**DISCLAIMER: Please note that this project is in early stages of development. It is very much a work in progress, it may cause your software to crash, and current performance results are likely to leave you feeling underwhelmed.**
- [Simple and Effective Type Check Removal through Lazy Basic Block Versioning](https://arxiv.org/pdf/1411.0352.pdf) ([ECOOP 2015 talk](https://www.youtube.com/watch?v=S-aHBuoiYE0))
- [Interprocedural Type Specialization of JavaScript Programs Without Type Analysis](https://drops.dagstuhl.de/opus/volltexte/2016/6101/pdf/LIPIcs-ECOOP-2016-7.pdf) ([ECOOP 2016 talk](https://www.youtube.com/watch?v=sRNBY7Ss97A))
The YJIT `ruby` binary can be built with either GCC or Clang. We recommend enabling debug symbols so that assertions are enabled during development as this makes debugging easier. Enabling debug mode will also make it possible for you to disassemble code generated by YJIT, and get access to stat counters. More detailed build instructions are provided in the [Ruby README](https://github.com/ruby/ruby#how-to-compile-and-install).
To support disassembly of the generated code, `libcapstone` is also required (`brew install capstone` on MacOS, `sudo apt-get install -y libcapstone-dev` on Ubuntu/Debian).
The machine code generated for a given method can be printed by adding `puts YJIT.disasm(method(:method_name))` to a Ruby script. Note that no code will be generated if the method is not compiled.
We have collected a set of benchmarks and implemented a simple benchmarking harness in the [yjit-bench](https://github.com/Shopify/yjit-bench) repository. This benchmarking harness is designed to disable CPU frequency scaling, set process affinity and disable address space randomization so that the variance between benchmarking runs will be as small as possible. Please kindly note that we are at an early stage in this project.
This section contains tips on writing Ruby code that will run as fast as possible on YJIT. Some of this advice is based on current limitations of YJIT, while other advice is broadly applicable. It probably won't be practical to apply these tips everywhere in your codebase, but you can profile your code using a tool such as [stackprof](https://github.com/tmm1/stackprof) and refactor the specific methods that make up the largest fractions of the execution time.
You can also compile YJIT in debug mode and use the `--yjit-stats` command-line option to see which bytecodes cause YJIT to exit, and refactor your code to avoid using these instructions in the hottest methods of your code.
We welcome open source contributors. You should feel free to open new issues to report bugs or just to ask questions.
Suggestions on how to make this readme file more helpful for new contributors are most welcome.
Bug fixes and bug reports are very valuable to us. If you find bugs in YJIT, it's very possible be that nobody has reported this bug before,
or that we don't have a good reproduction for it, so please open an issue and provide some information about your configuration and a description of how you
encountered the problem. If you are able to produce a small reproduction to help us track down the bug, that is very much appreciated as well.
If you would like to contribute a large patch to YJIT, we suggest opening an issue or a discussion on this repository so that
we can have an active discussion. A common problem is that sometimes people submit large pull requests to open source projects
without prior communication, and we have to reject them because the work they implemented does not fit within the design of the
project. We want to save you time and frustration, so please reach out and we can have a productive discussion as to how
you can contribute things we will want to merge into YJIT.