUCK YOU WANT TO. +You may use this program either according to the terms of the Perl license +(also known as the Artistic license) or under the following terms +(also knows as the Three-clause BSD License). + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this +list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors +may be used to endorse or promote products derived from this software without +specific prior written permission. diff --git a/bin/merge.py b/bin/merge.py index 67c9cc9..cc287e8 100755 --- a/bin/merge.py +++ b/bin/merge.py @@ -285,7 +285,21 @@ def param_values(parameters, by_param): return paramvalues -def param_key(elem): +# Returns the values used for each function argument in the measurement, e.g. +# { 'data': [], 'length' : [16, 31, 32] } +# non-numeric values such as '' or 'long_test_string' are skipped +def arg_values(name, by_arg): + TODO + argvalues = dict([[arg, set()] for arg in parameters]) + + for _, paramvalue in by_param.keys(): + for i, param in enumerate(parameters): + if is_numeric(paramvalue[i]): + paramvalues[param].add(paramvalue[i]) + + return paramvalues + +def mk_param_key(elem): name = elem['name'] paramtuple = () @@ -295,12 +309,14 @@ def param_key(elem): return (name, paramtuple) -#def param_arg_key(elem): -# # Argumentbasierte Parametrisierung ist erstmal out of scope -# #if 'args' in elem: -# # argtuple = tuple(elem['args']) -# -# return (name, paramtuple, argtuple) +def mk_arg_key(elem): + name = elem['name'] + argtuple = () + + if 'args' in elem: + argtuple = tuple(elem['args']) + + return (name, argtuple) def add_data_to_aggregate(aggregate, key, isa, data): if not key in aggregate: @@ -363,6 +379,15 @@ def mean_std_by_param(data, keys, name, what, index): return np.mean([np.std(partition) for partition in partitions]) # returns the mean standard deviation of all measurements of 'what' +# (e.g. energy or duration) for transition 'name' where +# the 'index'th argumetn is dynamic and all other arguments are fixed. +# I.e., if arguments are a, b, c ∈ {1,2,3} and 'index' is 1, then +# this function returns the mean of the standard deviations of (a=1, b=*, c=1), +# (a=1, b=*, c=2), and so on +def mean_std_by_arg(data, keys, name, what, index): + return mean_std_by_param(data, keys, name, what, index) + +# returns the mean standard deviation of all measurements of 'what' # (e.g. power consumption or timeout) for state/transition 'name' where the # trace of previous transitions is fixed except for a single transition, # whose occurence or absence is silently ignored. @@ -385,7 +410,7 @@ def mean_std_by_trace_part(data, transitions, name, what): return ret -def load_run_elem(index, element, trace, by_name, by_param, by_trace): +def load_run_elem(index, element, trace, by_name, by_arg, by_param, by_trace): means, stds, durations, energies, rel_energies_prev, rel_energies_next, clips, timeouts, sub_thresholds = mimosa_data(element) online_means = [] @@ -393,9 +418,10 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace): if element['isa'] == 'state': online_means, online_durations = online_data(element) - key = param_key(element) + arg_key = mk_arg_key(element) + param_key = mk_param_key(element) pre_trace = tuple(map(lambda x : x['name'], trace[1:index:2])) - trace_key = (*key, pre_trace) + trace_key = (*param_key, pre_trace) name = element['name'] elem_data = { @@ -408,12 +434,13 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace): 'clip_rate' : clips, 'timeouts' : timeouts, 'sub_thresholds' : sub_thresholds, - 'param' : [key[1]] * len(means), + 'param' : [param_key[1]] * len(means), 