ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing. It offers several programming models: HIP (GPU-kernel-based programming), OpenMP/Message Passing Interface (MPI) (directive-based programming), OpenCL.
ROCm is free, libre and open-source software (except the GPU firmware blobs), it is distributed under various licenses. ROCm is short for Radeon Open Compute platform.
Background
The first GPGPU software stack from ATI/AMD was Close to Metal, which became Stream.
ROCm was launched around 2016 with the Boltzmann Initiative. ROCm stack builds upon previous AMD GPU stacks, some tools trace back to GPUOpen, others to the Heterogeneous System Architecture (HSA).
Heterogeneous System Architecture Intermediate Language
HSAIL was aimed at producing a middle-level, hardware-agnostic intermediate representation, that could be JIT-compiled to the eventual hardware (GPU, FPGA...) using the appropriate finalizer. This approach was dropped for ROCm: now it builds only GPU code, using LLVM, and its AMDGPU backend that was upstreamed, although there is still research on such enhanced modularity with LLVM MLIR.
Programming abilities
ROCm as a stack ranges from the kernel driver to the end-user applications. AMD has introductory videos about AMD GCN hardware, and ROCm programming via its learning portal.
One of the best technical introductions about the stack and ROCm/HIP programming, remains, to date, to be found on Reddit.
High-level programming
HIP programming
HIP(HCC) kernel language
Memory allocation
NUMA
Heterogeneous Memory Model and Shared Virtual Memory
ROCm code objects
Compute/Graphics interop
Low-level programming
Hardware support
ROCm is primarily targeted at discrete professional GPUs, but unofficial support includes Vega-family and RDNA 2 consumer GPUs.
Accelerated Processor Units (APU) are "enabled", but not officially supported. Having ROCm functional there is involved.
Professional-grade GPUs
AMD Instinct accelerators are the first-class ROCm citizens, alongside the prosumer Radeon Pro GPU series: they mostly see full support.
The only consumer-grade GPU that has relatively equal support is, as of January 2022, the Radeon VII (GCN 5 - Vega).
Consumer-grade GPUs
Software ecosystem
Learning resources
AMD ROCm product manager Terry Deem gave a tour of the stack.
Third-party integration
The main consumers of the stack are machine learning and high-performance computing/GPGPU applications.
Machine learning
Various Deep Learning frameworks have a ROCm backend: PyTorch TensorFlow ONNX MXNet CuPy MIOpen Caffe Iree (which uses LLVM Multi-Level Intermediate Representation (MLIR))
Supercomputing
ROCm is gaining significant traction in the top 500. ROCm is used with the Exascale supercomputers ElCapitan and Frontier.
Some related software is to be found at AMD Infinity hub.
Other acceleration & graphics interoperation
As of version 3.0, Blender can now use HIP compute kernels for its renderer Cycles.
Other Languages
Julia
Julia has the AMDGPU.jl package, which integrates with LLVM and selects components of the ROCm stack. Instead of compiling code through HIP, AMDGPU.jl uses Julia's compiler to generate LLVM IR directly, which is later consumed by LLVM to generate native device code. AMDGPU.jl uses ROCr's HSA implementation to upload native code onto the device and execute it, similar to how HIP loads its own generated device code.
AMDGPU.jl also supports integration with ROCm's rocBLAS (for BLAS), rocRAND (for random number generation), and rocFFT (for FFTs). Future integration with rocALUTION, rocSOLVER, MIOpen, and certain other ROCm libraries is planned.
Software distribution
Official
ROCm software is currently spread across dozens of public GitHub repositories. Within the main public meta-repository, there is an xml manifest for each official release: using git-repo, a version control tool built on top of git, is the recommended way to synchronize with the stack locally.
AMD starts distributing containerized applications for ROCm, notably scientific research applications gathered under AMD Infinity Hub.
AMD distributes itself packages tailored to various Linux distributions.
Third-party
There is a growing third-party ecosystem packaging ROCm.