import os import argparse import sys import subprocess import psutil import os import collections import csv import numpy as np import pandas as pd import matplotlib from matplotlib import pyplot as plt import matplotlib.ticker as mticker import xlsxwriter import seaborn as sns from matplotlib.ticker import FormatStrFormatter from matplotlib.legend_handler import HandlerTuple from subprocess import Popen, PIPE from scipy.stats import gmean config_super_list = ['standard', 'async', 'uvm', 'uvm_prefetch', 'uvm_prefetch_async'] workload_super_list = ['vector_seq'] thread_batch_list = [2, 4, 8, 16, 32, 64, 128] def dict_to_list(input_dict): return_list = [] for elemement in input_dict: return_list.append(elemement) return return_list def addOptions(parser): parser.add_argument("-i", "--iterations", type=int, default=1, help="Number of iterations") parser.add_argument("-c", "--csv", type=str, default='output.xlsx', help="output trace log file") parser.add_argument("-f", "--figure", type=str, default='micro', help="output pdf file") parser.add_argument("-p", "--profiling", action='store_true', help="whether to run profiling or just parse results") def get_config_list(root_directory): config_list = [] for dict in os.listdir(root_directory): if os.path.isdir(dict) and dict in config_super_list: config_list.append(dict) return config_list def get_workload_dict(root_directory, config_list): workload_list = [] workload_dict = dict() for config in config_list: config_dir = root_directory + '/' + config for root, directories, files in os.walk(config_dir, topdown=False): for dir in directories: if dir in workload_super_list: if dir not in workload_dict: workload_dict[dir] = dict() workload_dict[dir][config] = os.path.join(root, dir) if dir not in workload_list: workload_list.append(dir) return workload_list, workload_dict def execute_bashes(workload_dict, thread_batch_list, iterations): for workload in workload_dict: if workload in workload_super_list: for config in workload_dict[workload]: if config in config_super_list: cur_dir = workload_dict[workload][config] pwd = os.getcwd() os.chdir(cur_dir) os.system('make') for thread_batch in thread_batch_list: for i in range(0, iterations): sh_file = './' + workload + '_' + str(thread_batch) exe_cmd = sh_file + ' > ' + str(thread_batch) + '_' + str(i) + '.log' os.system(exe_cmd) os.chdir(pwd) def process_file(log_file, config): result_dict = dict() text = open(log_file, "r") overlap = 0 result_dict['gpu_kernel'] = 0 result_dict['memcpy'] = 0 result_dict['memcpy_HtoD'] = 0 result_dict['memcpy_DtoH'] = 0 result_dict['allocation'] = 0 for line in text: line = line.replace(':', '') line = line.strip() words = line.split(',') if 'KERNEL' in words[0] and len(words) >= 4: result_dict['gpu_kernel'] += int(words[-1]) elif 'MEMCPY' in words[0]: if 'HTOD' in words[0] or 'HtoD' in words[0]: result_dict['memcpy_HtoD'] += int(words[-1]) else: result_dict['memcpy_DtoH'] += int(words[-1]) elif 'cudaMalloc' in words[0]: result_dict['allocation'] += int(words[3]) elif 'cudaFree' in words[0]: result_dict['allocation'] += int(words[3]) return_dict = dict() if config == 'uvm': return_dict['gpu_kernel'] = result_dict['gpu_kernel'] - result_dict['memcpy_HtoD'] else: return_dict['gpu_kernel'] = result_dict['gpu_kernel'] return_dict['memcpy'] = result_dict['memcpy_HtoD'] + result_dict['memcpy_DtoH'] return_dict['allocation'] = result_dict['allocation'] return return_dict def process_results(workload_dict, thread_batch_list, iterations): result_dict = dict() for workload in workload_dict: if workload in workload_super_list: