diff options
Diffstat (limited to 'bin/workload.py')
| -rwxr-xr-x | bin/workload.py | 161 |
1 files changed, 81 insertions, 80 deletions
diff --git a/bin/workload.py b/bin/workload.py index 19a7378..72b66bb 100755 --- a/bin/workload.py +++ b/bin/workload.py @@ -1,92 +1,93 @@ #!/usr/bin/env python3 +import argparse +import json +import logging import sys -from dfatool.automata import PTA -from dfatool.utils import human_readable -from dfatool.lex import TimedSequence, TimedWord, Workload +import dfatool.cli +import dfatool.utils +from dfatool.behaviour import EventSequenceModel +from dfatool.model import AnalyticModel -args = sys.argv[1:] - -loops = dict() -ptafiles = list() -loop_names = set() +def main(): + parser = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__ + ) + parser.add_argument("--aggregate", choices=["sum"], default="sum") + parser.add_argument("--aggregate-unit", choices=["s", "B/s"], default="s") + parser.add_argument( + "--aggregate-init", + default=0, + type=float, + ) + parser.add_argument( + "--log-level", + metavar="LEVEL", + choices=["debug", "info", "warning", "error"], + default="warning", + help="Set log level", + ) + parser.add_argument("--normalize-output", type=str) + parser.add_argument( + "--info", + action="store_true", + help="Show benchmark information (number of measurements, parameter values, ...)", + ) + parser.add_argument( + "--models", + nargs="+", + type=str, + help="Path to model file (.json or .json.xz)", + ) + parser.add_argument( + "--use-lut", + action="store_true", + help="Use LUT rather than performance model for prediction", + ) + parser.add_argument("event", nargs="+", type=str) + args = parser.parse_args() -def simulate_word(timedword): - prev_state = "UNINITIALIZED" - prev_param = None - ret = dict() - for trace_part in timedword: - print("Trace Part {}".format(trace_part)) - if type(trace_part) is TimedWord: - result = pta.simulate( - trace_part, orig_state=prev_state, orig_param=prev_param - ) - elif type(trace_part) is Workload: - result = pta.simulate( - trace_part.word, orig_state=prev_state, orig_param=prev_param - ) - if prev_state != result.end_state: - print( - "Warning: loop starts in state {}, but terminates in {}".format( - prev_state, result.end_state.name - ) - ) - if prev_param != result.parameters: - print( - "Warning: loop starts with parameters {}, but terminates with {}".format( - prev_param, result.parameters - ) - ) - ret[trace_part.name] = result - loop_names.add(trace_part.name) + if args.log_level: + numeric_level = getattr(logging, args.log_level.upper(), None) + if not isinstance(numeric_level, int): + print(f"Invalid log level: {args.log_level}", file=sys.stderr) + sys.exit(1) + logging.basicConfig( + level=numeric_level, + format="{asctime} {levelname}:{name}:{message}", + style="{", + ) - print(" Duration: " + human_readable(result.duration, "s")) - if result.duration_mae: - print( - u" ± {} / {:.0f}%".format( - human_readable(result.duration_mae, "s"), result.duration_mape - ) - ) - print(" Energy: " + human_readable(result.energy, "J")) - if result.energy_mae: - print( - u" ± {} / {:.0f}%".format( - human_readable(result.energy_mae, "J"), result.energy_mape - ) - ) - print(" Mean Power: " + human_readable(result.mean_power, "W")) - print("") + models = list() + for model_file in args.models: + with open(model_file, "r") as f: + models.append(AnalyticModel.from_json(json.load(f))) - prev_state = result.end_state - prev_param = result.parameters + if args.info: + for i in range(len(models)): + print(f"""{args.models[i]}: {" ".join(models[i].parameters)}""") + _, param_info = models[i].get_fitted() + for name in models[i].names: + for attr in models[i].attributes(name): + print(f" {name}.{attr} {param_info(name, attr)}") - return ret + workload = EventSequenceModel(models) + aggregate = workload.eval_strs( + args.event, + aggregate=args.aggregate, + aggregate_init=args.aggregate_init, + use_lut=args.use_lut, + ) + if args.normalize_output: + sf = dfatool.cli.parse_shift_function( + "--normalize-output", args.normalize_output + ) + print(dfatool.utils.human_readable(sf(aggregate), args.aggregate_unit)) + else: + print(dfatool.utils.human_readable(aggregate, args.aggregate_unit)) -for i in range(len(args) // 2): - ptafile, raw_word = args[i * 2], args[i * 2 + 1] - ptafiles.append(ptafile) - pta = PTA.from_file(ptafile) - timedword = TimedSequence(raw_word) - print("Input: {}\n".format(timedword)) - loops[ptafile] = simulate_word(timedword) -for loop_name in sorted(loop_names): - result_set = list() - total_power = 0 - for ptafile in sorted(ptafiles): - if loop_name in loops[ptafile]: - result_set.append(loops[ptafile][loop_name]) - total_power += loops[ptafile][loop_name].mean_power - print( - "{}: total mean power is {}".format(loop_name, human_readable(total_power, "W")) - ) - for i, result in enumerate(result_set): - print( - " {:.0f}% {} (period: {})".format( - result.mean_power * 100 / total_power, - ptafiles[i], - human_readable(result.duration, "s"), - ) - ) +if __name__ == "__main__": + main() |