Fceri_gamma2 Work-Precision Diagrams

The following benchmark is of 3744 ODEs with 58276 terms that describe a stiff chemical reaction network - the largest network in this folder. This fceri_gamma2 model was used as a benchmark model in Gupta et al.. It describes high-affinity human IgE receptor signalling Faeder et al.. We use ReactionNetworkImporters to load the BioNetGen model files as a Catalyst model, and then use ModelingToolkit to convert the Catalyst network model to ODEs.

using DiffEqBase, OrdinaryDiffEq, Catalyst, ReactionNetworkImporters,
      Sundials, Plots, DiffEqDevTools, ODEInterface, ODEInterfaceDiffEq,
      LSODA, TimerOutputs, LinearAlgebra, ModelingToolkit, BenchmarkTools,
      LinearSolve, RecursiveFactorization
using OrdinaryDiffEqAdamsBashforthMoulton, OrdinaryDiffEqBDF, OrdinaryDiffEqSDIRK, OrdinaryDiffEqVerner, OrdinaryDiffEqLowOrderRK

gr()
const to = TimerOutput()
tf = 150.0

# generate ModelingToolkit ODEs
@timeit to "Parse Network" prnbng = loadrxnetwork(BNGNetwork(), joinpath(@__DIR__, "Models/fceri_gamma2.net"))
show(to)
rn = complete(prnbng)
obs = [eq.lhs for eq in observed(rn)]

@timeit to "Create ODESys" osys = complete(Catalyst.ode_model(rn))
show(to)

tspan = (0.0, tf)
@timeit to "ODEProb SparseJac" sparsejacprob = ODEProblem{true, SciMLBase.FullSpecialize}(
    osys, Float64[], tspan, Float64[], jac = true, sparse = true)
show(to)
@timeit to "ODEProb No Jac" oprob = ODEProblem{true, SciMLBase.FullSpecialize}(
    osys, Float64[], tspan, Float64[])
show(to)
oprob_sparse = ODEProblem{true, SciMLBase.FullSpecialize}(
    osys, Float64[], tspan, Float64[]; sparse = true);

Scanning blocks...done
Parsing parameters...done
Creating parameters...done
Parsing species...done
Creating variables...done
Setting up expression bindings...done
Parsing groups...done
Parsing functions...done
Parsing and adding reactions...done
──────────────────────────────────────────────────────────────────────────
                                 Time                    Allocations      
                        ───────────────────────   ────────────────────────
   Tot / % measured:         11.0s /  99.8%           2.26GiB / 100.0%    

Section         ncalls     time    %tot     avg     alloc    %tot      avg
──────────────────────────────────────────────────────────────────────────
Parse Network        1    10.9s  100.0%   10.9s   2.26GiB  100.0%  2.26GiB
───────────────────────────────────────────────────────────────────────────
─────────────────────────────────────────────────────────────────────────
                                 Time                    Allocations      
                        ───────────────────────   ────────────────────────
   Tot / % measured:         42.4s /  92.5%           8.04GiB /  92.2%    

Section         ncalls     time    %tot     avg     alloc    %tot      avg
──────────────────────────────────────────────────────────────────────────
Create ODESys        1    28.3s   72.1%   28.3s   5.15GiB   69.5%  5.15GiB
Parse Network        1    10.9s   27.9%   10.9s   2.26GiB   30.5%  2.26GiB
───────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────
──
                                     Time                    Allocations   
   
                            ───────────────────────   ─────────────────────
───
     Tot / % measured:            652s /  99.5%           67.0GiB /  99.1% 
   

Section             ncalls     time    %tot     avg     alloc    %tot      
avg
───────────────────────────────────────────────────────────────────────────
───
ODEProb SparseJac        1     609s   94.0%    609s   58.9GiB   88.8%  58.9
GiB
Create ODESys            1    28.3s    4.4%   28.3s   5.15GiB    7.8%  5.15
GiB
Parse Network            1    10.9s    1.7%   10.9s   2.26GiB    3.4%  2.26
GiB
───────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────
──────
                                     Time                    Allocations   
   
                            ───────────────────────   ─────────────────────
───
     Tot / % measured:            663s /  99.5%           69.0GiB /  99.1% 
   

Section             ncalls     time    %tot     avg     alloc    %tot      
avg
───────────────────────────────────────────────────────────────────────────
───
ODEProb SparseJac        1     609s   92.3%    609s   58.9GiB   86.2%  58.9
GiB
Create ODESys            1    28.3s    4.3%   28.3s   5.15GiB    7.5%  5.15
GiB
ODEProb No Jac           1    11.6s    1.8%   11.6s   2.05GiB    3.0%  2.05
GiB
Parse Network            1    10.9s    1.7%   10.9s   2.26GiB    3.3%  2.26
GiB
───────────────────────────────────────────────────────────────────────────
───

@show numspecies(rn) # Number of ODEs
@show numreactions(rn) # Approx. number of terms in the ODE
@show length(parameters(rn)); # Number of Parameters

numspecies(rn) = 3744
numreactions(rn) = 58276
length(parameters(rn)) = 26

Time ODE derivative function compilation

As compiling the ODE derivative functions has in the past taken longer than running a simulation, we first force compilation by evaluating these functions one time.

u = oprob.u0
du = copy(u)
p = oprob.p
@timeit to "ODE rhs Eval1" sparsejacprob.f(du, u, p, 0.0)
sparsejacprob.f(du, u, p, 0.0)

