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Developer and advanced user guide

When creating the CMake build, make sure to add the -DCMAKE_EXPORT_COMPILE_COMMANDS=1 flag. See its documentation here.

A template project for using aligator with CMake and C++ can be found in the aligator-cmake-example-project repository.

Creating a Python extension module

When aligator is installed, the CMake configuration file (aligatorConfig.cmake) provides a CMake function to help users easily create a Python extension module. Users can write an extension module in C++ for performance reasons when providing e.g. custom constraints, cost functions, dynamics, and so on.

The CMake function is called as follows:

aligator_create_python_extension(<name> [WITH_SOABI] <sources...>)

This will create a CMake MODULE target named <name> on which the user can set properties and add an install directive.

An usage example can be found in this repo.

Debugging

Debugging a C++ executable

This project builds some C++ examples and tests. Debugging them is fairly straightforward using GDB:

gdb path/to/executable

with the appropriate command line arguments. Examples will appear in the binaries of build/examples. Make sure to look at GDB's documentation.

If you want to catch std::exception instances thrown, enter the following command once in GDB:

(gdb) catch throw std::exception

Debugging a Python example or test

In order for debug symbols to be loaded and important variables not being optimized out, you will want to compile in DEBUG mode.

Then, you can run the module under gdb using

gdb --args python example/file.py

If you want to look at Eigen types such as vectors and matrices, you should look into the eigengdb plugin for GDB.

Hybrid debugging with Visual Studio Code

[TODO]

Using aligator's parallelization features

The SolverProxDDP solver is able to leverage multicore CPU architectures.

Inside your code

Before calling the solver make sure to enable parallelization as follows:

In Python:

solver.rollout_type = aligator.ROLLOUT_LINEAR
solver.linear_solver_choice = aligator.LQ_SOLVER_PARALLEL
solver.setNumThreads(num_threads)

And in C++:

std::size_t num_threads = 4ul;  // for example
solver.rollout_type = aligator::RolloutType::LINEAR;
solver.linear_solver_choice = aligator::LQSolverChoice::PARALLEL;
solver.setNumThreads(num_threads);

Shell setup for CPU core optimization

Aligator uses OpenMP for parallelization which is setup using environment variables in your shell. The settings are local to your shell.

Visualization

Printing OpenMP parameters at launch:

export OMP_DISPLAY_ENV=VERBOSE

Print when a thread is launched and with which affinity (CPU thread(s) on where it will try to run):

export OMP_DISPLAY_AFFINITY=TRUE

Core and thread assignment

OpenMP operates with places which define a CPU thread or core reserved for a thread. Places can be a CPU thread or an entire CPU core (which can have one thread, or multiple with hyperthreading).

Assigning places with CPU threads:
export OMP_PLACES ="threads(n)" # Threads will run on the first nth CPU threads, with one thread per CPU thread.

or

export OMP_PLACES="{0},{1},{2}" # Threads will run on CPU threads 0, 1 ,2
Assigning places with CPU cores:

Threads will run on the first nth CPU cores, with one thread per core, even if the core has multiple threads

export OMP_PLACES="cores(n)"

For more info on places see here.

Using only performance cores (Intel performance hybrid architectures)

Some modern CPUs have a mix of performance (P) and efficiency (E) cores. The E-cores are often slower, hence we should have OpenMP schedule threads on P-cores only.

Get your CPU model with

lscpu | grep -i "Model Name"

Get CPU core info with:

lscpu -e

# with an i7-13800H
CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE    MAXMHZ   MINMHZ      MHZ
  0    0      0    0 0:0:0:0          yes 5000.0000 400.0000  400.000
  1    0      0    0 0:0:0:0          yes 5000.0000 400.0000  400.000
  2    0      0    1 4:4:1:0          yes 5000.0000 400.0000  400.000
  3    0      0    1 4:4:1:0          yes 5000.0000 400.0000  400.000
  4    0      0    2 8:8:2:0          yes 5200.0000 400.0000  400.000
  5    0      0    2 8:8:2:0          yes 5200.0000 400.0000 5176.303
  6    0      0    3 12:12:3:0        yes 5200.0000 400.0000 1482.743
  7    0      0    3 12:12:3:0        yes 5200.0000 400.0000  400.000
  8    0      0    4 16:16:4:0        yes 5000.0000 400.0000 3485.561
  9    0      0    4 16:16:4:0        yes 5000.0000 400.0000  721.684
 10    0      0    5 20:20:5:0        yes 5000.0000 400.0000 1641.311
 11    0      0    5 20:20:5:0        yes 5000.0000 400.0000  400.000
 12    0      0    6 24:24:6:0        yes 4000.0000 400.0000  400.000
 13    0      0    7 25:25:6:0        yes 4000.0000 400.0000 2949.734
 14    0      0    8 26:26:6:0        yes 4000.0000 400.0000 2554.695
 15    0      0    9 27:27:6:0        yes 4000.0000 400.0000 3588.623
 16    0      0   10 28:28:7:0        yes 4000.0000 400.0000  400.000
 17    0      0   11 29:29:7:0        yes 4000.0000 400.0000  400.000
 18    0      0   12 30:30:7:0        yes 4000.0000 400.0000  400.000
 19    0      0   13 31:31:7:0        yes 4000.0000 400.0000 3610.068

A little digging on the internet tells us that this CPU has 6 performance cores and 8 efficiency cores for a total of 20 threads. We see higher frequencies in core 0 to 5: these are the performance cores. To use only performance cores on this CPU you would set:

export OMP_PLACES="cores(6)"
# or
export OMP_PLACES="threads(12)"

Important

Put your PC in performance mode (usually found in the power settings).

Profiling

We use google benchmark to define C++ benchmarks which are able to aggregate data from runs, and Flame Graphs to produce a breakdown of the various function calls and their importance as a proportion of the call stack.

If you have the Rust toolchain and cargo installed, we suggest you install cargo-flamegraph. Then, you can create a flame graph with the following command:

flamegraph -o my_flamegraph.svg -- ./build/examples/example-croc-talos-arm

Here's Crocoddyl's flame graph: croc-talos-arm Here's for aligator::SolverFDDP: prox-talos-arm