#JUMMP TIME CODE#

When Julia is run with the custom image, the run time is now 0.7 seconds instead of 16: $ time julia -sysimage customimage model.jlĠ.68s user 0.22s system 153% cpu 0.587 total Sysimage_path = 'customimage.' * Libdl.dlext, A custom image for our problem can be created as follows: using PackageCompiler, Libdl In cases where the compilation latency is intolerable, JuMP is compatible with the PackageCompiler.jl package, which makes it easy to generate a custom sysimage (a binary extension to Julia that caches compiled code) that dramatically reduces the compilation latency. However, the compilation latency is independent on the problem size, and so 16 seconds of additional overhead may be tolerable for larger models that take minutes or hours to solve. Saving the problem in model.jl and calling from the command line results in: $ time julia model.jlġ5.78s user 0.48s system 100% cpu 16.173 totalĬlearly, 16 seconds is a large overhead to pay for solving this trivial model. Iteration Objective Infeasibilities num(sum) Problem not reduced by presolve: solving the LP Presolve : Reductions: rows 2(-0) columns 2(-0) elements 4(-0) - Not reduced

Print(io, solution_summary(model verbose = true))Įnd Running HiGHS 1.5.1 Ĭopyright (c) 2023 HiGHS under MIT licence terms model = Model(HiGHS.Optimizer add_bridges = false) A JuMP ModelĪs an example of compilation latency, consider the following linear program with two variables and two constraints: using JuMP, HiGHS If you only use constraints that are natively supported by the solver, you can disable bridges by passing add_bridges = false to Model. Disable bridges if none are being usedĪt present, the majority of the latency problems are caused by JuMP's bridging mechanism. Instead, use one of the suggested workflows from the Julia documentation. This doesn't work for JuMP, because we have to pay the compilation latency every time you run the script. In other languages, you might be used to a workflow like: $ julia my_script.jl This issue is actively being worked on, but there are a few things you can do to improve things.

#JUMMP TIME CODE#

This latency occurs because the first time you call JuMP code in each session, Julia needs to compile a lot of code specific to your problem. Similar to the infamous time-to-first-plot plotting problem, JuMP suffers from time-to-first-solve latency. This is particularly important if you're learning JuMP after using a language like MATLAB. The most important rule is to avoid global variables. The first thing to do is read the Performance tips section of the Julia manual. You're almost ready to write your own models, but before you do so there are some important things to be aware of.

Solving a problem using MathOptInterfaceīy now you should have read the other 'getting started' tutorials.Simple semidefinite programming examples.Optimal control for a Space Shuttle reentry trajectory.Computing the duals of a mixed-integer program.Sensitivity analysis of a linear program.

Jummp time