30 lines
1.2 KiB
Markdown
30 lines
1.2 KiB
Markdown
## Benchmark
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## Benchmarks details
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- Create in memory a list of `N=128` one dimensional numpy arrays of length `L` starting from `L=2` up to `L=2²²=2<sup>22</sup>=4_194_304` in steps of powers of two.
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- 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`
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- The total memory required is at least `128 × 32M × 2 = 8G`
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- Load the whole list in one big numpy array of size `N`x`L` (*good*) and `L`x`N` (*bad*). The corresponding loops are:
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```python
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# good loop (store each time series on a different row)
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for row, time_series in enumerate(collection):
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ts[row, :] = time_series
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```
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```python
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# bad loop (store each time series on a different column)
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for column, time_series in enumerate(collection):
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ts[:, column] = time_series
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```
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- Time the *bad* and the *good* loop
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- Plot the timings for the *good* and the *bad* loop as a function of `L`
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- Plot `slowdown = time_bad/time_good` as a function of `L`
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## Scripts
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- [Benchmark script](bench.py) and [measurements](results_ns128)
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- [Plotting script](bench_plot.py)
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