Introducing Triton: Open-source GPU programming for neural networks
📰 OpenAI News
OpenAI introduces Triton, an open-source Python-like programming language for efficient GPU programming of neural networks
Action Steps
- Write efficient GPU code using Triton's Python-like syntax
- Optimize matrix multiplication kernels to match cuBLAS performance
- Use Triton to produce custom GPU kernels for deep learning models
- Explore Triton's compiler backend and high-level system architecture
Who Needs to Know This
Researchers and developers working on deep learning projects can benefit from Triton to optimize GPU performance, while data scientists and AI engineers can utilize it to improve model training efficiency
Key Insight
💡 Triton allows researchers to reach peak hardware performance with relatively little effort, making GPU programming more accessible
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🚀 Triton: Open-source GPU programming for neural networks, enabling researchers to write efficient code with little CUDA experience 💻
Key Takeaways
OpenAI introduces Triton, an open-source Python-like programming language for efficient GPU programming of neural networks
Full Article
# Introducing Triton: Open-source GPU programming for neural networks | OpenAI
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Introducing Triton: Open-source GPU programming for neural networks | OpenAI
Table of contents
* [The challenges of GPU programming](https://openai.com/index/triton#the-challenges-of-gpu-programming)
* [Programming model](https://openai.com/index/triton#programming-model)
* [Matrix multiplication](https://openai.com/index/triton#matrix-multiplication)
* [High-level system architecture](https://openai.com/index/triton#high-level-system-architecture)
* [Compiler backend](https://openai.com/index/triton#compiler-backend)
* [Contributing](https://openai.com/index/triton#contributing)
July 28, 2021
[Release](https://openai.com/research/index/release/)
# Introducing Triton: Open-source GPU programming for neural networks
[View code(opens in a new window)](https://github.com/openai/triton)[Read documentation(opens in a new window)](https://triton-lang.org/main/index.html)
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We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.
## Why it matters
Triton makes it possible to reach peak hardware performance with relatively little effort; for example, it can be used to write FP16 matrix multiplication kernels that match the performance of cuBLAS—something that many GPU programmers can’t do—in under 25 lines of code. Our researchers have already used it to produce kernels that are up to 2x more efficient than equivalent Torch implementations, and we’re excited to work with the community to make GPU programming more accessible to everyone.
Novel research ideas in the field of Deep Learning are generally implemented using a combination of native framework operators. While convenient, this approach often requires the creation (and/or movement) of many temporary tensors, which can hurt the performance of neural networks at scale. These issues can be mitigated by writing specialized GPU kernels, but doing so can be surprisingly difficult due to the many intricacies of GPU programming.[1](https://openai.com/index/triton#citation-bottom-1), [2](https://openai.com/index/triton#citation-bottom-2), [3](https://openai.com/index/triton#citation-bottom-3) And, although a variety of systems have recently emerged[4](https://openai.com/index/triton#citation-bottom-4), [5](https://openai.com/index/triton#citation-bottom-5)to make this process easier, we have found them to be either too verbose, lack flexibility or generate code noticeably slower than our hand-tuned baselines. This has led us to extend and improve Triton[6](https://openai.com/index/triton#citation-bottom-6), a recent language and compiler whose original creator now works at OpenAI.
## The challenges of GPU programming
The architecture of modern GPUs can be roughly divided into three major components—DRAM, SRAM and ALUs—each of which must be considered when optimizing CUDA code:
* Memory transfers from DRAM must be _coalesced_ into large transactions to levera
[Skip to main content](https://openai.com/index/triton#main)
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* [Business](https://openai.com/business/)
* [Developers](https://openai.com/api/)
* [Company](https://openai.com/about/)
* [Foundation(opens in a new window)](https://openaifoundation.org/)
[Try ChatGPT(opens in a new window)](https://chatgpt.com/?openaicom-did=575e9bad-72a0-477c-a390-3cb039cf8372&openaicom_referred=true)
* Research
* Products
* Business
* Developers
* Company
* [Foundation(opens in a new window)](https://openaifoundation.org/)
Introducing Triton: Open-source GPU programming for neural networks | OpenAI
Table of contents
* [The challenges of GPU programming](https://openai.com/index/triton#the-challenges-of-gpu-programming)
* [Programming model](https://openai.com/index/triton#programming-model)
* [Matrix multiplication](https://openai.com/index/triton#matrix-multiplication)
* [High-level system architecture](https://openai.com/index/triton#high-level-system-architecture)
* [Compiler backend](https://openai.com/index/triton#compiler-backend)
* [Contributing](https://openai.com/index/triton#contributing)
July 28, 2021
[Release](https://openai.com/research/index/release/)
# Introducing Triton: Open-source GPU programming for neural networks
[View code(opens in a new window)](https://github.com/openai/triton)[Read documentation(opens in a new window)](https://triton-lang.org/main/index.html)
Listen to article
Share
We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.
## Why it matters
Triton makes it possible to reach peak hardware performance with relatively little effort; for example, it can be used to write FP16 matrix multiplication kernels that match the performance of cuBLAS—something that many GPU programmers can’t do—in under 25 lines of code. Our researchers have already used it to produce kernels that are up to 2x more efficient than equivalent Torch implementations, and we’re excited to work with the community to make GPU programming more accessible to everyone.
Novel research ideas in the field of Deep Learning are generally implemented using a combination of native framework operators. While convenient, this approach often requires the creation (and/or movement) of many temporary tensors, which can hurt the performance of neural networks at scale. These issues can be mitigated by writing specialized GPU kernels, but doing so can be surprisingly difficult due to the many intricacies of GPU programming.[1](https://openai.com/index/triton#citation-bottom-1), [2](https://openai.com/index/triton#citation-bottom-2), [3](https://openai.com/index/triton#citation-bottom-3) And, although a variety of systems have recently emerged[4](https://openai.com/index/triton#citation-bottom-4), [5](https://openai.com/index/triton#citation-bottom-5)to make this process easier, we have found them to be either too verbose, lack flexibility or generate code noticeably slower than our hand-tuned baselines. This has led us to extend and improve Triton[6](https://openai.com/index/triton#citation-bottom-6), a recent language and compiler whose original creator now works at OpenAI.
## The challenges of GPU programming
The architecture of modern GPUs can be roughly divided into three major components—DRAM, SRAM and ALUs—each of which must be considered when optimizing CUDA code:
* Memory transfers from DRAM must be _coalesced_ into large transactions to levera
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