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benchmark_python/README.md

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+## Benchmark
+![](loading-timings-ns128.svg)
+![](loading-slowdown-ns128.svg)
+
+## Benchmarks details
+
+  - Create in memory a list of `N=128` one dimensional numpy arrays of length `L` starting from `L=2` up to `L=2^22=4_194_304` in steps of powers of two.
+  - Given that each item of the array is of type `float64`, i.e. 8 bytes, the size of the arrays goes from `16B` to `32M`
+  - The total memory required is at least `128 × 32M × 2 = 8G`
+  - Load the whole list in one big numpy array of size `N`x`L` (*good*) and `L`x`N` (*bad*). The corresponding loops are: 
+    ```python
+    # good loop (store each time series on a different row)
+    for row, time_series in enumerate(collection):
+        ts[row, :] = time_series
+    ```
+
+    ```python
+    # bad loop (store each time series on a different column)
+    for column, time_series in enumerate(collection):
+        ts[:, column] = time_series
+    ```
+  - Time the *bad* and the *good* loop
+  - Plot the timings for the *good* and the *bad* loop as a function of `L`
+  - Plot `slowdown = time_bad/time_good` as a function of `L`
+
+## Scripts
+
+  - [Benchmark script](bench.py) and [measurements](results_ns128)
+  - [Plotting script](bench_plot.py)