Implementing distributed {\lambda}-calculus interpreter

TL;DR

This paper presents a foundational implementation of a distributed lambda calculus interpreter using the actor model, including a parser and Krivine Machine, with initial performance improvements and discussion of future enhancements.

Contribution

It introduces a basic, extensible distributed lambda calculus interpreter with a focus on particular parallelizable patterns and a prototype implementation.

Findings

Achieved some computation speed improvements.

Identified challenges in creating an efficient distributed version.

Provided a basis for future enhancements like concurrency and auto-parallelization.

Abstract

This paper describes how one can implement distributed {\lambda}-calculus interpreter from scratch. At first, we describe how to implement a monadic parser, than the Krivine Machine is introduced for the interpretation part and as for distribution, the actor model is used. In this work we are not providing general solution for parallelism, but we consider particular patterns, which always can be parallelized. As a result, the basic extensible implementation of call-by-name distributed machine is introduced and prototype is presented. We achieved computation speed improvement in some cases, but efficient distributed version is not achieved, problems are discussed in evaluation section. This work provides a foundation for further research, completing the implementation it is possible to add concurrency for non-determinism, improve the interpreter using call-by-need semantic or study optimal auto parallelization to generalize what could be done efficiently in parallel.