I have a JuMP (mixed-integer) model where I’m trying to determine the upper and lower bounds on many variables. Each individual solve is very quick, but since I have many variables (~1,000), the overall solve time is slow (~5s).

One thing that I observed is that `julia`

often seems to be using only one core at a time, and I was hoping that parallelization might be able to reduce solve times significantly. However, I’m running into an issue that seems to have to do with there being only a single Gurobi environment created.

As recommended by @odow, I tried using a caching pool (https://github.com/JuliaOpt/Gurobi.jl/issues/119#issuecomment-375445961), but I can’t seem to get it working consistently:

```
julia> addprocs(2)
julia> @everywhere using JuMP, Gurobi
julia> @everywhere function upperbound_mip(x)
@JuMP.objective(x.m, Max, x)
JuMP.solve(x.m)
return JuMP.getobjectivevalue(x.m)
end
julia> m = JuMP.Model(solver = Gurobi.GurobiSolver(OutputFlag=0));
julia> @JuMP.variable(m, 0 <= x[i=1:100] <= i);
julia> upperbound_mip(x[1]) #added new line not in code sample from odow
julia> let x=x
wp = CachingPool(workers())
pmap(wp, (i)->upperbound_mip(x[i]), 1:100)
end
```

```
ERROR: On worker 2:
AssertionError: env.ptr_env != C_NULL
get_error_msg at /home/vtjeng/.julia/v0.6/Gurobi/src/grb_env.jl:38
Type at /home/vtjeng/.julia/v0.6/Gurobi/src/grb_env.jl:50 [inlined]
get_intattr at /home/vtjeng/.julia/v0.6/Gurobi/src/grb_attrs.jl:16
setvarLB! at /home/vtjeng/.julia/v0.6/Gurobi/src/GurobiSolverInterface.jl:187
#build#119 at /home/vtjeng/.julia/v0.6/JuMP/src/solvers.jl:338
#build at ./<missing>:0
#solve#116 at /home/vtjeng/.julia/v0.6/JuMP/src/solvers.jl:168
upperbound_mip at ./REPL[3]:3
#17 at ./REPL[8]:3
#exec_from_cache#193 at ./distributed/workerpool.jl:284
exec_from_cache at ./distributed/workerpool.jl:283
#106 at ./distributed/process_messages.jl:268 [inlined]
run_work_thunk at ./distributed/process_messages.jl:56
macro expansion at ./distributed/process_messages.jl:268 [inlined]
#105 at ./event.jl:73
```

As a note, Error solve JuMP models in parallel mentions that the best idea is to generate models locally.