How to Create and Run Multiple Solver Instances in Parallel Using Drake

Discover the best practices for running multiple solver instances in parallel. Learn how to efficiently utilize threads and processes, ensuring smooth execution without errors.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Create and Run Multiple Solver Instances in Parallel

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Creating and Running Multiple Solver Instances in Parallel

Working with solvers in programming can sometimes be challenging, especially when trying to optimize performance by running multiple solvers simultaneously. If you are using the Drake library and want to run multiple solver instances in parallel, you may run into some roadblocks. This guide will guide you through the problem and provide a clear solution so you can execute your tasks efficiently.

The Problem

You may have encountered issues while attempting to run multiple solver instances in separate threads or processes in Drake, which can lead to various errors, such as:

Terminate called recursively: This happens when multiple threads access the same solver instance, resulting in conflicting operations.

Compiling issues: When trying to copy or move SolverInterface instances, you might face restrictions since they are not designed to be copied or moved.

These complications can arise when using a simple for-loop with pthread to create threads for running solvers. It’s essential to implement an approach that allows for distinct instances of solvers dedicated to each thread.

The Solution

Below are a few steps to create and run multiple solvers in parallel without encountering the common pitfalls mentioned earlier.

1. Creating Distinct Instances

To efficiently handle multiple threads, you must ensure that each thread operates on its unique solver instance. You can achieve this with the following approach:

Calling MakeSolver: When you call drake::solvers::MakeSolver(solver_id) multiple times, each invocation will return a unique solver instance. Here’s an example of how to implement this:

[[See Video to Reveal this Text or Code Snippet]]

By doing this, no solver instances are shared amongst threads, minimizing the risk of errors.

2. Using std::thread over pthread

While the pthread API can be utilized, it’s often deemed outdated. Instead, consider using std::thread from the C++ standard library. It provides a cleaner interface and easier management of threads:

[[See Video to Reveal this Text or Code Snippet]]

3. Handling Exceptions

When working within threads, it is essential to handle exceptions properly. Uncaught exceptions can cause threads to terminate unexpectedly. Here's how you can wrap your thread logic with a try-catch block:

[[See Video to Reveal this Text or Code Snippet]]

4. Conclusion

Running multiple solver instances in parallel using Drake is feasible and efficient when done correctly. By ensuring each thread has its unique solver instance, utilizing the std::thread API, and managing exceptions, you can avoid common pitfalls.

Now, you're ready to enhance the performance of your applications by effectively leveraging multiple solver instances in your projects!