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Co-dfns Compiler

The Co-dfns project aims to provide a high-performance, high-reliability compiler for a parallel extension of the Dyalog dfns programming language. The dfns language is a functionally oriented, lexically scoped dialect of APL. The Co-dfns language extends the dfns language to include explicit task parallelism with implicit structures for synchronization and determinism. The language is designed to enable rigorous formal analysis of programs to aid in compiler optimization and programmer productivity, as well as in the general reliability of the code itself.

Our mission is to deliver scalable APL programming to information and domain experts across many fields, expanding the scope and capabilities of what you can effectively accomplish with APL.

Getting Help

Documentation on Co-dfns can be found in the docs/ folder.

APL and Co-dfns can be a little different at first. I encourage you to contact me ( for any questions you may have regarding either APL or Co-dfns.

Additionally, if you have general Dyalog APL inquiries, please do not hesitate to email Dyalog's support email:

Contributing and Helping

We are seeking to create an open funding model for Co-dfns research through user and patron contributions. You can support the project by contributing code, feedback, benchmarks, and so forth, but you can also directly support the Co-dfns project by funding the author:

Related Projects

There are a number of related initiatives that are based on the Co-dfns technology:

  • Mystika: a high-level, high-performance cryptographic stack
  • apixlib: programmable, easy to use image processing


The following publications are either directly related to Co-dfns or talk about Co-dfns in their presentations.

U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning. ARRAY 2023.

Implementing the Convolutional Neural Network U-Net in APL, Dyalog User Meeting 2022

Text Processing in APL, Dyalog User Meeting 2022

Co-dfns Report, Dyalog User Meeting 2022

DSLs, Architecture, & Structural Design in APL, 3 ways by Aaron W Hsu #FnConf 2022

A Taste of GPU Compute, mentions Co-dfns

Modern APL in the Real World: Theory, Practice, Case Studies - λC 20 Global Edition

Programming Obesity: A Code Health Epidemic - FnConf 2019

Program Obesity: A Code Health Epidemic - λC 2019 Unconference

A Data Parallel Compiler Hosted on the GPU

Pragmatic Array Oriented Functional Programming @ JIO Talks

Live Reading/Writing Co-dfns, The Way of APL

Parallel-by-construction Tree Manipulation with APL - λC 2018 (Part 1 2)

Does APL Need a Type System? (FnConf '18)

Array-oriented Functional Programming

Tree Manipulation Workshop and Dyalog '18 Talk

User Command for Co-dfns

Functional Array Funhouse Intensive - λC 2017 (Part 1 2 3 4 5)

APL Patterns vs. Anti-Patterns @ FunctionalConf 2017

Co-dfns 2017 Report

APL Style: Patterns and Anti-patterns

Co-dfns Compiler Architecture and Design (Video)

The Key to a Data Parallel Compiler

Accelerating Information Experts Through Compiler Design

Co-dfns: Ancient Language, Modern Compiler

U11: Using Co-dfns to Accelerate APL Code

U07: Co-dfns Report: Performance and Reliability Prototyping

I04: Co-dfns Compiler

Dyalog 2016 Presentation


High-performance, Reliable, and Parallel APL



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