Run the loop in parallel

[Sumit112192]

📝 Description

Type: 🎢 infrastructure

Run the loop in montecarlo_main_loop related to finalize_array in parallel.

[tardis-bot]

*beep* *bop*
Hi human,
I ran ruff on the latest commit (1a47b9b).
Here are the outputs produced.
Results can also be downloaded as artifacts here.
Summarised output:

Complete output(might be large):

[Sumit112192]

@andrewfullard Are there any disadvantages to running the parallel loop that I made?

[codecov]

Codecov Report

Attention: Patch coverage is 0% with 2 lines in your changes missing coverage. Please review.

Project coverage is 69.25%. Comparing base (7231707) to head (1a47b9b).
Report is 4 commits behind head on master.

Files	Patch %	Lines
...ardis/transport/montecarlo/montecarlo_main_loop.py	0.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2769      +/-   ##
==========================================
- Coverage   69.86%   69.25%   -0.61%     
==========================================
  Files         192      196       +4     
  Lines       15048    15002      -46     
==========================================
- Hits        10513    10390     -123     
- Misses       4535     4612      +77     

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[tardis-bot]

*beep* *bop*

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[Sumit112192]

Benchmark the change to check if threading overloads shadows the parallel execution to increase run_time.

[Sumit112192]

Without Parallel

from numba import njit, prange
from numba.typed import List
import numpy as np

from tardis.transport.montecarlo.packet_trackers import RPacketTracker

@njit()
def aNumbaFuncWithoutParallel(no_of_packets):
    length = 100
    rpacket_trackers = List()
    for i in range(no_of_packets):
        rpacket_trackers.append(RPacketTracker(length))

    for i in range(no_of_packets):
        random_num_interaction = np.random.randint(2, length)
        rpacket_trackers[i].num_interactions = random_num_interaction
    for rpacket_tracker in rpacket_trackers:
        rpacket_tracker.finalize_array()

%timeit aNumbaFuncWithoutParallel(40000)

308 ms ± 6.33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithoutParallel(100000)

795 ms ± 56.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithoutParallel(200000)

1.52 s ± 70.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithoutParallel(400000)

3.04 s ± 88.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

[Sumit112192]

With Parallel

from numba import njit, prange
from numba.typed import List
import numpy as np

from tardis.transport.montecarlo.packet_trackers import RPacketTracker

@njit(parallel=True)
def aNumbaFuncWithParallel(no_of_packets):
    length = 100
    rpacket_tracker = List()
    for i in range(no_of_packets):
        rpacket_tracker.append(RPacketTracker(length))

    for i in range(no_of_packets):
        random_num_interaction = np.random.randint(1, length)
        rpacket_tracker[i].num_interactions = random_num_interaction
    for i in prange(no_of_packets):
        rpacket_tracker[i].finalize_array()

%timeit aNumbaFuncWithParallel(40000)

/tmp/ipykernel_34215/2781711506.py:12: NumbaTypeSafetyWarning: �[1m�[1m�[1munsafe cast from uint64 to int64. Precision may be lost.�[0m�[0m�[0m
  rpacket_tracker[i].finalize_array()


346 ms ± 34.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithParallel(100000)

783 ms ± 63.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithParallel(200000)

1.51 s ± 35.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit aNumbaFuncWithParallel(400000)

3.01 s ± 64.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

[Sumit112192]

Since the runtime is nearly the same even with parallel execution, I am closing this PR.