result_dict[workload] = dict() for thread_batch in thread_batch_list: thread_batch_str = str(thread_batch) result_dict[workload][thread_batch_str] = dict() for config in workload_dict[workload]: if config in config_super_list: result_dict[workload][thread_batch_str][config] = [] cur_dir = workload_dict[workload][config] for i in range(0, iterations): log_file = cur_dir + '/' + thread_batch_str + '_' + str(i) + '.log' result_dict[workload][thread_batch_str][config].append(process_file(log_file, config)) return result_dict def export_csv(result_dict, thread_batch_list, config_list, iterations): workload_list = dict_to_list(result_dict) csv_list = [] for workload in workload_list: avg_dict = dict() for b in range(0, len(thread_batch_list)): thread_batch_str = str(thread_batch_list[b]) avg_dict[thread_batch_str] = dict() for c in range(0, len(config_list)): avg_dict[thread_batch_str][config_list[c]] = dict() metric_list = dict_to_list(result_dict[workload][thread_batch_str][config_list[c]][0]) for metric in metric_list: avg_dict[thread_batch_str][config_list[c]][metric] = 0 avg_dict[thread_batch_str][config_list[c]]['all'] = 0 for i in range(0, iterations): for metric in metric_list: avg_dict[thread_batch_str][config_list[c]][metric] += result_dict[workload][thread_batch_str][config_list[c]][i][metric] avg_dict[thread_batch_str][config_list[c]]['all'] += result_dict[workload][thread_batch_str][config_list[c]][i][metric] for c in range(0, len(config_list)): thread_batch_str_0 = str(thread_batch_list[0]) normarlized_all = avg_dict[thread_batch_str][config_list[c]]['all'] / avg_dict[thread_batch_str_0]['standard']['all'] for metric in metric_list: avg_dict[thread_batch_str][config_list[c]][metric] = (avg_dict[thread_batch_str][config_list[c]][metric] / avg_dict[thread_batch_str][config_list[c]]['all']) * normarlized_all workload_csv_file = workload + '_sensitivity_shared.csv' csv_list.append(workload_csv_file) out = open(workload_csv_file, "w") out.write('group,,') for i in range(0, len(config_list)): out.write(config_list[i]) if i != len(config_list) - 1: out.write(',') else: out.write(os.linesep) for thread_batch in thread_batch_list: thread_batch_str = str(thread_batch) for metric in metric_list: out.write(str(int (thread_batch * 256 * 4 / 1024)) + 'KB,' + metric + ',') for j in range(0, len(config_list)): out.write(str(avg_dict[thread_batch_str][config_list[j]][metric])) if j != len(config_list) - 1: out.write(',') else: out.write(os.linesep) out.close() return csv_list def plot_std_results(csv_file, output_file): df = pd.read_csv(csv_file, index_col=0) group_list = [] for index in df.index: if index not in group_list: group_list.append(index) col_list = df.columns ngroups = len(group_list) x = np.arange(ngroups) nbars = len(col_list) width = (1 - 0.4) / (1.5 * nbars) # the width of the bars matplotlib.rcParams["hatch.linewidth"] = 2 # patterns = [ "|" , "/", "-", "", "x", "-", "\\", "+", "o", "O" ] # patterns = [ "|" , "/", "x", "*", ".", "-", "\\", "+", "o", "O" ] # patterns = ["//", "//", "//", "//", "//", "//", "//"] patterns = ["", "", "", "", "", ""] # color_tab = ['#D9D9D9', '#BFBFBF', '#A6A6A6', '#7F7F7F', '#7F7F7F', '#7F7F7F'] color_tab = ['#ff0000', '#ff6d01','#46bdc6', '#4285f4', '#ea4335', '#34a853'] edge_color_tab = ['#000000', '#000000', '#000000', '#000000', '#000000', '#000000'] fig, ax = plt.subplots(figsize=[8.8, 2.8]) rects = [] for i in range(0, nbars): # height_cum = np.array([0.0] * ngroups) height_total = np.array([1 for g in group_list]) # y coo height_curr = np.array([float(df[col_list[i]][g]) for g in group_list]) # y