3744-element Vector{Float64}:
   -6.376571413863561
 -560.0
    0.0
 -566.3765714138635
    6.376571413863561
  560.0
    0.0
    0.0
    0.0
    0.0
    ⋮
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0

We also time the ODE rhs function with BenchmarkTools as it is more accurate given how fast evaluating f is:

@btime sparsejacprob.f($du, $u, $p, 0.0)

102.139 μs (2 allocations: 480 bytes)
3744-element Vector{Float64}:
   -6.376571413863561
 -560.0
    0.0
 -566.3765714138635
    6.376571413863561
  560.0
    0.0
    0.0
    0.0
    0.0
    ⋮
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0
    0.0

Picture of the solution

sol = solve(oprob, CVODE_BDF(), saveat = tf/1000.0, reltol = 1e-5, abstol = 1e-5)
plot(sol; idxs = obs, legend = false, fmt = :png)

For these benchmarks we will be using the time-series error with these saving points.

Generate Test Solution

@time sol = solve(sparsejacprob, CVODE_BDF(linear_solver = :GMRES), reltol = 1e-15, abstol = 1e-15)
test_sol = TestSolution(sol);

115.729877 seconds (7.11 M allocations: 1.146 GiB, 0.65% gc time, 92.55% co
mpilation time)

Setups

Sets plotting defaults

default(legendfontsize = 7, framestyle = :box, gridalpha = 0.3, gridlinewidth = 2.5)

Declare pre-conditioners

using IncompleteLU, LinearAlgebra

jaccache = sparsejacprob.f.jac(oprob.u0, oprob.p, 0.0)
W = I - 1.0*jaccache
prectmp = ilu(W, τ = 50.0)
preccache = Ref(prectmp)

const τ1 = 5
function psetupilu(p, t, u, du, jok, jcurPtr, gamma)
    if !jok
        sparsejacprob.f.jac(jaccache, u, p, t)
        jcurPtr[] = true

        # W = I - gamma*J
        @. W = -gamma*jaccache
        idxs = diagind(W)
        @. @view(W[idxs]) = @view(W[idxs]) + 1

        # Build preconditioner on W
        preccache[] = ilu(W, τ = τ1)
    end
end
function precilu(z, r, p, t, y, fy, gamma, delta, lr)
    ldiv!(z, preccache[], r)
end

const τ2 = 5
function incompletelu(A, p)
    Pl = ilu(convert(AbstractMatrix, A); τ = τ2)
    return Pl, I
end;

Sets tolerances

abstols = 1.0 ./ 10.0 .^ (5:7)
reltols = 1.0 ./ 10.0 .^ (5:7);

Note on cost: this is one of the most expensive documents in the repository. Every work-precision point costs three solves (one for the error, one to warm up, one that is timed), and on this network a single stiff solve at these tolerances runs from a few seconds (preconditioned Krylov) to well over ten minutes (dense direct solves). The tolerance grid was therefore cut from four points (1e-5 … 1e-8) to three, saveat was relaxed from tf/10000 to tf/1000, and the methods that are an order of magnitude off the pace are timed once in the isolation section below instead of being swept across every tolerance. See the section "Methods that are not competitive" for the measured numbers behind those choices.

Work-Precision Diagrams (CVODE and lsoda solvers)

Declare solvers.

lsoda and the default (dense direct) CVODE_BDF are not in this suite: they run 700-2000 s and 300-800 s per point respectively, which is 100x the preconditioned-Krylov CVODE variants and by itself made this chunk take 6.5 h of CI time. Both are timed once in the isolation section below.

setups = [
    Dict(:alg=>CVODE_BDF(linear_solver = :LapackDense), :prob_choice => 1),
    Dict(:alg=>CVODE_BDF(linear_solver = :GMRES), :prob_choice => 1),
    Dict(
        :alg=>CVODE_BDF(linear_solver = :GMRES, prec = precilu, psetup = psetupilu, prec_side = 1),
        :prob_choice => 2),
    Dict(:alg=>CVODE_BDF(linear_solver = :KLU), :prob_choice => 3)
];

Plot Work-Precision Diagram.

wp = WorkPrecisionSet(
    [oprob, oprob_sparse, sparsejacprob], abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = [test_sol, test_sol, test_sol], maxiters = Int(1e6), numruns = 10)

names = ["CVODE_BDF (LapackDense)" "CVODE_BDF (GMRES)" "CVODE_BDF (GMRES, iLU)" "CVODE_BDF (KLU, sparse jac)"]
plot(wp; label = names)

Work-Precision Diagrams (various Julia solvers)

Declare solvers (using default linear solver).

setups = [
    Dict(:alg=>QNDF(autodiff = AutoFiniteDiff())),
    Dict(:alg=>FBDF(autodiff = AutoFiniteDiff())),
    Dict(:alg=>NordsieckBDF(autodiff = AutoFiniteDiff())),
    Dict(:alg=>KenCarp4(autodiff = AutoFiniteDiff()))
];

Plot Work-Precision Diagram (using default linear solver).

wp = WorkPrecisionSet(oprob, abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = test_sol, maxiters = Int(1e6), numruns = 10)

names = ["QNDF" "FBDF" "NordsieckBDF" "KenCarp4"]
plot(wp; label = names)

Declare solvers (using GMRES linear solver).

