diff --git a/src/tsnkit/benchmark/__init__.py b/src/tsnkit/benchmark/__init__.py new file mode 100644 index 00000000..b6f3d914 --- /dev/null +++ b/src/tsnkit/benchmark/__init__.py @@ -0,0 +1,9 @@ +""" +Author: Elaine Hu +__init__.py (c) 2024 +Desc: description +Created: 2024-08-10 +""" + +from .draw import draw +from ._processes import killif, run, validate_schedule, mute, print_output, str_flag diff --git a/src/tsnkit/benchmark/__main__.py b/src/tsnkit/benchmark/__main__.py new file mode 100644 index 00000000..c18a62d5 --- /dev/null +++ b/src/tsnkit/benchmark/__main__.py @@ -0,0 +1,246 @@ +import argparse +import gc +import os +import signal +import time +from functools import partial + +import pandas as pd +import numpy as np + +from ..utils import Result +from . import draw, killif, run, validate_schedule, mute, print_output, str_flag +from .. import utils +from multiprocessing import Pool, cpu_count, Value, Process, Queue + +from ..models import (at, cg, cp_wa, dt, i_ilp, i_omt, jrs_mc, jrs_nw, jrs_nw_l, jrs_wa, + ls, ls_pl, ls_tb, smt_fr, smt_nw, smt_pr, smt_wa) + +SCRIPT_DIR = os.path.dirname(__file__) +DATASET_LOGS = pd.read_csv(SCRIPT_DIR + "/data/dataset_logs.csv") + +ALGO_DICT = { + "at": at, + "cg": cg, + "cp_wa": cp_wa, + "dt": dt, + "i_ilp": i_ilp, + "i_omt": i_omt, + "jrs_mc": jrs_mc, + "jrs_nw": jrs_nw, + "jrs_nw_l": jrs_nw_l, + "jrs_wa": jrs_wa, + "ls": ls, + "ls_pl": ls_pl, + "ls_tb": ls_tb, + "smt_fr": smt_fr, + "smt_nw": smt_nw, + "smt_pr": smt_pr, + "smt_wa": smt_wa +} + +MULTIPROC = ["ls_pl"] + + +def parse(): + parser = argparse.ArgumentParser() + + parser.add_argument("algorithms", type=str, nargs="+", help="list of algorithms to be tested") + parser.add_argument("--ins", type=str, nargs="+", help="list of problem instances") + parser.add_argument("-t", type=int, default=600, help="total timeout limit") + parser.add_argument("-o", type=str, default="./", help="output path") + + return parser.parse_args() + + +def import_algorithm(algo_name): + try: + algo = ALGO_DICT[algo_name] + return algo + except KeyError as e: + print(f"no model named {algo_name}") + return None + + +def remove_configs(config_num): + os.remove(f"./{config_num}-DELAY.csv") + os.remove(f"./{config_num}-GCL.csv") + os.remove(f"./{config_num}-OFFSET.csv") + os.remove(f"./{config_num}-QUEUE.csv") + os.remove(f"./{config_num}-ROUTE.csv") + + +def process_num(name: str): + if name in ["dt", "ls", "ls_tb"]: + return 1 + return 4 + + +def print_result(task_num: int, result: str): + print_format = "| {:<13} | {:<6} | {:}" + + print(print_format.format(time.strftime("%d~%H:%M:%S"), task_num, result), flush=True) + + +if __name__ == "__main__": + args = parse() + algorithms = args.algorithms + ins = args.ins + utils.T_LIMIT = args.t + output_affix = args.o + + data_path = f"{SCRIPT_DIR}/data/" + + results = pd.DataFrame( + columns=["name", "data_id", "flag", "solve_time", "total_time", "total_mem", "log"], + index=np.arange(4352)) + + total_ins = 0 + algo_header = "| {:<13} | {:<13} | {:<6} | {:<10} | {:<10} | {:<10}" + sim_header = "| {:<13} | {:<6} | {:<12}" + + for i, name in enumerate(algorithms): + + alg = import_algorithm(name) + if alg is None: + continue + + print(f"------------------------------------{name}------------------------------------") + print(algo_header.format("time", "task id", "flag", "solve_time", "total_time", "total_mem", ), flush=True) + + successful = [] + a, b = ins[i].split("-") + tasks = int(b) - int(a) + 1 + + signal = Value("i", 0) + oom_queue = Queue() + + oom = Process( + target=killif, + args=( + os.getpid(), + process_num(name), + utils.T_LIMIT, + signal, + oom_queue, + ), + ) + + oom.start() + + def store(output, verbose=True): + # output = [task_id, result, algo_time, total_time, algo_mem, total_mem] + flag = output[1] + task_num = output[0] + result = [name, task_num, "successful", output[2], output[3], output[4], []] + if flag == Result.schedulable.value: + successful.append(int(task_num)) + elif flag == Result.unknown.value: + result[2] = "unknown" + else: + result[2] = "infeasible" + results.iloc[total_ins + int(task_num) - 1, :] = result + signal.value += 1 + if verbose: + print_output(task_num, str_flag(flag), output[2], output[3], output[4]) + + if name in MULTIPROC: + for file_num in [str(j) for j in range(int(a), int(b) + 1)]: + store(run(alg.benchmark, file_num, process_num(name)), verbose=False) + else: + with Pool(processes=cpu_count() // process_num(name), maxtasksperchild=1, initializer=mute) as p: + for file_num in [str(j) for j in range(int(a), int(b) + 1)]: + p.apply_async( + run, + args=( + alg.benchmark, + file_num, + process_num(name), + ), + callback=store, + ) + p.close() + try: + while signal.value < tasks: + time.sleep(1) + except KeyboardInterrupt: + print(f"Terminate calculation by hand.") + tasks = signal.value + + oom.terminate() + gc.collect() + + # add the processes that timed out to the results dataframe + for index, row in results.iloc[total_ins: total_ins+tasks, :].iterrows(): + if not row.isnull().any() or oom_queue.empty(): + continue + process = oom_queue.get() # [proc_time, proc_mem] + mem = process[1] / (1024 ** 2) + # ["name", "data_id", "flag", "solve_time", "total_time", "total_mem", "log"] + results.iloc[index, :] = [name, index+1-total_ins, "unknown", process[0], process[0], round(mem, 3), []] + + if not successful: + results.iloc[:(total_ins + tasks), :].to_csv(f"{output_affix}results.csv") + total_ins += tasks + gc.collect() + continue + + # schedule validation + print(sim_header.format("time", "task id", "flag"), flush=True) + signal.value = 0 + + def validate(log, task_num: int): + deadline = pd.read_csv(f"{data_path}{task_num}_task.csv")["deadline"] + + flag = "successful" + sim_result = "successful" + for flow_id, flow_log in enumerate(log): + delay = np.mean([flow_log[1][s] - flow_log[0][s] for s in range(len(flow_log[1]))]) + + # validate schedule + if np.isnan(delay) or delay > deadline[flow_id]: + flag = "infeasible" + sim_result = "fail" + break + + # ["name", "data_id", "flag", "solve_time", "total_time", "total_mem", "log"] + results.iloc[total_ins + task_num - 1, 2] = flag + results.iloc[total_ins + task_num - 1, 6] = log + signal.value += 1 + print_result(task_num, sim_result) + remove_configs(task_num) + + def error(err, task_num: int): + results.iloc[(total_ins + task_num - 1), 2] = "infeasible" + print_result(task_num, f"error: {err}") + signal.value += 1 + + with Pool(processes=cpu_count(), maxtasksperchild=1, initializer=mute) as p: + for task in successful: + validate_param = partial(validate, task_num=task) + error_param = partial(error, task_num=task) + task_path = f"{data_path}{task}_task.csv" + p.apply_async( + validate_schedule, + args=( + task_path, # task path + task, # config path + ), + callback=validate_param, + error_callback=error_param + ) + p.close() + try: + while signal.value < len(successful): + time.sleep(1) + except