awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
ROCm avatar

ROCm/ROCm

0
View on GitHub↗
rocm.docs.amd.com↗

ROCm

Features

  • AMD Hardware Acceleration - Optimizes machine learning and AI computations by leveraging AMD Instinct and Radeon GPUs through an open-source platform.
  • GPU Acceleration - Runs machine learning, engineering, and scientific workloads on GFX9 and CDNA GPUs using the ROCm software stack.
  • Deep Learning Frameworks - Integrates with popular deep learning frameworks to enable accelerated training and inference on AMD GPUs.
  • Computation Abstraction Layers - Provides a unified runtime and compiler infrastructure mapping multiple programming models to AMD GPU hardware.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI
  • Matrix Fused Multiply-Add Engines - Performs matrix fused multiply-add on KxN matrices using mixed-precision inputs to accelerate ML and HPC workloads.
  • GPU-Accelerated HPC Development Platforms - Runs high-performance computing workloads on AMD GPUs using HIP, OpenCL, and OpenMP interfaces.
  • GPU Debugger Extensions - Ships a GDB-based debugger for inspecting and controlling GPU-accelerated programs on AMD hardware.
  • GDB-Based GPU Debuggers - Inspects and controls GPU-accelerated programs using a GDB-based debugger for AMD hardware.
  • CMake Integrations - Provides config-file CMake packages that automatically locate and link ROCm components without custom modules.
  • Platform Library Distributions - Organizes GPU runtime, compiler, math libraries, and profiling tools into layered, versioned packages.
  • Mixed-Precision Matrix Accelerators - Provides optimized libraries for mixed-precision matrix operations and HPC workloads on AMD GPUs.
  • GPU Debugger Extensions - Extends GDB with GPU thread inspection, memory access, and breakpoint support for AMD hardware.
  • GPU Programming Runtimes - Provides the HIP runtime that translates CUDA-like code to run on AMD GPUs.
  • Multi-Language GPU Programming Interfaces - Provides HIP, OpenCL, and OpenMP interfaces for developing high-performance computing applications on AMD GPUs.
  • GDB-Based GPU Debuggers - Inspects and controls GPU-accelerated programs with a GDB-based debugger for AMD hardware.
  • GPU Health Monitors - Tracks and manages data center GPU health metrics through a command-line interface wrapping device driver calls.
  • GPU - Provides a command-line tool for monitoring and managing AMD GPU health, reliability, and performance metrics.
  • Kernel Dispatchers - Launches compute kernels directly on GPU hardware via a command processor for minimal latency.
  • Mixed-Precision Compute Engines - Accelerates matrix multiply-add operations by dispatching FP32, FP16, BF16, and Int8 tiles to dedicated hardware units.
  • Mixed-Precision Computing - Performs matrix operations with FP32, FP16, BF16, and Int8 inputs to speed up ML and HPC workloads.
  • GPU Health Monitors - Monitors and manages data center GPU health metrics through a command-line interface.
  • Config-File Package Integrations - Exports pre-built CMake config files that automatically locate and link ROCm components without custom Find modules.
  • Inter-GPU Bandwidth Probes - Reports the throughput of the xGMI interconnect between GPU nodes via a dedicated library API.
  • HPC System Tuners - Offers guidance on system optimization and performance validation for HPC workloads on AMD GPUs.
    • 6,645 stars·574 forks·Shell·MIT·35 views

    Star history

    Star history chart for rocm/rocmStar history chart for rocm/rocm

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Projects sharing features with ROCm

    These projects share indexed features with ROCm. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • nvidia/isaac-gr00tNVIDIA avatar

      NVIDIA/Isaac-GR00T

      6,222View on GitHub↗
      Jupyter Notebook
      View on GitHub↗6,222
    • rocm-developer-tools/hipROCm-Developer-Tools avatar

      ROCm-Developer-Tools/HIP

      4,362View on GitHub↗

      HIP is a C++ GPU kernel language and cross-platform runtime designed for writing portable high-performance compute applications. It provides a programming interface that allows a single source codebase to execute on both AMD and NVIDIA GPU architectures. The project functions as a compatibility layer that enables the conversion and migration of existing CUDA source code to run on AMD hardware. This is achieved through a syntax mapping that mirrors CUDA and a source-to-source translation process during compilation. The toolkit covers the broader surface of cross-platform GPGPU development, in

      C++
      View on GitHub↗4,362
    • infrasys-ai/aisystemInfrasys-AI avatar

      Infrasys-AI/AISystem

      17,017View on GitHub↗

      AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

      Jupyter Notebookaiaiinfraaisys
      View on GitHub↗17,017
    • nvidia/cutlassNVIDIA avatar

      NVIDIA/cutlass

      9,904View on GitHub↗

      Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra operations on NVIDIA GPUs. It provides a kernel composition framework for designing custom GPU kernels and a mixed-precision tensor library capable of executing operations across diverse data formats, ranging from 64-bit floating point to 4-bit integers. The project features a toolkit for operator fusion that integrates activation functions and bias calculations directly into matrix multiplication kernels to reduce memory passes. It also includes a Python-based domain-specific langu

      C++cppcudadeep-learning
      View on GitHub↗9,904
    Compare all 30 related projects→

    Frequently asked questions

    What are the main features of rocm/rocm?

    The main features of rocm/rocm are: AMD Hardware Acceleration, GPU Acceleration, Deep Learning Frameworks, Computation Abstraction Layers, Matrix Fused Multiply-Add Engines, GPU-Accelerated HPC Development Platforms, GPU Debugger Extensions, GDB-Based GPU Debuggers.

    Which projects share features with rocm/rocm?

    Projects with overlapping indexed features include: nvidia/isaac-gr00t. rocm-developer-tools/hip — HIP is a C++ GPU kernel language and cross-platform runtime designed for writing portable high-performance compute… infrasys-ai/aisystem — AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip… nvidia/cutlass — Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… google/flax — Flax is a deep learning framework and JAX neural network library designed for building complex machine learning…