setups = [
    Dict(:alg=>QNDF(linsolve = KrylovJL_GMRES(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>FBDF(linsolve = KrylovJL_GMRES(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>NordsieckBDF(linsolve = KrylovJL_GMRES(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>KenCarp4(linsolve = KrylovJL_GMRES(), autodiff = AutoFiniteDiff()))
];

Plot Work-Precision Diagram (using GMRES linear solver).

wp = WorkPrecisionSet(oprob, abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = test_sol, maxiters = Int(1e6), numruns = 10)

names = ["QNDF (GMRES)" "FBDF (GMRES)" "NordsieckBDF (GMRES)" "KenCarp4 (GMRES)"]
plot(wp; label = names)

Declare solvers (using GMRES linear solver, with pre-conditioner).

setups = [
    Dict(:alg=>QNDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true)),
    Dict(:alg=>FBDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true)),
    Dict(:alg=>NordsieckBDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true)),
    Dict(:alg=>KenCarp4(
        linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true))
];

Plot Work-Precision Diagram (using GMRES linear solver, with pre-conditioner).

wp = WorkPrecisionSet(sparsejacprob, abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = test_sol, maxiters = Int(1e6), numruns = 10)

names = ["QNDF (GMRES, iLU)" "FBDF (GMRES, iLU)" "NordsieckBDF (GMRES, iLU)" "KenCarp4 (GMRES, iLU)"]
plot(wp; label = names)

Declare solvers (using sparse jacobian)

We designate the solvers we wish to use.

setups = [
    Dict(:alg=>QNDF(linsolve = KLUFactorization(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>FBDF(linsolve = KLUFactorization(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>NordsieckBDF(linsolve = KLUFactorization(), autodiff = AutoFiniteDiff())),
    Dict(:alg=>KenCarp4(linsolve = KLUFactorization(), autodiff = AutoFiniteDiff()))
];

Plot Work-Precision Diagram (using sparse jacobian)

Finally, we generate a work-precision diagram for the selection of solvers.

wp = WorkPrecisionSet(sparsejacprob, abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = test_sol, maxiters = Int(1e6), numruns = 10)

names = ["QNDF (KLU, sparse jac)" "FBDF (KLU, sparse jac)" "NordsieckBDF (KLU, sparse jac)" "KenCarp4 (KLU, sparse jac)"]
plot(wp; label = names)

Methods that are not competitive (timed once, capped)

On this 3744-species / 58276-reaction stiff network the dense direct solves and TRBDF2 are one to two orders of magnitude off the pace of the preconditioned Krylov and sparse-KLU methods. Sweeping them across the whole tolerance grid costs multiple hours of CI time per document and dominated the runtime of this benchmark, so they are not folded into the work-precision suites above. Instead each is solved once, at a fixed tolerance, next to a competitive reference, under a hard wall-clock cap so that a pathological method cannot stall the build. A solve that hits the cap is reported as a lower bound.

const _loser_tol = 1e-6
const _loser_cap = 300.0   # seconds of wall clock per isolated solve

loser_labels = String[]
loser_elapsed = Float64[]

# LSODA.jl does not support callbacks, so `lsoda` is the one entry that has to
# run to completion; everything else is stopped by the wall-clock callback.
function _time_loser!(label, prob, alg; cap = true)
    println("--- $label ---")
    tstart = time()
    kw = if cap
        capcb = DiscreteCallback(
            (u, t, integrator) -> time() - tstart > _loser_cap,
            integrator -> terminate!(integrator); save_positions = (false, false))
        (; callback = capcb)
    else
        (;)
    end
    t = @elapsed sol = solve(prob, alg; abstol = _loser_tol, reltol = _loser_tol,
        maxiters = Int(1e6), save_everystep = false, kw...)
    hit_cap = cap && t >= _loser_cap
    @show sol.retcode
    println("elapsed = ", t, " s", hit_cap ? " (hit the $(_loser_cap) s cap)" : "")
    push!(loser_labels, hit_cap ? label * " (>cap)" : label)
    push!(loser_elapsed, t)
    return sol
end

# Competitive reference (preconditioned Krylov CVODE)
_time_loser!("CVODE_BDF GMRES+iLU (reference)", oprob_sparse,
    CVODE_BDF(linear_solver = :GMRES, prec = precilu, psetup = psetupilu, prec_side = 1))

# Dense direct solves
_time_loser!("lsoda", oprob, lsoda(); cap = false)
_time_loser!("CVODE_BDF (dense)", oprob, CVODE_BDF())

# TRBDF2, the slowest of the Julia BDF/SDIRK set in every linear-solver variant
_time_loser!("TRBDF2 (KLU, sparse jac)", sparsejacprob,
    TRBDF2(linsolve = KLUFactorization(), autodiff = AutoFiniteDiff()))