KeyboardInterrupt: + print("Terminate calculation by hand.") + + results.iloc[:(total_ins + tasks), :].to_csv(f"{output_affix}results.csv") + total_ins += tasks + + gc.collect() + + results = results.iloc[0:total_ins] + results.to_csv(f"{output_affix}results.csv") + draw(f"{output_affix}results.csv") diff --git a/src/tsnkit/benchmark/_processes.py b/src/tsnkit/benchmark/_processes.py new file mode 100644 index 00000000..ffb3e4f3 --- /dev/null +++ b/src/tsnkit/benchmark/_processes.py @@ -0,0 +1,130 @@ +import os +import signal +import sys +import time +import warnings +from functools import partialmethod + +import psutil +from tqdm import tqdm + +from ..utils import Result +from ..simulation import tas + +SCRIPT_DIR = os.path.dirname(__file__) + + +def kill_process(proc: psutil.Process): + if sys.platform == "win32" or sys.platform == "cygwin": + proc.kill() + else: + proc.send_signal(signal.SIGKILL) + + +def interrupt_process(proc: psutil.Process): + if sys.platform == "win32" or sys.platform == "cygwin": + proc.terminate() + else: + proc.send_signal(signal.SIGINT) + + +def print_output(name: str, flag: str, solve_time: float, total_time: float, total_mem: float): + print_format = "| {:<13} | {:<13} | {:<6} | {:<10} | {:<10} | {:<10}" + print(print_format.format( + time.strftime("%d~%H:%M:%S"), + name, + flag, + round(solve_time, 3), + round(total_time, 3), + round(total_mem, 3)), + flush=True) + + +def killif(main_proc, mem_limit, time_limit, sig, queue): + ''' + Kill the process if it uses more than mem memory or more than time seconds + Args: + main_proc: the main process id + mem_limit: the memory limit, uint: GB + time_limit: the time limit, uint: seconds + ''' + time.sleep(1) + BREAK_TIME = 0.5 ## Check every 0.5 seconds + WAIT_TIME = 60 ## Wait for 1 mins before next killing + self_proc = os.getpid() + mem_limit = mem_limit * 1024 ** 3 + pids_killed = set() + pids_killed_time = {} + while True: + # _keep_alive = False + _current_time = time.time() + # kill the process if it uses more than mem memory or more than time seconds + for proc in psutil.process_iter( + ['pid', 'name', 'username', 'ppid', 'cpu_times', 'status']): + if 'python' not in proc.name() and 'cpoptimizer' not in proc.name(): + continue + if proc.ppid() != main_proc and 'cpoptimizer' not in proc.name(): + continue + if proc.pid == main_proc or proc.pid == self_proc: + continue + # if proc.cpu_times().user > 0 and proc.status() != psutil.STATUS_ZOMBIE: + # _keep_alive = True + if proc.pid in pids_killed and _current_time - pids_killed_time[proc.pid] < WAIT_TIME: + continue + try: + mem = proc.memory_info().rss + start_time = proc.create_time() + elapse_time = _current_time - start_time + if elapse_time > time_limit * 1.1 or mem > mem_limit: + if proc.status() == psutil.STATUS_ZOMBIE or elapse_time > time_limit * 1.2 or mem > mem_limit * 1.1: + kill_process(proc) + if not (sys.platform == "win32" or sys.platform == "cygwin"): + sig.value += 1 + + interrupt_process(proc) + + pids_killed.add(proc.pid) + pids_killed_time[proc.pid] = _current_time + + proc_time = proc.cpu_times().user + queue.put([round(proc.cpu_times().user, 3), mem]) + print_output("-", str(Result.unknown), proc_time, proc_time, mem / (1024 ** 2)) + + if sys.platform == "win32" or sys.platform == "cygwin": + sig.value += 1 + + except (psutil.NoSuchProcess, psutil.AccessDenied, + psutil.ZombieProcess): + pass + except Exception as e: + pass + time.sleep(BREAK_TIME) + + +def validate_schedule(task_path, file_num): + tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) # disable pbar during simulations + log = tas.simulation(task_path, f"./{file_num}-", it=2) + return log + + +def str_flag(flag): + if flag == Result.schedulable.value: + return str(Result.schedulable) + elif flag == Result.unschedulable.value: + return str(Result.unschedulable) + elif flag == Result.unknown.value: + return str(Result.unknown) + else: + return str(Result.error) + + +def run(alg, file_num: str, workers: int): + path = SCRIPT_DIR + "/data/" + file_num + stats = alg(file_num, path + "_task.csv", path + "_topo.csv", workers=workers) + + return stats.to_list() + + +def mute(): + sys.stdout = open(os.devnull, 'w') + warnings.filterwarnings("ignore") diff --git a/src/tsnkit/benchmark/data/100_task.csv b/src/tsnkit/benchmark/data/100_task.csv new file mode 100644 index 00000000..b74d1537 --- /dev/null +++ b/src/tsnkit/benchmark/data/100_task.csv @@ -0,0 +1,101 @@ +stream,src,dst,size,period,deadline,jitter +0,9,[11],100,800000,800000,800000 +1,9,[13],400,200000,200000,200000 +2,11,[8],100,400000,400000,400000 +3,13,[14],200,200000,200000,200000 +4,10,[11],400,100000,100000,100000 +5,8,[12],300,800000,800000,800000 +6,15,[11],400,200000,200000,200000 +7,9,[14],100,100000,100000,100000 +8,8,[12],200,100000,100000,100000 +9,13,[8],200,200000,200000,200000 +10,9,[8],300,200000,200000,200000 +11,15,[14],500,800000,800000,800000 +12,10,[15],100,200000,200000,200000 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a/src/tsnkit/benchmark/data/99_task.csv b/src/tsnkit/benchmark/data/99_task.csv new file mode 100644 index 00000000..ba3621f2 --- /dev/null +++ b/src/tsnkit/benchmark/data/99_task.csv @@ -0,0 +1,101 @@ +stream,src,dst,size,period,deadline,jitter +0,12,[11],300,100000,100000,100000 +1,12,[10],400,800000,800000,800000 +2,8,[15],300,800000,800000,800000 +3,9,[14],100,100000,100000,100000 +4,14,[12],500,100000,100000,100000 +5,9,[13],300,100000,100000,100000 +6,11,[15],300,800000,800000,800000 +7,12,[10],500,100000,100000,100000 +8,8,[14],100,400000,400000,400000 +9,10,[12],300,800000,800000,800000 +10,9,[13],200,100000,100000,100000 +11,11,[9],200,100000,100000,100000 +12,15,[10],500,800000,800000,800000 +13,11,[13],200,800000,800000,800000 +14,14,[9],300,400000,400000,400000 +15,12,[14],100,100000,100000,100000 +16,10,[13],400,100000,100000,100000 +17,11,[13],500,800000,800000,800000 +18,10,[9],400,200000,200000,200000 +19,10,[12],200,400000,400000,400000 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b/src/tsnkit/benchmark/data/9_task.csv @@ -0,0 +1,11 @@ +stream,src,dst,size,period,deadline,jitter +0,47,[45],300,1000000,1000000,1000000 +1,45,[46],300,2000000,2000000,2000000 +2,54,[45],200,1000000,1000000,1000000 +3,32,[43],100,2000000,2000000,2000000 +4,46,[47],300,4000000,4000000,4000000 +5,46,[47],100,4000000,4000000,4000000 +6,46,[50],300,500000,500000,500000 +7,37,[39],200,1000000,1000000,1000000 +8,55,[39],400,500000,500000,500000 +9,42,[51],100,500000,500000,500000 diff --git a/src/tsnkit/benchmark/data/9_topo.csv b/src/tsnkit/benchmark/data/9_topo.csv new file mode 100644 index 00000000..5b3ba277 --- /dev/null +++ b/src/tsnkit/benchmark/data/9_topo.csv @@ -0,0 +1,111 @@ +link,q_num,rate,t_proc,t_prop +"(0, 1)",8,1,2000,0 +"(0, 28)",8,1,2000,0 +"(1, 0)",8,1,2000,0 +"(1, 2)",8,1,2000,0 +"(1, 29)",8,1,2000,0 +"(2, 1)",8,1,2000,0 +"(2, 3)",8,1,2000,0 +"(2, 