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ROCm avatar

ROCm/ROCm

0
View on GitHub↗
6,645 estrellas·574 forks·Shell·MIT·10 vistasrocm.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.
  • 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.

Historial de estrellas

Gráfico del historial de estrellas de rocm/rocmGráfico del historial de estrellas de rocm/rocm

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Preguntas frecuentes

¿Cuáles son las características principales de rocm/rocm?

Las características principales de rocm/rocm son: 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.

¿Qué alternativas de código abierto existen para rocm/rocm?

Las alternativas de código abierto para rocm/rocm incluyen: 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…