ROCm VS oneAPI.jl

Compare ROCm vs oneAPI.jl and see what are their differences.

ROCm

AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm] (by RadeonOpenCompute)

oneAPI.jl

Julia support for the oneAPI programming toolkit. (by JuliaGPU)
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ROCm oneAPI.jl
198 4
3,637 171
- 1.8%
0.0 8.1
4 months ago 8 days ago
Python Julia
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ROCm

Posts with mentions or reviews of ROCm. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-06.

oneAPI.jl

Posts with mentions or reviews of oneAPI.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-08.
  • GPU vendor-agnostic fluid dynamics solver in Julia
    11 projects | news.ycombinator.com | 8 May 2023
    https://github.com/JuliaGPU/oneAPI.jl

    As for syntax, Julia syntax scales from a scripting language to a fully typed language. You can write valid and performant code without specifying any types, but you can also specialize methods for specific types. The type notation uses `::`. The types also have parameters in the curly brackets. The other aspect that makes this specific example complicated is the use of Lisp-like macros which starts with `@`. These allow for code transformation as I described earlier. The last aspect is that the author is making extensive use of Unicode. This is purely optional as you can write Julia with just ASCII. Some authors like to use `ε` instead of `in`.

  • Cuda.jl v3.3: union types, debug info, graph APIs
    8 projects | news.ycombinator.com | 13 Jun 2021
    https://github.com/JuliaGPU/AMDGPU.jl

    https://github.com/JuliaGPU/oneAPI.jl

    These are both less mature than CUDA.jl, but are in active development.

  • Unified programming model for all devices – will it catch on?
    2 projects | news.ycombinator.com | 1 Mar 2021
    OpenCL and various other solutions basically require that one writes kernels in C/C++. This is an unfortunate limitation, and can make it hard for less experienced users (researchers especially) to write correct and performant GPU code, since neither language lends itself to writing many mathematical and scientific models in a clean, maintainable manner (in my opinion).

    What oneAPI (the runtime), and also AMD's ROCm (specifically the ROCR runtime), do that is new is that they enable packages like oneAPI.jl [1] and AMDGPU.jl [2] to exist (both Julia packages), without having to go through OpenCL or C++ transpilation (which we've tried out before, and it's quite painful). This is a great thing, because now users of an entirely different language can still utilize their GPUs effectively and with near-optimal performance (optimal w.r.t what the device can reasonably attain).

    [1] https://github.com/JuliaGPU/oneAPI.jl

What are some alternatives?

When comparing ROCm and oneAPI.jl you can also consider the following projects:

tensorflow-directml - Fork of TensorFlow accelerated by DirectML

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform

SHARK - SHARK - High Performance Machine Learning Distribution

plaidml - PlaidML is a framework for making deep learning work everywhere.

llama.cpp - LLM inference in C/C++

exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.

tensorflow-upstream - TensorFlow ROCm port

ROCm-OpenCL-Runtime - ROCm OpenOpenCL Runtime

AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!

kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.