30)",8,1,2000,0 +"(3, 2)",8,1,2000,0 +"(3, 4)",8,1,2000,0 +"(3, 31)",8,1,2000,0 +"(4, 3)",8,1,2000,0 +"(4, 5)",8,1,2000,0 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'#91A0A5', '#E5E2B9', '#88878D', + '#E8D3C0', '#7D7465', '#789798', + '#7A8A71', '#9AA690']) + +dark_palette = sns.color_palette([ + '#686789', '#B77F70', '#E5E2B9', '#BEB1A8', '#A79A89', '#8A95A9', + '#ECCED0', '#7D7465', '#E8D3C0', '#7A8A71', '#789798', '#B57C82', + '#9FABB9', '#B0B1B6', '#99857E', '#88878D', '#91A0A5', +]) + +METHOD_ORDER = ['smt_wa', 'smt_nw', 'jrs_wa', 'at', 'jrs_nw_l', 'ls', 'jrs_mc', 'i_ilp', 'i_omt', 'cg', 'jrs_nw', + 'smt_fr', 'cp_wa', 'ls_tb', 'ls_pl', 'smt_pr', 'dt'] + +marker_dict = {'jrs_wa': 'o', 'jrs_mc': 'o', 'jrs_nw_l': '^', 'jrs_nw': '^', 'ls': 's', 'i_ilp': 's', 'cg': 'v', + 'smt_wa': 'D', 'at': 'D', 'cp_wa': '*', 'smt_pr': '*', 'i_omt': 'p', 'ls_tb': 'p', 'ls_pl': 'p', + 'smt_nw': 'X', 'smt_fr': 'X', 'dt': '.'} + +dash_dict = {name: (2, 2) for name in METHOD_ORDER} + +ALPHA_REJ = 0.5 + +gradient_palette = sns.color_palette(sns.color_palette(dark_palette).as_hex(), n_colors=16) + +_temp_morandi = ["#F0F0F0", "#E0E0E0", "#C0C0C0", "#8B8680", "#808080"] + +morandi_cmap = mcolors.LinearSegmentedColormap.from_list("morandi_cmap", _temp_morandi) + +default_color = "#FFEBCD" +morandi_cmap.set_bad(color=default_color) + +extended_colors = [default_color] + _temp_morandi +extended_cmap = mcolors.LinearSegmentedColormap.from_list("morandi_cmap", extended_colors) + + +def get_schedulability(data: pd.DataFrame, var: str): + data = copy.deepcopy(data) + data[var] = data["data_id"].map(dict(zip(DATASET_LOGS["id"], DATASET_LOGS[var]))) + + group_index = ['name', 'flag', var] + grouped_data = data.groupby(group_index, as_index=False)['data_id'].count().groupby('flag') + + schedulability = pd.merge( + left=grouped_data.get_group('successful')[['name', var, 'data_id']].rename( + columns={'data_id': 'num_successful'}), + right=grouped_data.get_group('infeasible')[['name', var, 'data_id']].rename( + columns={'data_id': 'num_infeasible'}), + how='outer', + on=['name', var] + ) + schedulability = pd.merge( + left=schedulability, + right=grouped_data.get_group('unknown')[['name', var, 'data_id']].rename( + columns={'data_id': 'num_unknown'}), + how='outer', + on=['name', var] + ) + schedulability = schedulability.fillna(0) + + # lower bound schedulability + schedulability['schedulability'] = (schedulability['num_successful']) / ( + schedulability['num_successful'] + schedulability['num_infeasible'] + schedulability['num_unknown']) + + return schedulability + + +def test_evidence_thres(stat: pd.DataFrame, var: str, confidence=0.9): + stat_pass = stat[ + stat['num_unknown'] <= (stat['num_successful'] + stat['num_infeasible'] + stat["num_unknown"]) * confidence] + stat_rej = stat[ + stat['num_unknown'] > (stat['num_successful'] + stat['num_infeasible'] + stat["num_unknown"]) * confidence] + + if stat[var].dtype == 'int' or stat[var].dtype == 'int64': + var_range = stat[var].unique() + var_range.sort() + stat_rej = stat_rej.sort_values(["name", var]).reset_index(drop=True) + addition_points = [] + for i, row in stat_rej.iterrows(): + var_index = np.where(var_range == row[var])[0][0] + if (var_index - 1 >= 0 + and var_range[var_index - 1] not in stat_rej[stat_rej["name"] == row["name"]][var].unique()): + addition_points.append( + stat_pass.loc[ + (stat_pass["name"] == row["name"]) & + (stat_pass[var] == var_range[var_index - 1]) + ] + ) + if (var_index + 1 < len(var_range) + and var_range[var_index + 1] not in stat_rej[stat_rej["name"] == row["name"]][var].unique()): + addition_points.append( + stat_pass.loc[ + (stat_pass["name"] == row["name"]) & + (stat_pass[var] == var_range[var_index + 1]) + ] + ) + stat_rej = pd.concat([stat_rej] + addition_points) + + stat_pass = stat_pass.fillna(0).reset_index(drop=True) + stat_rej = stat_rej.fillna(0).reset_index(drop=True) + + return stat_pass, stat_rej + + +def remove_duplicate_legend(ax): + handles, labels = ax.get_legend_handles_labels() + handles = handles[::-1] + labels = labels[::-1] + unique_labels = [] + unique_handles = [] + for i in range(len(labels)): + label = labels[i] + if label not in unique_labels: + unique_labels.append(label) + unique_handles.append(handles[i]) + return unique_handles[::-1], unique_labels[::-1] + + +def draw_streams(df: pd.DataFrame, file_name: str): + draw_fig4(df, "num_stream", "Number of Streams", file_name) + + +def draw_bridges(df: pd.DataFrame, file_name: str): + draw_fig4(df, "num_sw", "Number of Bridges", file_name) + + +def draw_frames(df: pd.DataFrame, file_name: str): + frames_list = [] + for piid in DATASET_LOGS["id"]: + task = pd.read_csv(SCRIPT_DIR + "/data/" + str(piid) + "_task.csv") + cycle = np.lcm.reduce(task["period"]) + frames = 0 + for period in task["period"]: + frames += cycle / period + frames = np.power(2, np.log2(frames).astype(int)) + frames_list.append(frames) + DATASET_LOGS["num_frame"] = frames_list + draw_fig4(df, "num_frame", "Number of Frames", file_name) + + +def draw_links(df: pd.DataFrame, file_name: str): + links_list = [] + for piid in DATASET_LOGS["id"]: + topo = pd.read_csv(SCRIPT_DIR + "/data/" + str(piid) + "_topo.csv") + links = len(topo["link"]) + links = (links // 50 + 1) * 50 # discreet + links_list.append(links) + DATASET_LOGS["num_link"] = links_list + draw_fig4(df, "num_link", "Number of Links", file_name) + + +def draw_fig4(df: pd.DataFrame, var: str, graph_name: str, file_name: str): + plt.rcParams['axes.axisbelow'] = True + plt.figure(figsize=(3, 2)) + + schedulability = get_schedulability(df, var) + stat_pass, stat_rej = test_evidence_thres(schedulability, var) + + # plot rejected points + ax = sns.lineplot(data=stat_rej, + x=var, + y='schedulability', + hue="name", + style="name", + palette=ordered_palette, + hue_order=METHOD_ORDER, + markers=marker_dict, + dashes=dash_dict, + alpha=ALPHA_REJ, + markeredgecolor=None, + fillstyle='none', + linewidth=1.2, + markersize=6) + + for stat in list([x[1].reset_index(drop=True) for x in stat_pass.groupby('name')]): + ax = sns.lineplot(ax=ax, + data=stat, + x=var, + y="schedulability", + hue="name", + style="name", + palette=ordered_palette, + hue_order=METHOD_ORDER, + markers=marker_dict, + dashes=False, + markeredgecolor=None, + fillstyle='none', + linewidth=1.2, + markersize=6, + ) + ax.grid(axis='y') + ax.set_ylim(0, 1) + ax.set_xlabel(graph_name, fontsize=12) + ax.set_ylabel('Schedulable Ratio', fontsize=12) + legend = ax.legend(*remove_duplicate_legend(ax), ncol=3, loc="upper center", prop={'size': 10}, mode="expand", + bbox_to_anchor=(0.0, 1.4, 1.0, 0), frameon=False) + legend.remove() + plt.savefig(file_name + ".pdf", bbox_inches="tight") + + +def draw_period(df: pd.DataFrame, file_name: str): + period_dict = {3: "Harmonic Sparse", 4: "Harmonic Dense"} + DATASET_LOGS["period"] = DATASET_LOGS["period"].apply(lambda x: period_dict[x]) + schedulability = get_schedulability(df, "period") + draw_fig5(schedulability, "period", list(period_dict.values()), file_name) + + +def draw_payload(df: pd.DataFrame, file_name: str): + size_dict = {2: "Small"} + DATASET_LOGS["size"] = DATASET_LOGS["size"].apply(lambda x: size_dict[x]) + schedulability = get_schedulability(df, "size") + draw_fig5(schedulability, "size", list(size_dict.values()), file_name) + + +def draw_deadline(df: pd.DataFrame, file_name: str): + deadline_dict = {1: "Implicit"} + DATASET_LOGS["deadline"] = DATASET_LOGS["deadline"].apply(lambda x: deadline_dict[x]) + schedulability = get_schedulability(df, "deadline") + draw_fig5(schedulability, "deadline", list(deadline_dict.values()), file_name) + + +def draw_topo(df: pd.DataFrame, file_name: str): + topo_dict = {0: "Line", 1: "Mesh"} + DATASET_LOGS["topo"] = DATASET_LOGS["topo"].apply(lambda x: topo_dict[x]) + schedulability = get_schedulability(df, "topo") + draw_fig5(schedulability, "topo", ["Line", "Mesh"], file_name) + + +def draw_fig5(df: pd.DataFrame, var: str, hue_order: list, file_name: str): + plt.figure(figsize=(18, 1)) + plt.rc('xtick', labelsize=8) + plt.rcParams['axes.axisbelow'] = True + ax = sns.barplot( + data=df, + y="schedulability", + x="name", + hue=var, + hue_order=hue_order, + palette=dark_palette, + order=METHOD_ORDER + ) + plt.xlabel('') + plt.grid(axis='y') + plt.yticks(np.arange(0, 1.00001, step=0.2)) + plt.ylabel('Schedulable Ratio') + ax.legend(*remove_duplicate_legend(ax), ncol=6, loc="upper center", prop={'size': 10}, mode="expand", + bbox_to_anchor=(0.0, 1.4, 1.0, 0), frameon=False) + plt.savefig(f"{file_name}.pdf", bbox_inches="tight") + + +def get_comparison_matrix(df: pd.DataFrame): + index_map = {method: i for i, method in enumerate(df["name"].unique())} + num_methods = len(index_map) + group_index = ["data_id"] + + single_data = df[["name", "data_id", "flag"]] + paired_data = pd.merge( + left=single_data[single_data["flag"] != "unknown"], + right=single_data[single_data["flag"] != "unknown"], + on=group_index + ).dropna() + + comparison_matrix = np.zeros((num_methods, num_methods)) + all_result_matrix = np.zeros((num_methods, num_methods)) + + for i, row in paired_data.iterrows(): + x = row["name_x"] + y = row["name_y"] + if (row["flag_x"] == "successful") and (row["flag_y"] == "infeasible"): + comparison_matrix[index_map[x], index_map[y]] += 1 + all_result_matrix[index_map[x], index_map[y]] += 1 + + return comparison_matrix, all_result_matrix + + +def draw_comparison_matrix(df: pd.DataFrame, file_name: str): + comparison_matrix, all_result_matrix = get_comparison_matrix(df) + comparison_matrix[np.where(comparison_matrix == 0)] = np.nan + + methods = df["name"].unique() + num_methods = len(methods) + dominate_matrix = np.empty([num_methods, num_methods], dtype=str) + + for i in range(num_methods): + for j in range(num_methods): + sa_ij = comparison_matrix[i][j] + sa_ji = comparison_matrix[j][i] + if sa_ij > 0 and np.isnan(sa_ji): + dominate_matrix[i][j] = '✗' + + means = np.nanmean(np.nan_to_num(comparison_matrix / all_result_matrix), axis=0) + sorted_indices = np.argsort(means) + sorted_comparison_matrix = (comparison_matrix / all_result_matrix)[:, sorted_indices][sorted_indices, :] + sorted_dominate_matrix = dominate_matrix[:, sorted_indices][sorted_indices, :] + + plt.figure() + sns.heatmap(data=sorted_comparison_matrix, + xticklabels=[methods[x] for x in sorted_indices], + yticklabels=[methods[x] for