CTBenchmarks

CTBenchmarks.jl is a comprehensive benchmarking suite for optimal control problems, designed to evaluate and compare the performance of different solvers and modelling approaches within the control-toolbox ecosystem.

This package provides:

🚀 Pre-configured benchmark suites for quick performance evaluation
📊 Automated result collection and analysis
🔧 Flexible API for creating custom benchmarks
📈 Detailed performance metrics including timing, memory usage, and solver statistics

Installation

Development Package

CTBenchmarks.jl is not yet registered in the Julia General Registry. You must clone the repository to use it.

To install CTBenchmarks.jl, clone the repository and activate the project:

using Pkg

# Clone the repository
Pkg.develop(url="https://github.com/control-toolbox/CTBenchmarks.jl")

# Or clone manually and activate
# git clone https://github.com/control-toolbox/CTBenchmarks.jl.git
# cd CTBenchmarks.jl
# julia --project=.

Once installed, load the package:

using CTBenchmarks

Quick Start

Running Pre-configured Benchmarks

CTBenchmarks provides two pre-configured benchmark suites:

Minimal Benchmark (Fast)

Run a quick benchmark on a single problem to test your setup:

CTBenchmarks.run(:minimal)

This runs the :beam problem with:

Grid size: 100
Discretisation: trapeze
Solvers: Ipopt and MadNLP
Models: JuMP, adnlp, exa, exa_gpu

Complete Benchmark (Comprehensive)

Run the full benchmark suite across all problems:

CTBenchmarks.run(:complete)

This runs 14 optimal control problems with:

Grid sizes: 100, 200, 500
Discretisations: trapeze, midpoint
Solvers: Ipopt and MadNLP
Models: JuMP, adnlp, exa, exa_gpu

Solver Output

By default, solver output is suppressed. To see detailed solver traces, use:

CTBenchmarks.run(:minimal; print_trace=true)

Saving Results

To save benchmark results to a directory:

CTBenchmarks.run(:minimal; outpath="my_results")

This creates a directory containing:

data.json - Benchmark results in JSON format
Project.toml - Package dependencies
Manifest.toml - Complete dependency tree

Creating Custom Benchmarks

For more control over your benchmarks, use the CTBenchmarks.benchmark function directly:

CTBenchmarks.benchmark(;
    outpath = "custom_benchmark",
    problems = [:beam, :chain, :robot],
    solver_models = [
        :ipopt => [:JuMP, :adnlp, :exa],
        :madnlp => [:exa, :exa_gpu]
    ],
    grid_sizes = [200, 500, 1000],
    disc_methods = [:trapeze],
    tol = 1e-6,
    ipopt_mu_strategy = "adaptive",
    print_trace = false,
    max_iter = 1000,
    max_wall_time = 500.0
)

Available Problems

CTBenchmarks includes 14 optimal control problems from OptimalControlProblems.jl:

:beam - Beam control problem
:chain - Chain of masses
:double_oscillator - Double oscillator
:ducted_fan - Ducted fan control
:electric_vehicle - Electric vehicle optimisation
:glider - Glider trajectory
:insurance - Insurance problem
:jackson - Jackson problem
:robbins - Robbins problem
:robot - Robot arm control
:rocket - Rocket trajectory
:space_shuttle - Space shuttle re-entry
:steering - Steering control
:vanderpol - Van der Pol oscillator

Solver and Model Combinations

Supported Solvers:

:ipopt - Interior Point Optimizer
:madnlp - Matrix-free Augmented Lagrangian NLP solver

Supported Models:

:JuMP - JuMP modelling framework
:adnlp - Automatic differentiation NLP models
:exa - ExaModels (CPU)
:exa_gpu - ExaModels (GPU acceleration)

Benchmark Parameters

grid_sizes: Number of discretisation points (e.g., [100, 200, 500])
disc_methods: Discretisation schemes (:trapeze, :midpoint)
tol: Solver tolerance (default: 1e-6)
max_iter: Maximum solver iterations (default: 1000)
max_wall_time: Maximum wall time in seconds (default: 500.0)

Benchmark Results in This Documentation

This documentation includes pre-computed benchmark results from continuous integration runs on different platforms:

Ubuntu Latest - Standard CPU benchmarks on GitHub Actions runners
Moonshot - GPU-accelerated benchmarks on dedicated hardware

These results provide reference performance data and demonstrate the capabilities of different solver and model combinations. You can explore them in the Core Benchmark section.

Each benchmark result page includes:

📊 Performance metrics (time, memory, iterations)
🖥️ Environment information (Julia version, OS, hardware)
📜 Reproducible benchmark scripts
📦 Complete dependency information

Understanding Benchmark Output

When you run a benchmark, you'll see output similar to:

Benchmarks results:

┌─ problem: beam
│
├──┬ solver: ipopt, disc_method: trapeze
│  │
│  │  N : 100
│  │  ✓ | JuMP    | time:    1.234 s | iters: 42    | obj: 1.234567e+00 | CPU:    2.5 MiB
│  │  ✓ | adnlp   | time:    0.987 s | iters: 42    | obj: 1.234567e+00 | CPU:    2.1 MiB
│  │  ✓ | exa     | time:    0.765 s | iters: 42    | obj: 1.234567e+00 | CPU:    1.8 MiB
│  └─
└─

Legend:

✓ / ✗ - Success or failure indicator
Model - Modelling framework (JuMP, ADNLPModels, ExaModels)
time - Total solve time
iters - Number of solver iterations
obj - Objective function value
Memory - CPU memory usage (GPU memory shown separately for GPU models)

Documentation build environment

You can download the exact environment used to build this documentation:

📦 Project.toml - Package dependencies
📋 Manifest.toml - Complete dependency tree with versions

ℹ️ Version info

Julia Version 1.12.1
Commit ba1e628ee49 (2025-10-17 13:02 UTC)
Build Info:
  Official https://julialang.org release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-18.1.7 (ORCJIT, znver3)
  GC: Built with stock GC
Threads: 1 default, 1 interactive, 1 GC (on 4 virtual cores)
Environment:
  JULIA_PKG_SERVER_REGISTRY_PREFERENCE = eager

📦 Package status

Status `~/work/CTBenchmarks.jl/CTBenchmarks.jl/docs/Project.toml`
  [db1dffaa] CTBenchmarks v0.2.3 `~/work/CTBenchmarks.jl/CTBenchmarks.jl`
  [a93c6f00] DataFrames v1.8.1
  [e30172f5] Documenter v1.15.0
⌅ [682c06a0] JSON v0.21.4
  [de0858da] Printf v1.11.0
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`

📚 Complete manifest

Status `~/work/CTBenchmarks.jl/CTBenchmarks.jl/docs/Manifest.toml`
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Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`