'online_means' : online_means, 'online_durations' : online_durations, } add_data_to_aggregate(by_name, name, element['isa'], elem_data) - add_data_to_aggregate(by_param, key, element['isa'], elem_data) + add_data_to_aggregate(by_arg, arg_key, element['isa'], elem_data) + add_data_to_aggregate(by_param, param_key, element['isa'], elem_data) add_data_to_aggregate(by_trace, trace_key, element['isa'], elem_data) def fmap(name, funtype): @@ -487,7 +514,10 @@ def param_measures(name, paramdata, key, fun): return ret -def keydata(name, val, paramdata, tracedata, key): +def arg_measures(name, argdata, key, fun): + return param_measures(name, argdata, key, fun) + +def keydata(name, val, argdata, paramdata, tracedata, key): ret = { 'count' : len(val[key]), 'median' : np.median(val[key]), @@ -504,6 +534,12 @@ def keydata(name, val, paramdata, tracedata, key): 'function' : {}, } + if val['isa'] == 'transition': + ret['arg_mean_goodness'] = arg_measures(name, argdata, key, np.mean) + ret['arg_median_goodness'] = arg_measures(name, argdata, key, np.median) + ret['std_arg'] = np.mean([np.std(argdata[x][key]) for x in argdata.keys() if x[0] == name]) + ret['std_by_arg'] = {} + return ret def splitidx_kfold(length, num_slices): @@ -569,7 +605,7 @@ def val_run_fun(aggdata, by_trace, name, key, funtype1, funtype2, splitfun, coun fake_add_data_to_aggregate(bpa_validation, bpa_key, isa, aggdata, idx) fake_by_name = { name : aggdata } - ares = analyze(fake_by_name, bpa_training, by_trace, parameters) + ares = analyze(fake_by_name, {}, bpa_training, by_trace, parameters) if name in ares[isa] and funtype2 in ares[isa][name][funtype1]['function']: xv2_assess_function(name, ares[isa][name][funtype1]['function'][funtype2], key, bpa_validation, mae, smape, rmsd) if funtype2 == 'estimate': @@ -597,7 +633,7 @@ def val_run_fun_p(aggdata, by_trace, name, key, funtype1, funtype2, splitfun, co for idx in range(0, len(val[key])): fake_add_data_to_aggregate(bna_training, name, isa, val, idx) - ares = analyze(bna_training, bpa_training, by_trace, parameters) + ares = analyze(bna_training, {}, bpa_training, by_trace, parameters) if name in ares[isa] and funtype2 in ares[isa][name][funtype1]['function']: xv2_assess_function(name, ares[isa][name][funtype1]['function'][funtype2], key, bpa_validation, mae, smape, rmsd) if funtype2 == 'estimate': @@ -718,7 +754,7 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters): to_pop.append(name) for name in to_pop: by_name.pop(name, None) - ares = analyze(by_name, bpa_training, by_trace, parameters) + ares = analyze(by_name, {}, bpa_training, by_trace, parameters) for name in sorted(ares['state'].keys()): state = ares['state'][name] if 'function' in state['power']: @@ -858,6 +894,12 @@ def analyze_by_param(aggval, by_param, allvalues, name, key1, key2, param, param if aggval[key1]['std_by_param'][param] > 0 and aggval[key1]['std_param'] / aggval[key1]['std_by_param'][param] < 0.6: aggval[key1]['fit_guess'][param] = try_fits(name, key2, param_idx, by_param) +def analyze_by_arg(aggval, by_arg, allvalues, name, key1, key2, arg_name, arg_idx): + aggval[key1]['std_by_arg'][arg_name] = mean_std_by_arg( + by_arg, allvalues, name, key2, arg_idx) + if aggval[key1]['std_by_arg'][arg_name] > 0 and aggval[key1]['std_arg'] / aggval[key1]['std_by_arg'][arg_name] < 0.6: + pass # TODO + def maybe_fit_function(aggval, model, by_param, parameters, name, key1, key2, unit): if 'function' in model[key1] and 'user' in model[key1]['function']: aggval[key1]['function']['user'] = { @@ -868,7 +910,7 @@ def maybe_fit_function(aggval, model, by_param, parameters, name, key1, key2, un