--- CVODE_BDF GMRES+iLU (reference) ---
sol.retcode = SciMLBase.ReturnCode.Success
elapsed = 0.553777169 s
--- lsoda ---
sol.retcode = SciMLBase.ReturnCode.Success
elapsed = 434.461325578 s
--- CVODE_BDF (dense) ---
sol.retcode = SciMLBase.ReturnCode.Terminated
elapsed = 301.325728878 s (hit the 300.0 s cap)
--- TRBDF2 (KLU, sparse jac) ---
sol.retcode = SciMLBase.ReturnCode.Terminated
elapsed = 302.446666322 s (hit the 300.0 s cap)
retcode: Terminated
Interpolation: 1st order linear
t: 2-element Vector{Float64}:
   0.0
 131.561975022357
u: 2-element Vector{Vector{Float64}}:
 [6000.0, 28.0, 400.0, 400.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.
0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
 [5803.3203234164885, 4.739068687067729, 282.72880003358847, 6.623845068532
344, 0.060953963271000076, 0.07847789735070294, 0.0007221696416847904, 122.
35169774837316, 1.5754144587483734, 0.00875606564912216  …  0.1111946524556
3105, 0.0009671077703958772, 0.0199253830317159, 1.0957776382647637, 2.4354
33594355807e-6, 2.1148893266150737e-6, 0.0004904341832156582, 7.15583945158
6584e-6, 0.007212440010882073, 1.360620263436771]

ref_t = loser_elapsed[1]
bar(loser_labels, loser_elapsed ./ ref_t; xrotation = 20, legend = false,
    ylabel = "wall time / (CVODE_BDF GMRES+iLU reference)",
    title = "fceri_gamma2 isolation (tol=$_loser_tol, one solve each)",
    size = (900, 500), left_margin = 5Plots.mm, bottom_margin = 15Plots.mm)

Explicit Work-Precision Diagram

Benchmarks for explicit solvers.

Declare solvers

We designate the solvers we wish to use, this also includes CVODE_Adams.

setups = [
    Dict(:alg=>CVODE_Adams()),
    Dict(:alg=>Tsit5()),
    Dict(:alg=>BS5()),
    Dict(:alg=>VCABM()),
    Dict(:alg=>Vern6()),
    Dict(:alg=>Vern9())
];

Vern7 and Vern8 were dropped from this panel: every explicit method sits in a narrow 40-60 s band on this problem, so the intermediate Verner orders add about 15 minutes of CI time without adding information that Vern6/Vern9 do not already carry. (The previous version of this cell also passed eight setups with only seven names, so the labels were shifted.)

Plot Work-Precision Diagram

wp = WorkPrecisionSet(oprob, abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = test_sol, maxiters = Int(1e6), numruns = 10)

names = ["CVODE_Adams" "Tsit5" "BS5" "VCABM" "Vern6" "Vern9"]
plot(wp; label = names)

Summary of results

Finally, we compute a single diagram comparing the various solvers used.

Declare solvers

We designate the solvers we wish to compare.

setups = [
    Dict(:alg=>CVODE_BDF(linear_solver = :GMRES), :prob_choice => 1),
    Dict(
        :alg=>CVODE_BDF(linear_solver = :GMRES, prec = precilu, psetup = psetupilu, prec_side = 1),
        :prob_choice => 2),
    Dict(
        :alg=>QNDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true),
        :prob_choice => 3),
    Dict(
        :alg=>FBDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true),
        :prob_choice => 3),
    Dict(
        :alg=>NordsieckBDF(linsolve = KrylovJL_GMRES(; precs = incompletelu), autodiff = AutoFiniteDiff(), concrete_jac = true),
        :prob_choice => 3),
    Dict(:alg=>Tsit5())
];

Plot Work-Precision Diagram

For these, we generate a work-precision diagram for the selection of solvers.

wp = WorkPrecisionSet(
    [oprob, oprob_sparse, sparsejacprob], abstols, reltols, setups; error_estimate = :l2,
    saveat = tf/1000.0, appxsol = [test_sol, test_sol, test_sol], maxiters = Int(1e6), numruns = 200)

names = ["CVODE_BDF (GMRES)" "CVODE_BDF (GMRES, iLU)" "QNDF (GMRES, iLU)" "FBDF (GMRES, iLU)" "NordsieckBDF (GMRES, iLU)" "Tsit5"]
colors = [:darkgreen :green :deepskyblue1 :dodgerblue2 :mediumorchid :orchid2]
markershapes = [:rect :octagon :hexagon :rtriangle :diamond :ltriangle]
plot(wp; label = names, left_margin = 10Plots.mm, right_margin = 10Plots.mm,
    xticks = [1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
    yticks = [1e-1, 1e0, 1e1, 1e2], color = colors, markershape = markershapes,
    legendfontsize = 15, tickfontsize = 15, guidefontsize = 15, legend = :topright,
    lw = 20, la = 0.8, markersize = 20, markerstrokealpha = 1.0,
    markerstrokewidth = 1.5, gridalpha = 0.3, gridlinewidth = 7.5, size = (1100, 1000))

Appendix

These benchmarks are a part of the SciMLBenchmarks.jl repository, found at: https://github.com/SciML/SciMLBenchmarks.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.

