What is a Kernel?

👤 Efrat Bdil 📅 1/7/2026 ⏱️ 2 min read
Hardware Acceleration #Kernels#Parallel Computing
Table of Contents

What is a Kernel?

A Kernel is the most fundamental computational unit that performs operations on data. It represents the “actual computation” - the operation that truly runs on the hardware.

Think of it as:

  • A function written in a low-level language (like C++ or CUDA)
  • The small algorithm that performs the real work
  • The core of the operator - where computation happens at the bit and memory level

Why Do We Need Kernels?

  1. To perform computations quickly and efficiently Kernels are written to maximize hardware capabilities: parallelism, local memory, multi-core utilization, dedicated compute engines, and more.

  2. To hide complexity from the user A model user doesn’t need to know how exactly a convolution is performed. The Kernel handles all the details - from memory allocation to processor instructions.

  3. Because every hardware requires tailored Kernels CPU, GPU, TPU, or a dedicated accelerator like NR1 - each has its own Kernels optimized for its configuration and architecture.

Conceptual Example (Not Code)

Let’s say you want to perform matrix multiplication. In PyTorch, this is done with a simple call.

But in reality:

  • PyTorch invokes the backend
  • The backend selects an appropriate Kernel
  • The Kernel runs on the hardware
  • And the actual computation happens there

The user sees “one operation.” The system performs dozens of optimizations under the hood.

How Does This Relate to AI?

Every model - from the smallest to the largest LLM - is built from thousands of basic operations. Each of these relies on a Kernel.

Therefore:

  • Performance
  • Memory consumption
  • Latency
  • Throughput

All are largely determined by the quality and efficiency of the Kernels running the model.

Summary

A Kernel is the computational core - the unit that actually performs the work upon which every AI library relies.

  • It is the true compute engine
  • Tailored to the hardware
  • Hidden from the user
  • Critical to performance

Behind every “simple” line in PyTorch or any ML framework - there’s a Kernel doing the real work.

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