# MIOpen: An Open Source Library For Deep Learning Primitives

arXiv: https://arxiv.org/abs/1910.00078 · HTML: https://mlsanity.com/paper/1910.00078/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [Relax: Composable Abstractions for End-to-End Dynamic Machine Learning](https://mlsanity.com/paper/2311.02103/) (2023)
- [A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks](https://mlsanity.com/paper/2111.04949/) (2021)
- [oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation](https://mlsanity.com/paper/2301.01333/) (2023)
- [Collage: Seamless Integration of Deep Learning Backends with Automatic Placement](https://mlsanity.com/paper/2111.00655/) (2021)
- [Compiler Toolchains for Deep Learning Workloads on Embedded Platforms](https://mlsanity.com/paper/2104.04576/) (2021)
- [Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models](https://mlsanity.com/paper/2404.10162/) (2024)

## Abstract (arXiv)

Deep Learning has established itself to be a common occurrence in the business lexicon. The unprecedented success of deep learning in recent years can be attributed to: abundance of data, availability of gargantuan compute capabilities offered by GPUs, and adoption of open-source philosophy by the researchers and industry. Deep neural networks can be decomposed into a series of different operators. MIOpen, AMD's open-source deep learning primitives library for GPUs, provides highly optimized implementations of such operators, shielding researchers from internal implementation details and hence, accelerating the time to discovery. This paper introduces MIOpen and provides details about the internal workings of the library and supported features. MIOpen innovates on several fronts, such as implementing fusion to optimize for memory bandwidth and GPU launch overheads, providing an auto-tuning infrastructure to overcome the large design space of problem configurations, and implementing different algorithms to optimize convolutions for different filter and input sizes. MIOpen is one of the first libraries to publicly support the bfloat16 data-type for convolutions, allowing efficient training at lower precision without the loss of accuracy.