Python loops (numba)

One of the proposals of the best-practice is to use Numba JIT (just in time) compiler to speedup Python loops. The outcome of this optimization is shown below.

Compute Structure (numba)

	Master	Numba
Total elapsed Time [s]	1545.69	261.70
compute_step_1 elapsed time [s]	1544.10	253.29
compute_step_2 elapsed time [s]	0.56	2.9e-3
Total instructions	9.62e12	1.24e12
compute_step_1 instructions	9.62e12	1.19e12
compute_step_2 instructions	4.87e9	5.25e6
Total average IPC	2.02	1.72
compute_step_1 average IPC	2.02	1.71
compute_step_2 average IPC	2.63	1.53

By using Numba decorators we achieved a speedup of 5.9X, mostly thanks to reducing the total number of instructions 7.76 times. Numba’s compiled code is much more efficient than Python’s interpreter.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 676553 (POP1) and 824080 (POP2).

Currently, the project receives funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101143931 (POP3). The JU receives support from the European Union's Horizon Europe research and innovation programme and Spain, Germany, France, Portugal and the Czech Republic.