def plot_benchmark_comparison(feature_name):
cpp_file = feature_name + '_cpp_benchmark.csv'
python_file = feature_name + '_python_benchmark.csv'
cpp_results = get_number_data(cpp_file)
python_results = get_number_data(python_file)
cpp_conversion_time = [d['Conversion Time'] / 10**6 for d in cpp_results]
cc = cpp_conversion_time
cpp_feature_time = [
d['Feature calculation time'] / 10**6 for d in cpp_results
]
cf = cpp_feature_time
python_feature_time = [
d['Feature calculation time'] / 10**6 for d in python_results
]
pf = python_feature_time
runs = [d['run id'] for d in python_results]
N = len(cpp_results)
X = np.arange(N)
width = 0.2
# Plot bar chart
plt.figure(1)
cpp_conversion_bar = plt.bar(X, cc, width)
cpp_feature_bar = plt.bar(X, cf, width, bottom=cc)
python_feature_bar = plt.bar(X + width, pf, width)
plt.ylabel('Time')
plt.title('Feature Time Comparison for ' + feature_name.capitalize())
plt.xticks(X, runs, rotation=90)
plt.legend(
(cpp_conversion_bar[0], cpp_feature_bar[0], python_feature_bar[0]),
('C++ Conversion', 'C++ Feature', 'Python Feature'))
plt.savefig(
os.path.join("figures", f"{feature_name}_benchmark_comparison.png"))
# Plot difference line plot
plt.figure(2)
total_difference = [pf[i] - (cc[i] + cf[i]) for i in range(N)]
feature_difference = [pf[i] - cf[i] for i in range(N)]
plt.plot(total_difference, label='Total difference')
plt.plot(feature_difference, label='Feature Difference')
plt.ylabel('Time')
plt.title('Feature Time Difference for ' + feature_name.capitalize())
plt.legend()
plt.savefig(os.path.join("figures", f"{feature_name}_time_difference.png"))
def plot_gpu_benchmark_comparison(feature_name):
cpp_file = feature_name + '_GPU_cpp_benchmark.csv'
gpu_file = feature_name + '_GPU_gpu_benchmark.csv'
cpp_results = get_number_data(cpp_file)
gpu_results = get_number_data(gpu_file)
cpp_feature_time = [
d['Feature calculation time'] / 10**6 for d in cpp_results
]
cf = cpp_feature_time
gpu_feature_time = [
d['Feature calculation time'] / 10**6 for d in gpu_results
]
gf = gpu_feature_time
runs = [d['run id'] for d in gpu_results]
N = len(cpp_results)
X = np.arange(N)
width = 0.2
# Plot bar chart
plt.figure(1)
cpp_feature_bar = plt.bar(X + width, cf, width, color='orange')
gpu_feature_bar = plt.bar(X, gf, width, color='red')
plt.ylabel('Time')
plt.title('Feature Time Comparison for ' + feature_name.capitalize())
plt.xticks(X, runs, rotation=90)
plt.legend((cpp_feature_bar[0], gpu_feature_bar[0]),
('C++ Feature', 'GPU Feature'))
plt.savefig(
os.path.join("figures",
f"gpu_{feature_name}_benchmark_comparison.png"))
def plot_line_log_scale(feature_name, python_file, cpp_file, gpu_file):
"""
Plot a line of the performance results in a log/log scale with a line (not bars)
:param python_file: the file with the python benchmarks
:param cpp_file: the file with the cpp benchmarks
:param gpu_file:the file with the gpu benchmarks
:return:
"""
python_results = get_number_data(python_file)
cpp_results = get_number_data(cpp_file)
gpu_results = get_number_data(gpu_file)
python_feature_time = np.asarray(
[d['Feature calculation time'] / 10**6 for d in python_results])
pf = np.log2(python_feature_time)
cpp_feature_time = np.asarray(
[d['Feature calculation time'] / 10**6 for d in cpp_results])
cf = np.log2(cpp_feature_time)
gpu_feature_time = np.asarray(
[d['Feature calculation time'] / 10**6 for d in gpu_results])
gf = np.log2(gpu_feature_time)
X = np.asarray([float(d['run id'].split('_')[0]) for d in python_results])
X = np.log2(X)
N = len(cpp_results)
py_plot = plt.plot(X[:len(python_results)], pf, color='green')
cpp_plot = plt.plot(X[:len(cpp_results)], cf, color='orange')
gpu_plot = plt.plot(X[:len(gpu_results)], gf, color='red')
plt.ylabel(' log(Time[s]) ')
plt.xlabel(' log(Nodes) ')
plt.title('Feature Time Comparison for ' + feature_name.capitalize())
plt.legend((py_plot[0], cpp_plot[0], gpu_plot[0]),
('Python', 'C++', 'GPU'))
plt.show()
def plot_gpu_benchmark_comparison(feature_name):
cpp_file = feature_name + '_GPU_gpu_new_benchmark.csv' #'_GPU_cpp_benchmark.csv'
gpu_file = feature_name + '_GPU_gpu_benchmark.csv'
gpu_old_file = feature_name + '_GPU_gpu_benchmark_old.csv'
cpp_results = get_number_data(cpp_file)
gpu_results = get_number_data(gpu_file)
gpu_old_results = get_number_data(gpu_old_file)
cpp_feature_time = [
d['Feature calculation time'] / 10**6 for d in cpp_results
]
cf = cpp_feature_time
gpu_feature_time = [
d['Feature calculation time'] / 10**6 for d in gpu_results
]
gf = gpu_feature_time
gpu_old_feature_time = [
d['Feature calculation time'] / 10**6 for d in gpu_old_results
]
gof = gpu_old_feature_time
runs = [d['run id'] for d in gpu_results]
# if feature_name == 'Motif3' or feature_name == 'Motif4':
# N = len(gpu_results)
# else:
N = len(cpp_results)
# if feature_name == 'Motif4':
# cf.append(0)
# N+=1
X = np.arange(N)
print(X)
width = 0.2
# Plot bar chart
plt.figure(1)
# if feature_name == 'Motif3':
# gof.append(0)
# cf.append(0)
# elif feature_name == 'Motif4':
# [[gof.append(0), cf.append(0)] for _ in range(3)]
cpp_feature_bar = plt.bar(x=X + width,
height=cf,
width=width,
color='orange')
gpu_feature_bar = plt.bar(x=X, height=gf, width=width, color='red')
gpu_old_feature_bar = plt.bar(X - width, gof, width, color='green')
plt.gcf().subplots_adjust(bottom=0.3)
plt.ylabel('Time')
plt.title('Feature Time Comparison for ' + feature_name.capitalize())
plt.xticks(ticks=X, labels=runs, rotation=45, fontsize=6, ha='right')
plt.legend(
(cpp_feature_bar[0], gpu_feature_bar[0], gpu_old_feature_bar[0]),
('GPU New Feature', 'GPU Feature', 'GPU Old Feature'))
plt.savefig(
os.path.join("figures",
f"gpu_{feature_name}_benchmark_comparison.png"))