To locally run this benchmark, do the following commands:

using SciMLBenchmarks
SciMLBenchmarks.weave_file("benchmarks/Bio","fceri_gamma2.jmd")

Computer Information:

Julia Version 1.10.12
Commit d93beab124c (2026-08-15 10:29 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 128 × AMD EPYC 7502 32-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 128 default, 0 interactive, 64 GC (on 128 virtual cores)
Environment:
  JULIA_DEPOT_PATH = /home/crackauc/github-runners/amdci8-1/.julia
  JULIA_NUM_THREADS = auto

Package Information:

Status `~/github-runners/amdci8-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/Bio/Project.toml`
  [47edcb42] ADTypes v1.24.0
  [6e4b80f9] BenchmarkTools v1.8.0
⌃ [479239e8] Catalyst v16.3.0
  [d360d2e6] ChainRulesCore v1.26.1
⌃ [2b5f629d] DiffEqBase v7.18.2
⌃ [f3b72e0c] DiffEqDevTools v3.4.0
  [40713840] IncompleteLU v0.2.1
  [033835bb] JLD2 v0.6.6
⌃ [7f56f5a3] LSODA v1.1.0
⌃ [7ed4a6bd] LinearSolve v5.13.0
⌃ [961ee093] ModelingToolkit v11.39.1
  [54ca160b] ODEInterface v0.5.2
⌅ [09606e27] ODEInterfaceDiffEq v4.1.0
⌃ [1dea7af3] OrdinaryDiffEq v7.7.0
  [89bda076] OrdinaryDiffEqAdamsBashforthMoulton v2.2.0
⌃ [6ad6398a] OrdinaryDiffEqBDF v2.4.4
⌃ [bbf590c4] OrdinaryDiffEqCore v4.15.0
⌃ [becaefa8] OrdinaryDiffEqExtrapolation v2.5.0
⌃ [1344f307] OrdinaryDiffEqLowOrderRK v2.2.3
⌃ [43230ef6] OrdinaryDiffEqRosenbrock v2.7.0
⌃ [2d112036] OrdinaryDiffEqSDIRK v2.9.0
  [358294b1] OrdinaryDiffEqStabilizedRK v2.6.0
⌃ [79d7bb75] OrdinaryDiffEqVerner v2.4.0
  [91a5bcdd] Plots v1.41.7
  [b4db0fb7] ReactionNetworkImporters v1.5.0
  [f2c3362d] RecursiveFactorization v0.2.30
⌃ [31c91b34] SciMLBenchmarks v0.1.3
  [c3572dad] Sundials v6.6.0
⌅ [a759f4b9] TimerOutputs v0.5.29
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`

And the full manifest:

