# Parallel and in-process compilation of individuals for genetic programming on GPU

## TL;DR

This paper explores methods to accelerate in-process compilation of individuals in genetic programming on GPU, using parallelization and shared memory to reduce overhead, and provides an open-source framework for experiments.

## Contribution

It introduces a multiprocess parallelization approach for in-process compilation in GPU genetic programming and offers an open-source C# framework for flexible experimentation.

## Key findings

- Parallelized compilation reduces overhead significantly.
- In-process compilation can be effectively accelerated with shared memory.
- Open-source framework facilitates easy development of GPU-based genetic programming experiments.

## Abstract

Three approaches to implement genetic programming on GPU hardware are compilation, interpretation and direct generation of machine code. The compiled approach is known to have a prohibitive overhead compared to other two. This paper investigates methods to accelerate compilation of individuals for genetic programming on GPU hardware. We apply in-process compilation to minimize the compilation overhead at each generation; and we investigate ways to parallelize in-process compilation. In-process compilation doesn't lend itself to trivial parallelization with threads; we propose a multiprocess parallelization using memory sharing and operating systems interprocess communication primitives. With parallelized compilation we achieve further reductions on compilation overhead. Another contribution of this work is the code framework we built in C# for the experiments. The framework makes it possible to build arbitrary grammatical genetic programming experiments that run on GPU with minimal extra coding effort, and is available as open source.