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ROCm
HIPIFY
C++2020–2025
ROCm / HIPIFY

HIPIFY

HIPIFY: Convert CUDA to Portable C++ Code

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Explore on GitHubRead Hacker News discussions
17Distinct people
24Mentions
0Explicit recommenders
738GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

HIPIFY: Convert CUDA to Portable C++ Code

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

  • rocm/hip · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:05:26 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year24 dated mentions

Observed mentions: 2020-05-10 12:23:30 UTC to 2025-10-23 13:58:10 UTC.

024682020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 8 mentions; 0 submissions, 8 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC220
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC303
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC808
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC817
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202

What Hacker News says

3 sources
my123·Dec 21, 2022
> Thus, the idea is that through typically negligible effort porting to HiP, your code becomes vendor-independent. Here, the big AMD mistake was to rename those function prefixes in the first place. It's a mistake that they could have avoided... What a lot of SW codebases did to support AMD (see PyTorch code notably): codebase is still CUDA, have the conversion pass to HIP done at build time. See https://github.com/ROCm-Developer-Tools/HIPIFY/blob/amd-stag... for the Perl script to do it. Then comes the problem of AMD not supporting ROCm HIP on most of their hardware or user base. On Windows, the ROCm HIP SDK is private and only available under NDA. This means that while you can use Blender w/ HIP on Windows, the Blender builds that you compile yourself will not be able to use ROCm HIP. On Linux, the supported GPUs are few and far between
Read the rest
, Vega20 onwards are supported today. APUs, RDNA1, and lower end RDNA2 w/o unsupported hacks (6700 XT and below) are excluded.
In Stable Diffusion on AMD RDNA3
westurner·Sep 19, 2022
From the Arch wiki, which has a list of GPU runtimes (but not TPU or QPU runtimes) and arch package names: OpenCL, SYCL, ROCm, HIP,: https://wiki.archlinux.org/title/GPGPU : > GPGPU stands for General-purpose computing on graphics processing units. - "PyTorch OpenCL Support" https://github.com/pytorch/pytorch/issues/488 - Blender re: removal of OpenCL support in 2021 : > The combination of the limited Cycles split kernel implementation, driver bugs, and stalled OpenCL standard has made maintenance too difficult. We can only make the kinds of bigger changes we are working on now by starting from a clean slate. We are working with AMD and Intel to get the new kernels working on their GPUs, possibly using different APIs (such as CYCL, HIP, Metal, …). - https://gitlab.com/illwieckz/i-love-compute - https://github.com/vosen/ZLUDA - https://git
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hub.com/RadeonOpenCompute/clang-ocl AMD ROCm: https://en.wikipedia.org/wiki/ROCm AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni... RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY : > hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced. ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort : > GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP: > HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include: > - HIP is very thin and has little or no performance impact over coding directly in CUDA mode. > - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more. > - HIP allows developers to use the "best" development environment and tools on each target platform. > - The [HIPIFY] tools automatically convert source from CUDA to HIP. > - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*
In AI Seamless Texture Generator Built-In to Blender

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