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ROCm
hip
C++2016–2025
ROCm / hip

hip

HIP: C++ Heterogeneous-Compute Interface for Portability

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44Distinct people
47Mentions
0Explicit recommenders
4.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

HIP: C++ Heterogeneous-Compute Interface for Portability

GitHub

Where it is used

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

What people are saying

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Also in these discussions

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

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 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 year47 dated mentions

Observed mentions: 2016-05-08 13:55:36 UTC to 2025-08-09 21:51:04 UTC.

02468102016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 7 mentions; 1 submissions, 6 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 10 mentions; 0 submissions, 10 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 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
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC716
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC10010
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC615
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC404
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC606
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC202
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC615
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC413
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC211

What Hacker News says

47 sources
nikonyrh·Aug 9, 2025
AMD already has Composable Kernels[1], and supports for example Triton[2]. Then there is also HIP[3], and there are tools to automatically convert from from CUDA to HIP. But since CUDA is the de-facto standard, there is always friction to use something else (unless you need to support also AMD stack). Making something just CUDA-compatible is non-trivial, and since Nvidia decides its direction and new features then the alternatives would always be lagging behind. Currently there are also major hardware differences between Nvidia and AMD, which may make highly optimized CUDA code inefficient or even buggy. [1] https://github.com/ROCm/composable_kernel?tab=readme-ov-file#composable-kernel [2] https://github.com/triton-lang/triton?tab=readme-ov-file#triton [3] https://github.com/ROCm/HIP?tab=readme-ov-file#what-is-this-repository-for
In Nvidia is dominating the S&P 500 more than any company in at least 44 years
eslaught·Jul 15, 2024
Are you aware of HIP? It's officially supported and, for code that avoids obscure features of CUDA like inline PTX, it's pretty much a find-and-replace to get a working build: https://github.com/ROCm/HIP Don't believe me? Include this at the top of your CUDA code, build with hipcc, and see what happens: https://gitlab.com/StanfordLegion/legion/-/blob/master/runti... It's incomplete because I'm lazy but you can see most things are just a single #ifdef away in the implementation.
In Run CUDA, unmodified, on AMD GPUs
latchkey·Dec 16, 2023
> what would be the point for someone to add ROCm support to various pieces of software which currently require CUDA It isn't just old cards though, CUDA is a point of centralization on a single provider during a time when access to that providers higher end cards isn't even available and that is causing people to look elsewhere. ROCm supports CUDA through the included HIP projects... https://github.com/ROCm/HIP https://github.com/ROCm/HIPCC https://github.com/ROCm/HIPIFY The later will regex replace your CUDA methods with HIP methods. If it is as easy as running hipify on your codebase (or just coding to HIP apis), it certainly makes sense to do so.
In Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
c0n5pir4cy·Dec 15, 2023
AMD has released HIP and a tool called HIPIFY which kind of behaves like this but at the source level¹. Rather than try and just translate CUDA to work on AMD compute they are more focused on higher level tooling. Currently they seem to have a particular focus on AI frameworks and tools like PyTorch/Tensorflow/ONNX. They have sponsored and helped with a lot of PyTorch development for example, so PyTorch support for AMD is much better than it was this time last year². ¹(https://github.com/ROCm/HIP) ²(https://pytorch.org/blog/experience-power-pytorch-2.0/)
In AMD's Next GPU Is a 3D-Integrated Superchip
fotcorn·Oct 6, 2023
ROCm has HIP (1) which is a compatibility layer to run CUDA code on AMD GPUs. In theory, you only have to adjust #includes, and everything should just work, but as usual, reality is different. Newer backends for AI frameworks like OpenXLA and OpenAI Triton directly generate GPU native code using MLIR and LLVM, they do not use CUDA apart from some glue code to actually load the code onto the GPU and get the data there. Both already support ROCm, but from what I've read the support is not as mature yet compared to NVIDIA. 1: https://github.com/ROCm-Developer-Tools/HIP
In AMD may get across the CUDA moat
0xcde4c3db·Aug 28, 2023
AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched. [1] https://github.com/ROCm-Developer-Tools/HIP
In Nvidia on the Mountaintop
jcelerier·Aug 19, 2023
> does not run on the GPU both Cuda and the Metal shader language are C++, so is OpenCL since 2.0 (https://www.khronos.org/opencl/), so is AMD ROCm's HIP (https://github.com/ROCm-Developer-Tools/HIP), so is SYCL (https://www.khronos.org/sycl/)? C++ is pretty much the language that runs most on GPUs. > no vector instructions, There's a thousand different possibilities for SIMD in C++, from #pragma omp simd, to libs such as std::experimental::simd (https://en.cppreference.com/w/cpp/experimental/simd/simd), Eve (https://github.com/jfalcou/eve), Highway (https://github.com/google/highway), Vc (https://github.com/VcDevel/Vc)...
In Stable Diffusion in C/C++
sorenjan·Sep 21, 2022
The last time I looked into it Blender's ROCm support seemed like a special case with support from AMD. I don't know if that's what it is, every time I try to get a handle on AMD's compute offering I get dizzy. There's so many different projects, Github repositories, sketchy hardware and OS support that changes with different driver versions (Close to Metal, Brook+, Stream Computing, AMD APP SDK, HSA, ROCm, HIP, AOMP, ). It seems very hacked together by a small team that can get defunded anytime, and it's not exactly confidence inspiring or ergonomic. As far as I can tell, HIP is still not generally available on Windows [0] or consumer gaming cards [1]. Not for developers, not for end users. That means hipSYCL can't be used by AMD users on Windows, and I don't see much value for Intel to support it either. I don't think HIP is going to ta
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ke any significant share of the market, why use AMD's platform when they so clearly have demonstrated that they don't really care about compute for more than a decade now? I wish GPU compute could get to a maturity level where you could use any framework you wanted, compile it to SPIR-V, and then run it on any GPU on any OS. Let's say I write some image processing code, or ML inference, or audio processing, that I want to run on consumer's GPUs. What options are there today? Nvidia makes it easy, AMD makes it hard. [0] https://github.com/ROCm-Developer-Tools/HIP/blob/develop/INS... [1] https://github.com/ROCm-Developer-Tools/HIP/blob/develop/doc...
In Ask HN: What is your experience with OpenCL?
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
fxtentacle·Nov 16, 2020
In case someone from AMD is reading here, you should probably gift out some of the cards to generate some traction. For example, send one to me ;) But joke aside, the ROCm documentation seems so wacky that I would not dare to purchase an expensive AMD card without recommendation by someone that I trust. For example, rocmdocs.amd.com appears to be the official documentation, but the front page doesn't mention any popular AI framework. Opening the "Deep Learning" box in the sidebar, I see that there should be a TensorFlow 2.2.0-beta1 release. But on https://github.com/ROCmSoftwarePlatform/tensorflow-upstream the featured release is "2.0.0-rocm", the docker link says it's TensorFlow 2.3 and the "Official Builds" at the bottom of the readme point to CUDA versions ... The whole GitHub repo kind of feels like a bad copy&paste job. I manage to e
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ventually download the TensorFlow 2.3.1 + ROCm source from GitHub, only to find that it doesn't compile. I need the hip_hcc library https://github.com/ROCm-Developer-Tools/HIP/ but apparently that one has been renamed some months ago, but the ROCm TensorFlow port has not been updated yet. After spending half a day trying to install a ROCm-compatible TensorFlow, I gave up. Obviously, I wouldn't want to hope on those issues getting resolved when I buy an as-is unusable $6400 accelerator card. That said, the MI100 could be a great deal if I could get it to work. AMD hardware is a lot more affordable than comparable NVIDIA products, plus the MI100 has lots of fast RAM. I work on optical flow (think autonomous selfie drone) and that needs lots and lots of GPU RAM. Plus I'm very happy with my AMD CPU. But like I said in the beginning, I wouldn't dare buying an AMD GPU unless there was believable evidence that I can get my TF2.3 model running with ROCm in a reasonable timeframe. I'm not trying to blame AMD for having bugs here. I have run into a fair share of issues with NVIDIA GPUs, too, like for example a RAM corruption bug inside CUDA 10.0 and a reproducible kernel freeze in CUDA 10.1. But for NVIDIA, the initial setup is quick and easy. And the CUDA documentation looks like they have a dedicated team that cares about developer productivity. For AMD's ROCm, on the other hand, even the marketing material reads more like someone hopes that the open source community will fix things for free. "first open-source software development platform for HPC/Hyperscale-class GPU computing" sounds great until you notice that in this context, "open-source" apparently means "no binaries + no support".
In AMD at a Tipping Point with Instinct MI100 GPU Accelerators

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