AMD ROCm icon
AMD ROCm icon

AMD ROCm

ROCm is an open-source stack, composed primarily of open-source software, designed for graphics processing unit (GPU) computation. ROCm consists of a collection of drivers, development tools, and APIs that enable GPU programming from low-level kernel to end-user applications.

Cost / License

  • Free
  • Open Source

Platforms

  • Windows
  • Linux
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Features

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  1.  GPU Support
  2.  Parallel Computing
  3.  AMD
  4.  GPU Acceleration

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AMD ROCm information

  • Developed by

    US flagAdvanced Micro Devices
  • Licensing

    Open Source (MIT) and Free product.
  • Alternatives

    2 alternatives listed
  • Supported Languages

    • English

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System & Hardware

GitHub repository

  •  5,965 Stars
  •  499 Forks
  •  200 Open Issues
  •   Updated  
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What is AMD ROCm?

ROCm is an open-source stack, composed primarily of open-source software, designed for graphics processing unit (GPU) computation. ROCm consists of a collection of drivers, development tools, and APIs that enable GPU programming from low-level kernel to end-user applications.

With ROCm, you can customize your GPU software to meet your specific needs. You can develop, collaborate, test, and deploy your applications in a free, open source, integrated, and secure software ecosystem. ROCm is particularly well-suited to GPU-accelerated high-performance computing (HPC), artificial intelligence (AI), scientific computing, and computer aided design (CAD).

ROCm is powered by AMD’s Heterogeneous-computing Interface for Portability (HIP), an open-source software C++ GPU programming environment and its corresponding runtime. HIP allows ROCm developers to create portable applications on different platforms by deploying code on a range of platforms, from dedicated gaming GPUs to exascale HPC clusters.

ROCm supports programming models, such as OpenMP and OpenCL, and includes all necessary open source software compilers, debuggers, and libraries. ROCm is fully integrated into machine learning (ML) frameworks, such as PyTorch and TensorFlow.

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