This function distributes the load of jobs across the available processors.
It attempts to balance the load as much as possible.
Source code in darkmagic/parallel.py
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48 | def distribute_load(n_proc, masses, times):
"""
This function distributes the load of jobs across the available processors.
It attempts to balance the load as much as possible.
"""
# TODO: change to logging
print("Distributing load among processors.")
# Our jobs list is a list of tuples, where each tuple is a pair of (mass, time) indices
total_job_list = np.array(
list(itertools.product(range(len(masses)), range(len(times))))
)
n_jobs = len(total_job_list)
base_jobs_per_proc = n_jobs // n_proc # each processor has at least this many jobs
extra_jobs = 1 if n_jobs % n_proc else 0 # might need 1 more job on some processors
# Note a fan of using a sentinel value to indicate a "do nothing" job
job_list = JOB_SENTINEL * np.ones(
(n_proc, base_jobs_per_proc + extra_jobs, 2), dtype=int
)
for i in range(n_jobs):
proc_index = i % n_proc
job_index = i // n_proc
job_list[proc_index, job_index] = total_job_list[i]
if n_jobs > n_proc:
print("Number of jobs exceeds the number of processors.")
print(f"Baseline number of jobs per processor: {str(base_jobs_per_proc)}")
print(
"Remaining processors with one extra job: "
+ str(n_jobs - n_proc * base_jobs_per_proc)
)
elif n_jobs < n_proc:
print("Number of jobs is fewer than the number of processors.")
print("Consider reducing the number of processors for more efficiency.")
print(f"Total number of jobs: {n_jobs}")
else:
print("Number of jobs matches the number of processors. Maximally parallized.")
return job_list
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