# Parallelism

The purpose of this tutorial is to give a brief overview of parallelism in Julia as it pertains to JuMP, and to explain some of the things to be aware of when writing parallel algorithms involving JuMP models.

There are two main types of parallelism in Julia:

2. Distributed computing

In multi-threading, multiple tasks are run in a single Julia process and share the same memory. In distributed computing, tasks are run in multiple Julia processes with independent memory spaces. This can include processes across multiple physical machines, such as in a high-performance computing cluster.

Choosing and understanding the type of parallelism you are using is important because the code you write for each type is different, and there are different limitations and benefits to each approach. However, the best choice is highly problem dependent, so you may want to experiment with both approaches to determine what works for your situation.

To use multi-threading with Julia, you must either start Julia with the command line flag --threads=N, or you must set the JULIA_NUM_THREADS environment variable before launching Julia. For this documentation, we set the environment variable to:

julia> ENV["JULIA_NUM_THREADS"]
"4"

You can check how many threads are available using:

julia> Threads.nthreads()
4

The easiest way to use multi-threading in Julia is by placing the Threads.@threads macro in front of a for-loop:

julia> @time begin
ids = Int[]
global ids, my_lock
end
sleep(1.0)
end
end
1.037087 seconds (31.32 k allocations: 1.836 MiB, 2.02% compilation time)

This for-loop sleeps for 1 second on each iteration. Thus, if it had executed sequentially, it should have taken the same number of seconds as there are threads available. Instead, it took only 1 second, showing that the iterations were executed simultaneously. We can verify this by checking the Threads.threadid() of the thread that executed each iteration:

julia> ids
4-element Vector{Int64}:
2
4
1
3
Warn

When working with threads, you need to avoid race conditions, in which two threads attempt to write to the same variable at the same time. In the above example we avoided a race condition by using ReentrantLock. See the Multi-threading section of the Julia documentation for more details.

### Distributed computing

To use distributed computing with Julia, use the Distributed package:

julia> import Distributed

Like multi-threading, we need to tell Julia how many processes to add. We can do this either by starting Julia with the -p N command line argument, or by using Distributed.addprocs:

julia> import Pkg

julia> project = Pkg.project();

julia> workers = Distributed.addprocs(4; exeflags = "--project=\$(project.path)")
4-element Vector{Int64}:
2
3
4
5
Warning

Not loading the parent environment with --project is a common mistake.

The added processes are "worker" processes that we can use to do computation with. They are orchestrated by the process with the id 1. You can check what process the code is currently running on using Distributed.myid()

julia> Distributed.myid()
1

As a general rule, to get maximum performance you should add as many processes as you have logical cores available.

Unlike the for-loop approach of multi-threading, distributed computing extends the Julia map function to a "parallel-map" function Distributed.pmap. For each element in the list of arguments to map over, Julia will copy the element to an idle worker process and evaluate the function, passing the element as an input argument.

julia> function hard_work(i::Int)
sleep(1.0)
return Distributed.myid()
end
hard_work (generic function with 1 method)

julia> Distributed.pmap(hard_work, 1:4)
ERROR: On worker 2:
UndefVarError: #hard_work not defined
Stacktrace:
[...]

Unfortunately, if you try this code directly, you will get an error message that says On worker 2: UndefVarError: hard_work not defined. The error is thrown because, although process 1 knows what the hard_work function is, the worker processes do not.

To fix the error, we need to use Distributed.@everywhere, which evaluates the code on every process:

julia> Distributed.@everywhere begin
function hard_work(i::Int)
sleep(1.0)
return Distributed.myid()
end
end

Now if we run pmap, we see that it took only 1 second instead of 4, and that it executed on each of the worker processes:

julia> @time ids = Distributed.pmap(hard_work, 1:4)
1.202006 seconds (216.39 k allocations: 13.301 MiB, 4.07% compilation time)
4-element Vector{Int64}:
2
3
5
4
Tip

## Using parallelism the wrong way

### When building a JuMP model

For large problems, building the model in JuMP can be a bottleneck, and you may consider trying to write code that builds the model in parallel, for example, by wrapping a for-loop that adds constraints with Threads.@threads.

Unfortunately, you cannot build a JuMP model in parallel, and attempting to do so may error or produce incorrect results.

In most cases, we find that the reason for the bottleneck is not JuMP, but in how you are constructing the problem data, and that with changes, it is possible to build a model in a way that is not the bottleneck in the solution process.

Tip

Looking for help to make your code run faster? Ask for help on the community forum. Make sure to include a reproducible example of your code.

### With a single JuMP model

A common approach people try is to use parallelism with a single JuMP model. For example, to optimize a model over multiple right-hand side vectors, you may try:

using JuMP
import HiGHS
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@objective(model, Min, x)
solutions = Pair{Int,Float64}[]
set_lower_bound(x, i)
optimize!(model)
push!(solutions, i => objective_value(model))
end
end

This will not work, and attempting to do so may error, crash Julia or produce incorrect results.

## Using parallelism the right way

To use parallelism with JuMP, the simplest rule to remember is that each worker must have its own instance of a JuMP model.

With multi-threading, create a new instance of model in each iteration of the for-loop:

julia> using JuMP

julia> import HiGHS

julia> solutions = Pair{Int,Float64}[]

model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@objective(model, Min, x)
set_lower_bound(x, i)
optimize!(model)
push!(solutions, i => objective_value(model))
end
end

julia> solutions
10-element Vector{Pair{Int64, Float64}}:
7 => 7.0
4 => 4.0
1 => 1.0
9 => 9.0
5 => 5.0
8 => 8.0
10 => 10.0
2 => 2.0
6 => 6.0
3 => 3.0

### With distributed computing

With distributed computing, remember to evaluate all of the code on all of the processes using Distributed.@everywhere, and then write a function which creates a new instance of the model on every evaluation:

julia> Distributed.@everywhere begin
using JuMP
import HiGHS
end

julia> Distributed.@everywhere begin
function solve_model_with_right_hand_side(i)
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@objective(model, Min, x)
set_lower_bound(x, i)
optimize!(model)
return objective_value(model)
end
end

julia> solutions = Distributed.pmap(solve_model_with_right_hand_side, 1:10)
10-element Vector{Float64}:
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0

## Other types of parallelism

### GPU

JuMP does not support GPU programming, and few solvers support execution on a GPU.

### Parallelism within the solver

Many solvers use parallelism internally. For example, commercial solvers like Gurobi and CPLEX both parallelize the search in branch-and-bound. Solvers supporting internal parallelism will typically support the MOI.NumberOfThreads attribute, which you can set using set_optimizer_attribute.