Status `~/github-runners/amdci8-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/Bio/Manifest.toml`
  [47edcb42] ADTypes v1.24.0
  [14f7f29c] AMD v0.5.3
  [6e696c72] AbstractPlutoDingetjes v1.4.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.45
  [79e6a3ab] Adapt v4.7.0
  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.30.0
  [4c555306] ArrayLayouts v1.12.2
⌃ [aae01518] BandedMatrices v1.11.0
  [6e4b80f9] BenchmarkTools v1.8.0
  [e2ed5e7c] Bijections v0.2.2
  [b2a6c25c] BinaryHeaps v1.1.0
⌃ [caf10ac8] BipartiteGraphs v0.1.11
  [d1d4a3ce] BitFlags v0.1.10
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [8e7c35d0] BlockArrays v1.10.0
⌃ [70df07ce] BracketingNonlinearSolve v1.12.5
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.7
⌃ [479239e8] Catalyst v16.3.0
  [d360d2e6] ChainRulesCore v1.26.1
  [0b6fb165] ChunkCodecCore v1.0.2
  [4c0bbee4] ChunkCodecLibZlib v1.1.0
  [55437552] ChunkCodecLibZstd v1.0.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [944b1d66] CodecZlib v0.7.9
  [35d6a980] ColorSchemes v3.31.0
  [3da002f7] ColorTypes v0.12.1
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.1
⌅ [861a8166] Combinatorics v1.0.2
  [38540f10] CommonSolve v0.2.14
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.2.0
  [34da2185] Compat v4.18.1
  [b152e2b5] CompositeTypes v0.1.4
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.8
  [f0e56b4a] ConcurrentUtilities v2.6.0
  [8f4d0f93] Conda v1.10.3
  [187b0558] ConstructionBase v1.6.0
  [d38c429a] Contour v0.6.3
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.19.6
  [e2d170a0] DataValueInterfaces v1.0.0
  [8bb1440f] DelimitedFiles v1.9.1
⌃ [2b5f629d] DiffEqBase v7.18.2
⌃ [459566f4] DiffEqCallbacks v4.19.2
⌃ [f3b72e0c] DiffEqDevTools v3.4.0
⌃ [77a26b50] DiffEqNoiseProcess v5.36.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.16.0
  [a0c0ee7d] DifferentiationInterface v0.7.21
  [8d63f2c5] DispatchDoctor v0.4.28
  [31c24e10] Distributions v0.25.131
  [ffbed154] DocStringExtensions v0.9.5
  [5b8099bc] DomainSets v0.8.1
⌃ [7c1d4256] DynamicPolynomials v0.6.6
  [06fc5a27] DynamicQuantities v1.13.0
  [4e289a0a] EnumX v1.0.7
  [f151be2c] EnzymeCore v0.8.21
  [460bff9d] ExceptionUnwrapping v0.1.11
  [e2ba6199] ExprTools v0.1.11
  [55351af7] ExproniconLite v0.10.14
  [c87230d0] FFMPEG v0.4.5
  [7034ab61] FastBroadcast v1.4.0
  [9aa1b823] FastClosures v0.3.2
  [a4df4552] FastPower v1.5.0
  [5789e2e9] FileIO v1.20.0
  [1a297f60] FillArrays v1.17.0
  [64ca27bc] FindFirstFunctions v3.2.1
  [6a86dc24] FiniteDiff v2.33.0
⌅ [53c48c17] FixedPointNumbers v0.8.6
  [1fa38f19] Format v1.3.7
  [f6369f11] ForwardDiff v1.4.5
  [a85aefff] FunctionMaps v0.1.2
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v1.13.0
  [46192b85] GPUArraysCore v0.2.0
⌃ [28b8d3ca] GR v0.73.26
  [a0844989] Gamma v1.2.0
  [d7ba0133] Git v1.5.0
  [86223c79] Graphs v1.14.0
  [42e2da0e] Grisu v1.0.2
⌅ [cd3eb016] HTTP v1.11.0
  [076d061b] HashArrayMappedTries v0.2.0
⌅ [eafb193a] Highlights v0.5.3
  [3e5b6fbb] HostCPUFeatures v0.1.18
  [34004b35] HypergeometricFunctions v0.3.30
  [7073ff75] IJulia v1.34.4
  [615f187c] IfElse v0.1.1
  [3263718b] ImplicitDiscreteSolve v2.2.0
  [40713840] IncompleteLU v0.2.1
  [d25df0c9] Inflate v0.1.5
  [18e54dd8] IntegerMathUtils v0.1.4
  [8197267c] IntervalSets v0.7.14
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [82899510] IteratorInterfaceExtensions v1.0.0
  [033835bb] JLD2 v0.6.6
  [1019f520] JLFzf v0.1.11
  [692b3bcd] JLLWrappers v1.8.0
⌅ [682c06a0] JSON v0.21.4
  [ae98c720] Jieko v0.2.1
⌃ [ccbc3e58] JumpProcesses v9.29.3
  [ba0b0d4f] Krylov v0.10.9
⌃ [2faa5264] LHLFactorization v2.2.0
⌃ [7f56f5a3] LSODA v1.1.0
  [b964fa9f] LaTeXStrings v1.4.1
  [23fbe1c1] Latexify v0.16.12
  [10f19ff3] LayoutPointers v0.1.17
  [87fe0de2] LineSearch v0.1.16
⌃ [7ed4a6bd] LinearSolve v5.13.0
  [2ab3a3ac] LogExpFunctions v1.0.1
  [e6f89c97] LoggingExtras v1.2.0
  [bdcacae8] LoopVectorization v0.12.174
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [bb5d69b7] MaybeInplace v0.1.8
  [739be429] MbedTLS v1.1.10
  [442fdcdd] Measures v0.3.3
  [e1d29d7a] Missings v1.2.0
⌃ [961ee093] ModelingToolkit v11.39.1
⌃ [7771a370] ModelingToolkitBase v1.68.0
