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benchmark_python/bench.py

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+#!/usr/bin/python3
+# Commands and ideas from: https://llvm.org/docs/Benchmarking.html
+# we assume that CPU 0 and 1 are together in an intel SMT-pair (hyperthreading)
+# - Disable address space randomization:
+#     echo 0 > /proc/sys/kernel/randomize_va_space
+# - Set scaling governor to performance for CPU 0
+#     echo performance > /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor
+# - Reserve CPU 0 fro our benchmark
+#     cset shield --cpu 0 --kthread=on
+# - Disable the SMT-peer of CPU 0, i.e. CPU 1
+#     echo 0 > /sys/devices/system/cpu/cpu1/online
+# - Disable turbo mode (works only on Intel):
+#     echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo
+# - Run with
+#    cset shield --exec -- ./bench.py
+#
+
+# To use the full power of the CPU, skip all the other steps and
+# just run with
+#    taskset --cpu-list 0 ./bench.py
+import os
+import sys
+import timeit
+
+import numpy as np
+
+NSERIES = (128, )
+POWS = 2**np.arange(2, 23, dtype=int)
+
+# Size of one dimensional numpy arrays of dtype 'float64':
+# A fix overhead of 96 bytes plus a variable size:
+# (n_items x 8 bytes)
+
+def load_data_row(x, time_series):
+    """Store one time series per raw"""
+    for row, ts in enumerate(time_series):
+        x[row,:] = ts
+    return x
+
+def load_data_column(x, time_series):
+    """Store one time series per column"""
+    for column, ts in enumerate(time_series):
+        x[:, column] = ts
+    return x
+
+if __name__ == '__main__':
+    for nseries in NSERIES:
+        print(30*'=', '\n', nseries)
+        float_items = POWS
+        byte_sizes = (float_items*8) #+ 96
+        bads = []
+        goods = []
+        results = open(f'results_ns{nseries}', 'wt')
+        for i, len_one_series in enumerate(float_items):
+            time_series = np.zeros((nseries, len_one_series), dtype='float64')
+            x = np.empty((nseries, len_one_series), dtype='float64')
+            print('Timing good...')
+            good = min(timeit.repeat(lambda: load_data_row(x, time_series), number=5))/5
+            x = np.empty((len_one_series, nseries), dtype='float64')
+            print('Timing bad...')
+            bad = min(timeit.repeat(lambda: load_data_column(x, time_series), number=5))/5
+            print(f'{len_one_series}/{POWS[-1]} {good} {bad}')
+            bads.append(bad)
+            goods.append(good)
+            results.write(f'{byte_sizes[i]} {good} {bad}\n')
+            results.flush()
+        results.close()
+