# Functional Design of Computation Graph

## TL;DR

This paper discusses the implementation and features of computation graph support in Owl, an OCaml-based numerical library, highlighting its role in optimizing performance in scientific computing.

## Contribution

It introduces the design and implementation of computation graph support in Owl, showcasing its unique features and efficiency improvements for scientific computing in OCaml.

## Key findings

- Enhanced performance in numerical computations
- Flexible control flow management in Owl
- Comparison with other libraries shows competitive efficiency

## Abstract

Representing the control flow of a computer program as a computation graph can bring many benefits in a broad variety of domains where performance is critical. This technique is a core component of most major numerical libraries (TensorFlow, PyTorch, Theano, MXNet,...) and is successfully used to speed up and optimise many computationally-intensive tasks. However, different design choices in each of these libraries lead to noticeable differences in efficiency and in the way an end user writes efficient code. In this report, we detail the implementation and features of the computation graph support in OCaml's numerical library Owl, a recent entry in the world of scientific computing.