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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()
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