<jupyter_start><jupyter_text>Times are measured on grid and saved in csv format.First columns is the buffersize in bytes followed by the average of ssend/recv time. Send messages specifies how many times the ssend/recv roundtrips have been measured.<jupyter_code>!head data-t40-n1000-grid.csv
df = pd.read_csv('data-t40-n100000-grid.csv', sep=';')
data_head = df[:7]
plt.figure(dpi=100)
plt.title('First seven data points')
plt.xlabel('Buffersize in bytes')
plt.ylabel('ssend/recv time in microseconds')
plt.plot(data_head.buffersize, [x * 10e6 for x in data_head.t_average], 'r+');
data_head = df[7:]
plt.figure(dpi=100)
plt.xlabel('Buffersize in bytes')
plt.ylabel('ssend/recv time in microseconds')
plt.plot(data_head.buffersize, [x * 10e6 for x in data_head.t_average], 'r+');
df = pd.read_csv('data-lin-t200-n100000.csv', sep=';')
plt.figure(dpi=100)
plt.xlabel('Buffersize in bytes')
plt.ylabel('ssend/recv time in microseconds')
plt.plot(df.buffersize, [x * 10e6 for x in df.t_average], 'r+');
df = pd.read_csv('data-lin-t200-n1000000.csv', sep=';')
slope, intercept, r_value, p_value, std_err = stats.linregress(df.buffersize, df.t_average)
plt.figure(dpi=100)
plt.xlabel('Buffersize in bytes')
plt.ylabel('ssend/recv time in microseconds')
plt.plot(df.buffersize, [x * 10e6 for x in df.t_average], 'r+', label="Means of transmission times");
plt.plot(df.buffersize, (intercept+slope*df.buffersize)*10e6,
         label="Fitted line: slope {:0.3e}\nstd_error {:0.3e}".format(slope*10e6, std_err));
plt.legend();
throughput = (df.buffersize / 1024**2) / df.t_average 
plt.figure(dpi=100)
plt.xlabel('Buffersize in bytes')
plt.ylabel('Throughput in MB/s')
plt.plot(df.buffersize, throughput, 'r+');<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '656232', 'license': 'MIT', 'n_tokens_mistral': 752, 'n_tokens_neox': 664, 'n_words': 151, 'path': '7_sheet/data-visualization.ipynb', 'repo_name': 'robinhundt/practical-course-parallel-computing', 'size': '1734'}