Automated Sparse Parallelism of ODEs via Tracing

Because the ModelingToolkit expressions obey Julia semantics, one can directly transform existing Julia functions into ModelingToolkit symbolic representations of the function by simply inputting the symbolic values into the function and using what is returned. For example, let's take the following numerical PDE discretization:

using ModelingToolkit, LinearAlgebra, SparseArrays

# Define the constants for the PDE
const α₂ = 1.0
const α₃ = 1.0
const β₁ = 1.0
const β₂ = 1.0
const β₃ = 1.0
const r₁ = 1.0
const r₂ = 1.0
const _DD = 100.0
const γ₁ = 0.1
const γ₂ = 0.1
const γ₃ = 0.1
const N = 32
const X = reshape([i for i in 1:N for j in 1:N],N,N)
const Y = reshape([j for i in 1:N for j in 1:N],N,N)
const α₁ = 1.0.*(X.>=4*N/5)

const Mx = Array(Tridiagonal([1.0 for i in 1:N-1],[-2.0 for i in 1:N],[1.0 for i in 1:N-1]))
const My = copy(Mx)
Mx[2,1] = 2.0
Mx[end-1,end] = 2.0
My[1,2] = 2.0
My[end,end-1] = 2.0

# Define the discretized PDE as an ODE function
function f(u,p,t)
    A = u[:,:,1]
    B = u[:,:,2]
    C = u[:,:,3]
    MyA = My*A
    AMx = A*Mx
    DA = @. _DD*(MyA + AMx)
    dA = @. DA + α₁ - β₁*A - r₁*A*B + r₂*C
    dB = @. α₂ - β₂*B - r₁*A*B + r₂*C
    dC = @. α₃ - β₃*C + r₁*A*B - r₂*C

We can build the ModelingToolkit version of this model by tracing the model function:

# Define the initial condition as normal arrays
@variables u[1:N,1:N,1:3]
du = simplify.(f(u,nothing,0.0))

The output, here the in-place modified du, is a symbolic representation of each output of the function. We can then utilize this in the ModelingToolkit functionality. For example, let's build a parallel version of f first:

fastf = eval(ModelingToolkit.build_function(du,u,

Now let's compute the sparse Jacobian function and compile a fast multithreaded version:

jac = ModelingToolkit.sparsejacobian(vec(du),vec(u))
fjac = eval(ModelingToolkit.build_function(jac,u,

It takes awhile for this to generate, but the results will be worth it! Now let's setup the parabolic PDE to be solved by DifferentialEquations.jl. We will setup the vanilla version and the sparse multithreaded version:

using OrdinaryDiffEq
u0 = zeros(N,N,3)
MyA = zeros(N,N);
AMx = zeros(N,N);
DA = zeros(N,N);
prob = ODEProblem(f!,u0,(0.0,10.0))
fastprob = ODEProblem(ODEFunction((du,u,p,t)->fastf(du,u),
                                   jac = (du,u,p,t) -> fjac(du,u),
                                   jac_prototype = similar(jac,Float64)),

Let's see the timing difference:

using BenchmarkTools
@btime solve(prob, TRBDF2()) # 33.073 s (895404 allocations: 23.87 GiB)
@btime solve(fastprob, TRBDF2()) # 209.670 ms (8208 allocations: 109.25 MiB)

Boom, an automatic 157x acceleration that grows as the size of the problem increases!