ROCm/hip

HIP: C++ Heterogeneous-Compute Interface for Portability

What it solves

It addresses the 문제는 the lack of a unified programming interface for GPU acceleration across different hardware vendors. Specifically, it simplifies the process of porting existing NVIDIA CUDA code to run on AMD GPUs, reducing developer effort and hardware lock-in.

How it works

HIP provides a C++ runtime API and a kernel language that mimics the NVIDIA CUDA programming interface. By offering a similar syntax and API, developers can write code that is portable across different GPU architectures.

Who it’s for

Developers who want to create portable GPU-accelerated applications or those who looking to migrate existing CUDA-based software to AMD hardware.

Highlights

  • C++ runtime API and kernel language specifically for AMD GPUs.
  • Designed for portability, allowing applications to run on both AMD and NVIDIA hardware.
  • High compatibility with NVIDIA CUDA, making porting straightforward.

Related

  • Project
    ROCm/rocm-examples

    A collection of code examples and tutorials for ROCm and HIP, enabling users to accelerate applications and port CUDA code to AMD GPUs.

  • Project
    ROCm/HIPIFY

    HIPIFY provides command‑line tools (primarily `hipify-clang`) that automatically translate CUDA C++ code into portable HIP code, enabling the same source to run on AMD and NVIDIA GPUs. It also can generate Markdown or CSV documentation of the supported CUDA‑to‑HIP API mappings.

  • Project
    AdaptiveCpp/AdaptiveCpp

    A community-driven platform for C++ heterogeneous programming that enables a single binary to run across CPUs and GPUs from all major vendors.

  • Project
    kunzmi/managedCuda

    A .NET wrapper for NVIDIA's CUDA Driver API and libraries, enabling C# and Visual Basic developers to integrate GPU acceleration into their applications.

  • Project
    ROCm/legacy-rocm-build

    ROCm / legacy‑rocm‑build is the (now‑deprecated) repository that assembles AMD’s open‑source GPU compute stack—drivers, HIP runtime, math libraries, profiling tools, and more—so developers can run AI/ML workloads on AMD GPUs. It points users to install guides and to the newer TheRock monorepo.