coo rect_base = ax.bar(x - 0.3 + (3 * i + 1.5) * width / 2, # x coo height_curr / height_total, # y coo width, label=col_list[i], color=color_tab[i], edgecolor=edge_color_tab[i], linewidth=0.5 ) rects.append(rect_base) # height_cum += height_curr hdl_pair = [(rects[i]) for i in range(nbars)] ax.set_xticks(x) ax.set_xticklabels(group_list, rotation=0, weight='bold') ax.legend() # ax.yaxis.set_major_formatter(mticker.PercentFormatter(1.0)) plt.xticks(fontsize=11) plt.yticks(fontsize=9, weight='bold') plt.grid(axis='y') plt.xlabel("") plt.ylabel("Standard deviation / Mean") plt.tight_layout() # plt.margins(x=0.01, y=0.01) plt.savefig(output_file + '_std.pdf', bbox_inches='tight') plt.close() def plot_results(csv_file, output_file): df = pd.read_csv(csv_file, index_col=[0, 1]) group_list = [] subgrp_list = [] for index in df.index: if index[0] not in group_list: group_list.append(index[0]) if index[1] not in subgrp_list: subgrp_list.append(index[1]) col_list = df.columns ngroups = len(group_list) nsubgrps = len(subgrp_list) x = np.arange(ngroups) nbars = len(col_list) width = (1 - 0.4) / (1.5 * nbars) # the width of the bars matplotlib.rcParams["hatch.linewidth"] = 2 patterns = ["", "-", "/", "|", "/", "-", "x", "-", "\\", "+", "o", "O"] color_tab = ['#000000', '#0000ff', '#ff0000', '#ff6666', '#00ff00'] fig, ax = plt.subplots(figsize=[8.8, 3.8]) hdl_pair = [] rects = [] for i in range(0, nbars): height_cum = np.array([0.0] * ngroups) height_curr = np.array([df[col_list[i]][g][0] for g in group_list]) # y coo rect_base = ax.bar(x - 0.3 + (3 * i + 1.5) * width / 2, # x coo height_curr, # y coo width, label=col_list[i]+" "+subgrp_list[0], color=color_tab[i], edgecolor=color_tab[0], linewidth=0.25 ) rects.append(rect_base) height_cum += height_curr for j in range(1, 3): height_curr = np.array([df[col_list[i]][g][j] for g in group_list]) rect = ax.bar(x - 0.3 + (3 * i + 1.5) * width / 2, # x coo height_curr, # y coo width, label=col_list[i]+" "+subgrp_list[j], bottom=height_cum, color=color_tab[i], edgecolor=color_tab[0], linewidth=0.25, alpha=0.25 * (4 - j) ) rects.append(rect) height_cum += height_curr hdl_pair = [(rects[i*nsubgrps], rects[i*nsubgrps+1], rects[i*nsubgrps+2]) for i in range(nbars)] ax.set_xticks(x) ax.set_xticklabels(group_list, rotation=0) ax.legend(hdl_pair, col_list, loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.2), fontsize=14, handler_map={tuple: HandlerTuple(ndivide=None)}) # ax.legend() # ax.yaxis.set_major_formatter(mticker.PercentFormatter(1.0)) # ax.set_yscale('log') plt.xticks(fontsize=14, rotation=15) plt.yticks(fontsize=14) plt.grid(axis='y') plt.xlabel("") plt.ylabel("Normalized Time", fontsize=14) plt.xlabel("Shared Memory Capacity", fontsize=14) plt.tight_layout() # plt.margins(x=0.01, y=0.01) plt.savefig(output_file, bbox_inches='tight') plt.close() def main(): parser = argparse.ArgumentParser() addOptions(parser) options = parser.parse_args() iterations = options.iterations output_csv_file = options.csv output_figure_file = options.figure profiling = options.profiling root_directory = './' config_list = get_config_list(root_directory) print(config_list) workload_list, workload_dict = get_workload_dict(root_directory, config_list) print(workload_dict) if profiling: execute_bashes(workload_dict, thread_batch_list, iterations) result_dict = process_results(workload_dict, thread_batch_list, iterations) csv_list = export_csv(result_dict, thread_batch_list, config_super_list, iterations) for csv_file in csv_list: plot_results(csv_file, csv_file.replace(".csv", ".pdf")) if __name__ == '__main__': main()