⌃ [6bb917b9] ModelingToolkitTearing v1.20.5
  [2e0e35c7] Moshi v0.3.12
  [46d2c3a1] MuladdMacro v0.2.7
  [102ac46a] MultivariatePolynomials v0.5.19
  [ffc61752] Mustache v1.0.21
  [d8a4904e] MutableArithmetics v1.8.0
  [77ba4419] NaNMath v1.1.4
⌃ [8913a72c] NonlinearSolve v4.28.0
⌃ [be0214bd] NonlinearSolveBase v2.47.0
⌃ [5959db7a] NonlinearSolveFirstOrder v2.4.0
⌃ [9a2c21bd] NonlinearSolveQuasiNewton v1.15.1
⌃ [26075421] NonlinearSolveSpectralMethods v1.8.0
  [54ca160b] ODEInterface v0.5.2
⌅ [09606e27] ODEInterfaceDiffEq v4.1.0
  [6fe1bfb0] OffsetArrays v1.17.0
  [4d8831e6] OpenSSL v1.6.1
⌅ [bac558e1] OrderedCollections v1.8.2
⌃ [1dea7af3] OrdinaryDiffEq v7.7.0
  [89bda076] OrdinaryDiffEqAdamsBashforthMoulton v2.2.0
⌃ [6ad6398a] OrdinaryDiffEqBDF v2.4.4
⌃ [bbf590c4] OrdinaryDiffEqCore v4.15.0
⌃ [50262376] OrdinaryDiffEqDefault v2.5.0
⌃ [4302a76b] OrdinaryDiffEqDifferentiation v3.10.0
⌃ [becaefa8] OrdinaryDiffEqExtrapolation v2.5.0
⌃ [1344f307] OrdinaryDiffEqLowOrderRK v2.2.3
⌃ [127b3ac7] OrdinaryDiffEqNonlinearSolve v2.9.0
⌃ [43230ef6] OrdinaryDiffEqRosenbrock v2.7.0
⌃ [b4bd8bb3] OrdinaryDiffEqRosenbrockTableaus v2.4.1
⌃ [2d112036] OrdinaryDiffEqSDIRK v2.9.0
  [358294b1] OrdinaryDiffEqStabilizedRK v2.6.0
⌃ [b1df2697] OrdinaryDiffEqTsit5 v2.1.3
⌃ [79d7bb75] OrdinaryDiffEqVerner v2.4.0
  [90014a1f] PDMats v0.11.41
⌅ [d96e819e] Parameters v0.12.3
⌅ [69de0a69] Parsers v2.8.7
  [ccf2f8ad] PlotThemes v3.3.0
  [995b91a9] PlotUtils v1.4.4
  [91a5bcdd] Plots v1.41.7
  [e409e4f3] PoissonRandom v0.4.13
  [f517fe37] Polyester v0.7.19
  [1d0040c9] PolyesterWeave v0.2.2
⌃ [d236fae5] PreallocationTools v1.6.0
⌅ [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.5.2
  [27ebfcd6] Primes v0.5.7
  [43287f4e] PtrArrays v1.4.0
  [0c0d3e7f] PureKLU v1.4.1
  [1fd47b50] QuadGK v2.11.3
  [b4db0fb7] ReactionNetworkImporters v1.5.0
  [988b38a3] ReadOnlyArrays v0.2.0
  [795d4caa] ReadOnlyDicts v1.0.1
  [3cdcf5f2] RecipesBase v1.3.4
  [01d81517] RecipesPipeline v0.6.12
⌃ [731186ca] RecursiveArrayTools v4.5.0
  [f2c3362d] RecursiveFactorization v0.2.30
  [189a3867] Reexport v1.2.2
  [05181044] RelocatableFolders v1.0.1
  [ae029012] Requires v1.3.1
  [ae5879a3] ResettableStacks v1.4.0
  [9fe22ead] RespecializeParams v1.3.0
  [79098fc4] Rmath v0.9.0
  [47965b36] RootedTrees v2.27.0
  [f2b01f46] Roots v3.0.7
  [7e49a35a] RuntimeGeneratedFunctions v0.5.25
⌃ [9dfe8606] SCCNonlinearSolve v1.15.0
  [94e857df] SIMDTypes v0.1.0
  [476501e8] SLEEFPirates v0.6.46
⌃ [0bca4576] SciMLBase v3.49.2
⌃ [31c91b34] SciMLBenchmarks v0.1.3
⌃ [19f34311] SciMLJacobianOperators v0.1.17
  [a6db7da4] SciMLLogging v2.1.0
⌃ [c0aeaf25] SciMLOperators v1.29.0
  [431bcebd] SciMLPublic v1.3.0
⌃ [53ae85a6] SciMLStructures v1.10.4
  [7e506255] ScopedValues v1.6.2
  [6c6a2e73] Scratch v1.3.0
  [efcf1570] Setfield v1.1.2
  [992d4aef] Showoff v1.0.3
  [777ac1f9] SimpleBufferStream v1.2.0
⌃ [727e6d20] SimpleNonlinearSolve v2.14.0
  [699a6c99] SimpleTraits v0.9.6
  [a2af1166] SortingAlgorithms v1.2.3
  [bd59d7e1] SparseBandedMatrices v1.4.0
  [a57abbd0] SparseColumnPivotedQR v2.1.7
  [0a514795] SparseMatrixColorings v0.4.27
  [276daf66] SpecialFunctions v2.9.0
  [860ef19b] StableRNGs v1.0.4
  [0c0c59c1] StarAlgebras v0.3.0
⌃ [64909d44] StateSelection v1.11.0
  [aedffcd0] Static v1.4.6
  [0d7ed370] StaticArrayInterface v1.10.0
  [90137ffa] StaticArrays v1.9.19
  [1e83bf80] StaticArraysCore v1.4.4
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.13
  [4c63d2b9] StatsFuns v2.2.1
  [7792a7ef] StrideArraysCore v0.5.9
  [69024149] StringEncodings v0.3.7
  [09ab397b] StructArrays v0.7.3
  [c3572dad] Sundials v6.6.0
  [2efcf032] SymbolicIndexingInterface v0.3.55
  [19f23fe9] SymbolicLimits v1.2.0
⌅ [d1185830] SymbolicUtils v4.45.0
⌃ [0c5d862f] Symbolics v7.36.0
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.14.0
  [ed4db957] TaskLocalValues v0.1.3
  [62fd8b95] TensorCore v0.1.1
  [8ea1fca8] TermInterface v2.0.0
  [1c621080] TestItems v1.1.0
  [8290d209] ThreadingUtilities v0.5.6
⌅ [a759f4b9] TimerOutputs v0.5.29
  [3bb67fe8] TranscodingStreams v0.11.3
  [d5829a12] TriangularSolve v0.2.6
  [410a4b4d] Tricks v0.1.13
  [781d530d] TruncatedStacktraces v1.4.0
  [5c2747f8] URIs v1.7.0
