CODEWITHSUNDEEP
pythonor-toolsoperations-researchcp-sat
stefano guerrieri
stefano guerrieri

Reputation: 143

CP-SAT: Higher num_search_workers value increases the resolution time

Dears,
I'm playing with num_search_workers parameter and I discovered a strange behaviour with or-tool 7.5 on windows.
I did the following tests on a 32 core machine and I discovered that 1 thread has the best performances.
Do you know why?:

start to solve using 1 threads ... solved in 13.578 secs

start to solve using 2 threads ... solved in 45.832 secs

start to solve using 4 threads ... solved in 53.031 secs

start to solve using 8 threads ... solved in 62.013 secs

start to solve using 16 threads ... solved in 157.5 secs

start to solve using 32 threads ... solved in 807.778 secs

start to solve using 64 threads ... solved in 386.252 secs

the model is more or less like the following:
consider that self.suggested_decisions is a dictionary of BoolVars (the decision variables) the problem is like:

model.Add(sum(self.scenario.constants['scaling_factor']*self.suggested_decisions[r][0] for r in self.all_records)>=sum(sum(self.suggested_decisions[r][d]*(int(0.60*self.scenario.constants['scaling_factor']))for r in self.all_records) for d in self.all_decisions))
model.Add(sum(int(self.scenario.dataset['AMOUNT_FINANCED'][r])*self.suggested_decisions[r][0] for r in self.all_records)>=2375361256)
model.Add(sum(self.scenario.constants['scaling_factor']*self.scenario.dataset['Bad'][r]*self.suggested_decisions[r][0] for r in self.all_records)<=sum(self.suggested_decisions[r][0]*int(self.scenario.constants['scaling_factor']*0.038) for r in self.all_records))
model.Maximize(sum(int(self.scenario.dataset[\'AMOUNT_FINANCED\'][r])*self.suggested_decisions[r][0] for r in self.all_records))

Upvotes: 1

Views: 1894

Answers (1)

Laurent Perron
Laurent Perron

Reputation: 11034

Welcome to the world of parallelism.

1 to 8 threads, you are just unlucky. Communications between workers change the search and slows it down.

Above 8 threads, you are most likely memory bound.

This being said, this is very rare.

Could you send me the model ?

Upvotes: 1

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