Lexa is a programming language supporting lexically scoped effect handlers. These effect handlers are a powerful means to express and manage complex control flow. They allow for strong local-reasoning principles, too.

This repository contains an implementation of Lexa. The compiler translates high-level, modular algebraic effects to low-level, swift stack switching. The design and implementation of the Lexa compiler are described in the following paper:

Cong Ma, Zhaoyi Ge, Edward Lee, Yizhou Zhang.
Lexical Effect Handlers, Directly.
Proceedings of the ACM on Programming Languages (PACMPL), Volume 8, Issue OOPSLA2, October 2024.

• Supported platform: x86-64.
• 32GB of RAM recommended for building.

docker run -it hflsmax/lexa-lang:OOPSLA24

1. Install Nix: Follow the instructions to install Nix on your system.
2. Clone the repository: Clone the Lexa repo to your local machine.
3. Build the development environment: Run nix develop in the repository root. This could take up to an hour, or a few seconds if you allow Nix to use the pre-built cache.
4. Build the project: Run dune build to build the compiler.

To run the Lexa compiler, use the following commands:

./lexa <source_file>
./a.out

See ./casestudies and ./test for exmaple Lexa programs.

1. Follow the instructions above to set up the project.
2. Figure 2: Run cd scripts; python ./plots.py --tick-plot --plot-only ./final_plotting_runtimes2.csv. The plot will be saved in ./scaling_plots/two_scaling_plot.pdf. To plot using fresh data, run python ./plots.py --tick-plot.
3. Table 1: Run cd scripts; python bench.py. The result will be saved in ./runtimes.csv.
4. Figure 16: Run cd scripts; python ./plots.py --plot-only ./final_plotting_runtimes.csv. The plot will be saved in ./scaling_plots/scaling_plot.pdf. To plot using fresh data, run python ./plots.py.
5. Test formalized translation: Run cd src/formalized_translation; racket artifact.rkt.

Please reach out to Cong Ma ([email protected]) for any questions. We welcome contributions to the project.