Article by Ayman Alheraki on January 11 2026 10:36 AM
there are some languages, extensions, or frameworks specifically developed for this purpose, such as:
It’s not a completely new language, but rather an extension of C/C++ that allows you to write code that runs directly on NVIDIA GPUs.
It provides a powerful programming environment for handling large datasets and machine learning tasks.
An open standard that supports multiple vendors (NVIDIA, AMD, Intel).
It allows you to write code that works on both CPU and GPU.
It’s less easy to learn compared to CUDA and has fewer resources, but it’s more flexible in terms of compatibility.
Languages tied to graphic libraries like DirectX (HLSL), OpenGL (GLSL), and Metal (from Apple).
Designed for programming shaders inside game engines and graphics applications.
Similar to C and designed for writing parallel code that can take advantage of SIMD on both CPU and GPU.
However, it’s not widely used outside of Intel-specific use cases.
A language like C++ relies on Stack, Heap, and complex memory management.
GPUs, on the other hand, work on thousands of smaller cores in parallel and use shared memory, which requires an entirely different programming model.
NVIDIA, AMD, Intel, and Apple each have their own approach to designing GPUs.
Creating a single language that would work universally across all these architectures without becoming overly general or inefficient is a challenge.
GPU programming requires an in-depth understanding of how to distribute data, synchronize cores, and manage GPU memory.
Most developers don’t need that level of control on a daily basis, so general-purpose programming languages don’t typically provide that functionality by default.
Yes, and there have been attempts to do so:
| Attempt | Description |
|---|---|
| Taichi | A new programming language based on Python designed for efficient GPU programming, focused on physics simulations and high-performance computation. |
| Halide | A Domain-Specific Language (DSL) built on C++ for efficient image processing using GPU/CPU. |
| Julia + CUDA.jl | The Julia language, known for its flexibility, also has a CUDA library for writing GPU code directly. |
However, these attempts remain within relatively narrow, specialized domains and are not general-purpose programming languages.
There is currently no general-purpose programming language designed entirely for GPUs.
However, there are specialized libraries, extensions, and languages (like CUDA, OpenCL, HLSL…) that serve this purpose efficiently.
The main reason is the deep differences in instruction execution and memory handling between CPUs and GPUs, along with the lack of standardization in GPU design between major companies.