问题
Im using GLPK with through Julia, and I need to optimize the same GLPK.Prob repeatedly. What changes between each optimization is that some combination of variables gets fixed to 0
simply put in pseudocode
lp = GLPK.Prob()
variables_to_block = [[1,2,3], [2,3], [6,7], [19,100,111]...]
for i in variables_to_block
block_vars(lp, i)
simplex(lp)
restore_vars(lp, i)
end
when I then run this, it looks like CPU1 acts as a scheduler, staying in the 9-11% range, and the load on CPU3 and CPU4 alternates between 0 and 100%, though never at the same time...the load on CPU2 stays at 0%
This can take a bit of time and I'd like to use all cores
There is however, a bit of a hassle to use the parallel features of Julia, particularly for lp-models because they involve pointers so (as far as I know) they cannot be copied easily between cores
Is there a way to either set the GLPK solver binary (or something) to automatically try to fully utilize all cores? either by compiling GLPK in such a way, or any other way
回答1:
As far as I know, GLPK is not multithreaded. If you must have a multithreaded solver, then consider using a newer one such as Gurobi or MOSEK. They have free academic licenses.
If commercial solvers are anathema to your purposes, then perhaps try the Splitting Cone Solver. It is released for free under the MIT License.
Otherwise, the suggestions in the comments (e.g. batch proessing) may be your only recourse.
来源：https://stackoverflow.com/questions/40082242/julia-parallel-mathematical-optimizers