  [3a884ed6] UnPack v1.0.2
  [1cfade01] UnicodeFun v0.4.1
  [41fe7b60] Unzip v0.2.0
  [3d5dd08c] VectorizationBase v0.21.74
  [33b4df10] VectorizedRNG v0.2.26
  [81def892] VersionParsing v1.3.0
  [d30d5f5c] WeakCacheSets v0.1.0
  [44d3d7a6] Weave v0.10.12
  [ddb6d928] YAML v0.4.16
  [c2297ded] ZMQ v1.5.1
  [6e34b625] Bzip2_jll v1.0.9+0
  [83423d85] Cairo_jll v1.18.7+0
  [ee1fde0b] Dbus_jll v1.16.2+0
  [2702e6a9] EpollShim_jll v0.0.20230411+1
  [2e619515] Expat_jll v2.8.3+0
⌅ [b22a6f82] FFMPEG_jll v8.1.2+0
  [a3f928ae] Fontconfig_jll v2.17.1+0
  [d7e528f0] FreeType2_jll v2.14.3+1
  [559328eb] FriBidi_jll v1.0.17+0
⌃ [0656b61e] GLFW_jll v3.4.1+1
⌅ [d2c73de3] GR_jll v0.73.26+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
  [61579ee1] Ghostscript_jll v9.55.1+0
  [020c3dae] Git_LFS_jll v3.7.1+0
  [f8c6e375] Git_jll v2.55.0+0
  [7746bdde] Glib_jll v2.88.3+0
  [3b182d85] Graphite2_jll v1.3.16+0
⌅ [2e76f6c2] HarfBuzz_jll v8.5.1+0
  [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
  [aacddb02] JpegTurbo_jll v3.2.0+1
  [c1c5ebd0] LAME_jll v3.100.3+0
  [88015f11] LERC_jll v4.1.0+0
  [1d63c593] LLVMOpenMP_jll v22.1.7+0
  [aae0fff6] LSODA_jll v0.1.2+0
⌅ [e9f186c6] Libffi_jll v3.4.7+0
  [7e76a0d4] Libglvnd_jll v1.7.1+1
  [94ce4f54] Libiconv_jll v1.18.0+0
  [4b2f31a3] Libmount_jll v2.42.0+0
  [89763e89] Libtiff_jll v4.7.3+0
  [38a345b3] Libuuid_jll v2.42.0+0
  [856f044c] MKL_jll v2025.2.0+0
  [c771fb93] ODEInterface_jll v0.0.2+0
  [e7412a2a] Ogg_jll v1.3.6+0
⌅ [656ef2d0] OpenBLAS32_jll v0.3.24+0
  [9bd350c2] OpenSSH_jll v10.5.1+0
⌃ [458c3c95] OpenSSL_jll v3.5.7+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [91d4177d] Opus_jll v1.6.1+0
⌃ [36c8627f] Pango_jll v1.58.0+0
  [30392449] Pixman_jll v0.46.4+0
  [c0090381] Qt6Base_jll v6.10.2+2
  [629bc702] Qt6Declarative_jll v6.10.2+2
  [ce943373] Qt6ShaderTools_jll v6.10.2+1
  [6de9746b] Qt6Svg_jll v6.10.2+0
  [e99dba38] Qt6Wayland_jll v6.10.2+1
  [f50d1b31] Rmath_jll v0.5.2+0
⌅ [ca45d3f4] SuiteSparse32_jll v5.10.1+0
  [fb77eaff] Sundials_jll v7.5.0+0
  [a44049a8] Vulkan_Loader_jll v1.3.243+0
  [a2964d1f] Wayland_jll v1.24.0+0
  [ffd25f8a] XZ_jll v5.8.3+0
  [f67eecfb] Xorg_libICE_jll v1.1.2+0
  [c834827a] Xorg_libSM_jll v1.2.6+0
  [4f6342f7] Xorg_libX11_jll v1.8.13+0
  [0c0b7dd1] Xorg_libXau_jll v1.0.13+0
  [935fb764] Xorg_libXcursor_jll v1.2.4+0
  [a3789734] Xorg_libXdmcp_jll v1.1.6+0
  [1082639a] Xorg_libXext_jll v1.3.8+0
  [d091e8ba] Xorg_libXfixes_jll v6.0.2+0
  [a51aa0fd] Xorg_libXi_jll v1.8.4+0
  [d1454406] Xorg_libXinerama_jll v1.1.7+0
  [ec84b674] Xorg_libXrandr_jll v1.5.6+0
  [ea2f1a96] Xorg_libXrender_jll v0.9.12+0
  [a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.1+0
  [cc61e674] Xorg_libxkbfile_jll v1.2.0+0
  [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.6+0
  [12413925] Xorg_xcb_util_image_jll v0.4.1+0
  [2def613f] Xorg_xcb_util_jll v0.4.1+0
  [975044d2] Xorg_xcb_util_keysyms_jll v0.4.1+0
  [0d47668e] Xorg_xcb_util_renderutil_jll v0.3.10+0
  [c22f9ab0] Xorg_xcb_util_wm_jll v0.4.2+0
  [35661453] Xorg_xkbcomp_jll v1.4.7+0
  [33bec58e] Xorg_xkeyboard_config_jll v2.47.0+2
  [c5fb5394] Xorg_xtrans_jll v1.6.0+0
  [8f1865be] ZeroMQ_jll v4.3.6+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [35ca27e7] eudev_jll v3.2.14+0
⌅ [214eeab7] fzf_jll v0.61.1+0
  [a4ae2306] libaom_jll v3.14.1+0
⌃ [0ac62f75] libass_jll v0.17.4+0
  [1183f4f0] libdecor_jll v0.2.2+0
  [8e53e030] libdrm_jll v2.4.134+0
  [2db6ffa8] libevdev_jll v1.13.4+0
  [f638f0a6] libfdk_aac_jll v2.0.4+0
  [36db933b] libinput_jll v1.28.1+0
  [b53b4c65] libpng_jll v1.6.58+0
  [a9144af2] libsodium_jll v1.0.21+0
  [9a156e7d] libva_jll v2.23.0+0
  [f27f6e37] libvorbis_jll v1.3.8+0
  [009596ad] mtdev_jll v1.1.7+0
  [1317d2d5] oneTBB_jll v2022.3.0+0
⌅ [1270edf5] x264_jll v10164.0.1+0
  [dfaa095f] x265_jll v4.1.0+0
  [d8fb68d0] xkbcommon_jll v1.13.0+0
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [9abbd945] Profile
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.1010+0
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [4536629a] OpenBLAS_jll v0.3.23+5
  [05823500] OpenLibm_jll v0.8.5+0
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.6.1+0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`