x in sorted_indices], + cmap=extended_cmap, + cbar_kws={"label": "Schedulability Advantage"}, + linewidths=1, + linecolor="white", + vmin=0 + ) + sns.heatmap(data=sorted_comparison_matrix, + xticklabels=[methods[x] for x in sorted_indices], + yticklabels=[methods[x] for x in sorted_indices], + cmap=morandi_cmap, + cbar=False, + linewidths=1, + linecolor='white', + vmin=0, + annot=sorted_dominate_matrix, + fmt='' + ) + + plt.xticks(rotation=45, ha='right') + plt.savefig(f"{file_name}.pdf", bbox_inches="tight") + + +def get_runtime_stat(data: pd.DataFrame, var: str): + data = data[(data["flag"] != "unknown") | (data["total_mem"] < 4000)] + data.loc[:, ["total_time"]] = data["total_time"] / 60 + return data.groupby([var, "name"], as_index=False)["total_time"].mean() + + +def get_memory_stat(data: pd.DataFrame, var: str): + data = data[(data["flag"] != "unknown") | (data["total_time"] < 7200)] + return data.groupby([var, "name"], as_index=False)["total_mem"].mean() + + +def draw_scalability(df: pd.DataFrame, x: str, y: str, x_label: str, y_label: str, file_name: str): + df = copy.deepcopy(df) + df[x] = df["data_id"].map(dict(zip(DATASET_LOGS["id"], DATASET_LOGS[x]))) + + plt.rcParams['axes.axisbelow'] = True + plt.figure(figsize=(3, 2)) + + stat = get_runtime_stat(df, x) if y == "total_time" else get_memory_stat(df, x) + + schedulability = get_schedulability(df, x) + pass_rej = test_evidence_thres(schedulability, x) + stat_pass = pd.merge(stat, pass_rej[0], on=["name", x]) + stat_rej = pd.merge(stat, pass_rej[1], on=["name", x]) + + ax = sns.lineplot(data=stat_pass, + x=x, + y=y, + hue="name", + style="name", + palette=ordered_palette, + hue_order=METHOD_ORDER, + markers=marker_dict, + dashes=False, + markeredgecolor=None, + fillstyle="none", + linewidth=1.2, + markersize=6, ) + + ax = sns.lineplot(ax=ax, + data=stat_rej, + x=x, + y=y, + hue="name", + style="name", + palette=ordered_palette, + hue_order=METHOD_ORDER, + markers=marker_dict, + dashes=dash_dict, + alpha=ALPHA_REJ, + markeredgecolor=None, + fillstyle="none", + linewidth=1.2, + markersize=6, ) + + ax.grid(axis="y") + ax.set_xlabel(x_label, fontsize=12) + ax.set_ylabel(y_label, fontsize=12) + + legend = plt.legend(prop={"size": 7}) + legend.remove() + plt.savefig(f"{file_name}.pdf", bbox_inches="tight") + + +def draw_runtime(df: pd.DataFrame, file_name: str): + draw_scalability(df, "num_stream", "total_time", "Number of streams", "Runtime (Mins)", f"{file_name}_stream") + draw_scalability(df, "num_sw", "total_time", "Number of bridges", "Runtime (Mins)", f"{file_name}_bridge") + + +def draw_mem(df: pd.DataFrame, file_name: str): + draw_scalability(df, "num_stream", "total_mem", "Number of streams", "Memory (MB)", f"{file_name}_stream") + draw_scalability(df, "num_sw", "total_mem", "Number of bridges", "Memory (MB)", f"{file_name}_bridge") + + +def draw(path: str, output_affix="./"): + df = pd.read_csv(path) + draw_streams(df, f"{output_affix}stream") + draw_bridges(df, f"{output_affix}bridge") + draw_links(df, f"{output_affix}link") + draw_frames(df, f"{output_affix}frame") + draw_topo(df, f"{output_affix}topo") + draw_period(df, f"{output_affix}period") + draw_payload(df, f"{output_affix}payload") + draw_deadline(df, f"{output_affix}deadline") + draw_comparison_matrix(df, f"{output_affix}comparison_matrix") + draw_runtime(df, f"{output_affix}runtime") + draw_mem(df, f"{output_affix}mem") + + +if __name__ == "__main__": + draw("./results.csv")