Kernel
A kernel is a specific program that runs on a GPU to perform a calculation, often for AI tasks.
In AI, a kernel is like a specialized worker that performs a single, focused calculation on a graphics processing unit (GPU). Think of it as a tiny program designed to do one job very efficiently, like multiplying two large sets of numbers.
GPUs have thousands of these workers that can perform calculations simultaneously. Kernels are the instructions that tell these workers exactly what to do, making them crucial for speeding up the complex math involved in training and running AI models.
In the context of GPU computing and AI, a kernel is a function designed to be executed on the GPU's parallel processing cores. It typically performs a specific computational task, such as matrix multiplication, convolution, or element-wise operations, which are fundamental building blocks for neural networks.
Kernels are launched by the CPU and then run across many GPU threads in parallel. Efficient kernel design and implementation, often using frameworks like CUDA or Triton, are critical for maximizing GPU utilization and achieving high performance in AI workloads.
Frontier AI labs are increasingly focused on optimizing the underlying computational processes that power their models. This includes developing specialized hardware and software, like custom chips and efficient programming tools, to accelerate AI training and inference. These efforts aim to make AI development faster, more reliable, and more accessible.
- How Claude is uplifting biomolecular modelingAnthropic · Anthropic Research ·
- OpenAI and Broadcom unveil LLM-optimized inference chipOpenAI · OpenAI News ·
- Powering the next generation of AI development with AWSAnthropic · Anthropic News ·
- Introducing Triton: Open-source GPU programming for neural networksOpenAI · OpenAI News ·
- Anthropic Claude
- Anthropic is investing in custom hardware for AI training, suggesting a focus on optimizing the core computational processes for their models.
- OpenAI ChatGPT
- OpenAI is developing custom AI inference chips and open-source GPU programming tools like Triton to boost neural network performance and simplify hardware utilization.
- Operating System Kernel
- The core of an operating system that manages hardware a
- Core
- A general term for the central or most important part of something, not sp
The term 'kernel' in computing originates from the core component of an operating system. In the context of parallel computing and GPUs, it refers to a specific function or program that runs on the parallel processors. Source ↗
While directly writing or running a GPU kernel is complex, you can observe their impact. Try running a large image generation model (like those from OpenAI or Google Gemini) and notice the speed. The faster the generation, the more efficiently the underlying kernels are likely performing the necessary calculations on the GPU.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- Code snippetTriton FP16 matrix multiplication kernel exampleExample of a concise FP16 matrix multiplication kernel in Triton, achieving high performance. · 5 min
- Code snippetTriton fused softmax kernel exampleExample of a fused softmax kernel written in Triton, simplifying normalization and maximizing data reuse. · 5 min
- Anti-patternWriting complex kernels directly in CUDAAvoid writing complex GPU kernels directly in CUDA; use Triton for easier, efficient code. · 5 min
- ComparisonTriton vs. PyTorch internal CUDA for fused kernelsTriton simplifies specialized kernel development, often outperforming general-purpose library implementations. · 5 min
- Decision frameworkWhen to use Triton for GPU programmingUse Triton for writing efficient GPU kernels when performance is critical and CUDA expertise is limited. · 5 min
Questions people ask
- What's the difference between a CPU kernel and a GPU kernel?
- A CPU kernel is part of the operating system managing the computer. A GPU kernel is a specific program that runs on a graphics card to perform parallel computations for tasks like AI.
- Why are kernels important for AI?
- Kernels perform the fundamental mathematical operations needed for AI models. Efficient kernels on GPUs allow AI to train faster and respond more quickly.
First seen on Subvolts Jul 28, 2021. Updated Sep 24, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.