Sequential computing vs Parallel computing using Gurobi

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Chenying Yang

2023年11月22日 07:40

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Hi, 

I'm solving a MILP using Gurobi in Julia. The optimisation is done independently for 10k firms. I tried two ways. One is to define the model first up to some model parameters, and then loop over firms, get firm-specific parameters, and implement optimize!(model) in the loop sequentially. The other way is to utilize parallel computation over firms and define the model @everywhere in each worker, call the model for each worker, and solve the optimization problem in parallel. However, I found that over 1000 firms, 6 firms have different results comparing sequential vs. parallel computation. 

I tried to investigate the reason. In the first approach, if I define the model inside the loop such that each iteration(firm) calls the model again, the results are same as the parallel approach. So, I am wondering whether in a sequential setting, after one iteration of optimize!(model), whether there is any update in setting for the next iteration, because that could explain the differences. In parallel, every iteration calls a new (same) model, so there is no update of settings in Gurobi optimizer. 

関連

• Parallelism
• Deterministic

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  Simranjit Kaur

  • Gurobi Staff

  2026年11月13日 01:34

  This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
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  Mario Ruthmair

  • Gurobi Staff

  2023年11月23日 17:59

  Hi,

  I am not a Julia expert, but provided that the Julia interface can modify the Gurobi model without creating it from scratch and calls optimize() again on the updated model, you will most probably observe a different solution path. Gurobi tries to continue from the last results using as much information as possible from the last solve. This may lead to an alternative optimal solution having the same optimal objective value but potentially different variable values. Is this what happens when you mention "different results"?

  Best regards,
  Mario

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