aggval[key1]['function']['user'], name, key2, parameters, by_param, yaxis='%s %s [%s]' % (name, key1, unit)) -def analyze(by_name, by_param, by_trace, parameters): +def analyze(by_name, by_arg, by_param, by_trace, parameters): aggdata = { 'state' : {}, 'transition' : {}, @@ -879,9 +921,9 @@ def analyze(by_name, by_param, by_trace, parameters): model = data['model'][isa][name] aggdata[isa][name] = { - 'power' : keydata(name, val, by_param, by_trace, 'means'), - 'duration' : keydata(name, val, by_param, by_trace, 'durations'), - 'energy' : keydata(name, val, by_param, by_trace, 'energies'), + 'power' : keydata(name, val, by_arg, by_param, by_trace, 'means'), + 'duration' : keydata(name, val, by_arg, by_param, by_trace, 'durations'), + 'energy' : keydata(name, val, by_arg, by_param, by_trace, 'energies'), 'clip' : { 'mean' : np.mean(val['clip_rate']), 'max' : max(val['clip_rate']), @@ -893,11 +935,11 @@ def analyze(by_name, by_param, by_trace, parameters): aggval['power']['std_outer'] = np.mean(val['stds']) if isa == 'transition': - aggval['rel_energy_prev'] = keydata(name, val, by_param, by_trace, 'rel_energies_prev') - aggval['rel_energy_next'] = keydata(name, val, by_param, by_trace, 'rel_energies_next') + aggval['rel_energy_prev'] = keydata(name, val, by_arg, by_param, by_trace, 'rel_energies_prev') + aggval['rel_energy_next'] = keydata(name, val, by_arg, by_param, by_trace, 'rel_energies_next') if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']: - aggval['timeout'] = keydata(name, val, by_param, by_trace, 'timeouts') + aggval['timeout'] = keydata(name, val, by_arg, by_param, by_trace, 'timeouts') for i, param in enumerate(sorted(data['model']['parameter'].keys())): values = list(set([key[1][i] for key in by_param.keys() if key[0] == name and key[1][i] != ''])) @@ -913,6 +955,11 @@ def analyze(by_name, by_param, by_trace, parameters): analyze_by_param(aggval, by_param, allvalues, name, 'rel_energy_next', 'rel_energies_next', param, i) if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']: analyze_by_param(aggval, by_param, allvalues, name, 'timeout', 'timeouts', param, i) + if 'parameters' in model: + for i, arg in enumerate(model['parameters']): + values = list(set([key[1][i] for key in by_arg.keys() if key[0] == name and is_numeric(key[1][i])])) + allvalues = [(*key[1][:i], *key[1][i+1:]) for key in by_arg.keys() if key[0] == name] + analyze_by_arg(aggval, by_arg, allvalues, name, 'power', 'means', arg['name'], i) if isa == 'state': fguess_to_function(name, 'means', aggval['power'], parameters, by_param, @@ -958,6 +1005,7 @@ except getopt.GetoptError as err: data = load_json(args[0]) by_name = {} +by_arg = {} by_param = {} by_trace = {} parameters = sorted(data['model']['parameter'].keys()) @@ -968,7 +1016,7 @@ for arg in args: if 'ignore-trace-idx' not in opts or opts['ignore-trace-idx'] != runidx: for i, elem in enumerate(run['trace']): if elem['name'] != 'UNINITIALIZED': - load_run_elem(i, elem, run['trace'], by_name, by_param, by_trace) + load_run_elem(i, elem, run['trace'], by_name, by_arg, by_param, by_trace) if 'states' in opts: if 'params' in opts: @@ -1001,7 +1049,7 @@ if 'validate' in opts: elif 'crossvalidate' in opts: crossvalidate(by_name, by_param, by_trace, data['model'], parameters) else: - data['aggregate'] = analyze(by_name, by_param, by_trace, parameters) + data['aggregate'] = analyze(by_name, by_arg, by_param, by_trace, parameters) # TODO optionally also plot data points for states/transitions which do not have # a function, but may depend on a parameter (visualization is always good!) |