⚠️ Note: AMD GPUs do not natively support CUDA, which is proprietary to NVIDIA.
This repository emulates a CUDA-like development environment for AMD GPUs by leveraging AMD’s ROCm platform, HIP (Heterogeneous-compute Interface for Portability), and CI pipelines to enable high-performance GPU compute across RDNA/CDNA hardware.

CUDA is NVIDIA-specific. It tightly couples hardware and software via proprietary drivers, compilers, and a runtime stack. AMD GPUs do not support CUDA natively. But if you're on Team Red (AMD), you’re not left behind—thanks to HIP and ROCm.

• NVIDIA uses: CUDA, cuDNN, nvcc, libcudart, and PTX kernels
• AMD uses: HIP, ROCm, hipcc, ROCr runtime, and HSACO kernels

• Write once, run on both NVIDIA and AMD
• Familiar to CUDA devs
• Supported by PyTorch, TensorFlow via AMD extensions

HIP (Heterogeneous-Compute Interface for Portability) is AMD’s CUDA-to-AMD translation layer. It allows CUDA-like syntax with hipcc to compile and run on both AMD and NVIDIA.

// HIP example kernel
__global__ void addKernel(float* c, const float* a, const float* b) {
    int i = threadIdx.x;
    c[i] = a[i] + b[i];
}

Each ROCm component repo links to this central YAML orchestrator via rocm-ci.yml.

AMD kernel compilation flow:

1. Source (.hip or .cpp) → LLVM IR
2. LLVM IR → HSA Intermediate Language (HSAIL)
3. HSAIL → HSACO (HSA Code Object)
4. Executed on GCN / RDNA via ROCr runtime

Think of HSACO like NVIDIA’s PTX, but for AMD hardware.

AMD compiler backends (LLVM + HCC) convert kernels into Static Single Assignment (SSSA) form for:

• Aggressive loop tiling
• SIMD vectorization
• Wavefront-level parallelism (similar to warps in CUDA)

• hipcc – HIP compiler (alias of clang)
• rocminfo – Device capabilities viewer
• clinfo – OpenCL device inspection
• rocm-smi – AMD GPU status tool (temp, power, clocks)
• hipify-perl – Convert .cu files to .hip

• ROCm now supports AI super-scaling (similar to Tensor Cores) via Matrix Cores in MI300/Instinct series.
• Upcoming OpenCL 3.0 compliance
• Potential Vulkan Compute ↔ HIP interop for hybrid render/compute

This project is designed to work with AMD’s modern GPU compute pipeline, including:

• Kernel Offloading: HIP kernels are offloaded to dGPU or APU over PCIe/Infinity Fabric via ROCr
• Wavefront Scheduler: SIMD32-based warp schedulers for controlling parallelism
• SSSA Core Tuning: Runtime toggling of shared memory, VGPR/SGPR registers
• Multi-GPU Strategy: ROCm-aware build agents can parallelize workloads across GPU groups
• LLDB & ROCgdb Debugging: Attach low-level debuggers in CI to trace kernel failures
• ROCm Profiler Support: CI-integrated hooks for collecting kernel execution metrics

Azure VMSS instances provisioned for ROCm builds should include Docker and be ROCm-aware. Use the following cloud-init config in the Custom Data field during VM creation:

#cloud-config

bootcmd:
  - mkdir -p /etc/systemd/system/walinuxagent.service.d
  - echo "[Unit]\nAfter=cloud-final.service" > /etc/systemd/system/walinuxagent.service.d/override.conf
  - sed "s/After=multi-user.target//g" /lib/systemd/system/cloud-final.service > /etc/systemd/system/cloud-final.service
  - systemctl daemon-reload

apt:
  sources:
    docker.list:
      source: deb [arch=amd64] https://download.docker.com/linux/ubuntu $RELEASE stable
      keyid: 9DC858229FC7DD38854AE2D88D81803C0EBFCD88

packages:
  - docker-ce
  - docker-ce-cli
  - libnuma-dev

groups:
  - docker

runcmd:
  - usermod -aG docker $USER
  - systemctl restart docker
  - systemctl enable docker

• Dynamic Buffer Kernel Dispatch using HIP Streams and Signal Queues

• Vulkan Interop pipeline for ROCm + Vulkan kernel rendering on RDNA

• Automatic HIPIFY scanning of CUDA code in Pull Requests

• SSBO + Async Compute Support for mixed graphics/GPU compute CI tests

• LLVM Pass Hooks for auto-injecting metrics during device compile

• HIP Programming Model

• Azure Pipelines YAML Docs

• LLVM for HIP

• AMD GPU ISA

• OpenMP Offloading to AMD

This pipeline and its configuration are provided for open experimentation and CI testing of HIP/ROCm workloads. Usage across different GPU architectures (e.g., RDNA2 vs CDNA2) may require manual configuration adjustments.

This repository and its contributors make no guarantees of performance, correctness, or long-term compatibility. ROCm stack is under active development and may break at any time with upstream changes.

• AMD ROCm Ecosystem (Thanks)

• Open Source Contributors (Yess)

• ChipSet By: AMD corporation (OG)

• Maintained by: Inbora Studio (Me)