AMD ROCm™ Release Notes v3.8.0

September, 2020

This page describes the features, fixed issues, and information about downloading and installing the ROCm software. It also covers known issues in the ROCm v3.8.0 release.

Download AMD ROCm Release Notes PDF

Support for Vega 7nm Workstation

This release extends support to the Vega 7nm Workstation (Vega20 GL-XE) version.

List of Supported Operating Systems

The AMD ROCm platform is designed to support the following operating systems:

  • Ubuntu 20.04 (5.4 and 5.6-oem) and 18.04.5 (Kernel 5.4)

    Note: Ubuntu versions lower than 18 are no longer supported.

  • CentOS 7.8 & RHEL 7.8 (Kernel 3.10.0-1127) (Using devtoolset-7 runtime support)

  • CentOS 8.2 & RHEL 8.2 (Kernel 4.18.0 ) (devtoolset is not required)

  • SLES 15 SP1

AMD ROCm Documentation Updates

AMD ROCm Installation Guide

The AMD ROCm Installation Guide in this release includes:

  • Updated Supported Environments

  • HIP Installation Instructions

  • Tensorflow ROCm Port: Basic Installations on RHEL v8.2


AMD ROCm - HIP Documentation Updates

  • HIP Repository Information

For more information, see https://rocmdocs.amd.com/en/latest/Programming_Guides/Programming-Guides.html#hip-repository-information

ROCm Data Center Tool User Guide

  • Error-Correction Codes Field and Output Documentation

For more information, see


What’s New in This Release

Hipfort-Interface for GPU Kernel Libraries

Hipfort is an interface library for accessing GPU Kernels. It provides support to the AMD ROCm architecture from within the Fortran programming language. Currently, the gfortran and HIP-Clang compilers support hipfort. Note, the gfortran compiler belongs to the GNU Compiler Collection (GCC). While hipfc wrapper calls hipcc for the non-fortran kernel source, gfortran is used for FORTRAN applications that call GPU kernels.

The hipfort interface library is meant for Fortran developers with a focus on gfortran users.

For information on HIPFort installation and examples, see


ROCm Data Center Tool

The ROCm™ Data Center Tool™ simplifies the administration and addresses key infrastructure challenges in AMD GPUs in cluster and datacenter environments. The important features of this tool are:

  • GPU telemetry

  • GPU statistics for jobs

  • Integration with third-party tools

  • Open source

The ROCm Data Center Tool can be used in the standalone mode if all components are installed. The same set of features is also available in a library format that can be used by existing management tools.


Refer to the ROCm Data Center Tool™ User Guide for more details on the different modes of operation.

NOTE: The ROCm Data Center User Guide is intended to provide an overview of ROCm Data Center Tool features and how system administrators and Data Center (or HPC) users can administer and configure AMD GPUs. The guide also provides an overview of its components and open source developer handbook.

For installation information on different distributions, refer to the ROCm Data Center User Guide at


Error Correcting Code Fields in ROCm Data Center Tool

The ROCm Data Center (RDC) tool is enhanced to provide counters to track correctable and uncorrectable errors. While a single bit per word error can be corrected, double bit per word errors cannot be corrected.

The RDC tool now helps monitor and protect undetected memory data corruption. If the system is using ECC- enabled memory, the ROCm Data Center tool can report the error counters to monitor the status of the memory.


For more information, refer to the ROCm Data Center User Guide at:


Static Linking Libraries

The underlying libraries of AMD ROCm are dynamic and are called shared objects (.so) in Linux. The AMD ROCm v3.8 release includes the capability to build static ROCm libraries and link to the applications statically. CMake target files enable linking an application statically to ROCm libraries and each component exports the required dependencies for linking. The static libraries are called Archives (.a) in Linux.

This release also comprises of the requisite changes required for all the components to work in a static environment. The components have been successfully tested for basic functionalities like rocminfo /rocm_bandwidth_test and archives.

In the AMD ROCm v3.8 release, the following libraries support static linking:


Fixed Defects

The following defects are fixed in this release:

  • GPU Kernel C++ Names Not Demangled

  • MIGraphX Fails for fp16 Datatype

  • Issue with Peer-to-Peer Transfers

  • “rocprof” option *“parallel-kernels” Not Supported in this Release

Known Issues

Undefined Reference Issue in Statically Linked Libraries

Libraries and applications statically linked using flags -rtlib=compiler-rt, such as rocBLAS, have an implicit dependency on gcc_s not captured in their CMAKE configuration.

Client applications may require linking with an additional library -lgcc_s to resolve the undefined reference to symbol “_Unwind_ResumeGCC_3.0”.

MIGraphX Pooling Operation Fails for Some Models

MIGraphX does not work for some models with pooling operations and the following error appears:

˜test_gpu_ops_test FAILED”

This issue is currently under investigation and there is no known workaround currently.

MIVisionX Installation Error on CentOS/RHEL8.2 and SLES 15

Installing ROCm on MIVisionX results in the following error on CentOS/RHEL8.2 and SLES 15:

“Problem: nothing provides opencv needed”

As a workaround, install opencv before installing MIVisionX.

Deploying ROCm

AMD hosts both Debian and RPM repositories for the ROCm v3.8.x packages.

For more information on ROCM installation on all platforms, see


Features and enhancements introduced in previous versions of ROCm can be found in Current-Release-Notes.

AMD ROCm Version History

This file contains archived version history information for the ROCm project.

Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.7.x

  • AOMP Enhancements

  • Compatibility with NVIDIA Communications Collective Library v2.7 API

  • Singular Value Decomposition of Bi-diagonal Matrices

  • rocSPARSE_gemmi() Operations for Sparse Matrices

AMD ROCm released a maintenance patch release v3.5.1. For more information about the release see,

Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.5.1

Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.5.0

rocProf Command Line Tool Python Requirement SQLite3 is a required Python module for the rocprof command-line tool. You can install the SQLite3 Python module using the pip utility and set env var ROCP_PYTHON_VERSION to the Python version, which includes the SQLite3 module.

Heterogeneous-Compute Interface for Portability In this release, the Heterogeneous Compute Compiler (HCC) compiler is deprecated and the HIP-Clang compiler is introduced for compiling Heterogeneous-Compute Interface for Portability (HIP) programs.

Radeon Open Compute Common Language Runtime In this release, the HIP runtime API is implemented on top of Radeon Open Compute Common Language Runtime (ROCclr). ROCclr is an abstraction layer that provides the ability to interact with different runtime backends such as ROCr.

OpenCL Runtime The following OpenCL runtime changes are made in this release:

-AMD ROCm OpenCL Runtime extends support to OpenCL2.2 -The developer branch is changed from master to master-next

AMD ROCm GNU Debugger (ROCgdb) The AMD ROCm Debugger (ROCgdb) is the AMD ROCm source-level debugger for Linux based on the GNU Debugger (GDB). It enables heterogeneous debugging on the AMD ROCm platform of an x86-based host architecture along with AMD GPU architectures and supported by the AMD Debugger API Library (ROCdbgapi).

AMD ROCm Debugger API Library The AMD ROCm Debugger API Library (ROCdbgapi) implements an AMD GPU debugger application programming interface (API) that provides the support necessary for a client of the library to control the execution and inspect the state of AMD GPU devices.

rocProfiler Dispatch Callbacks Start Stop API In this release, a new rocprofiler start/stop API is added to enable/disable GPU kernel HSA dispatch callbacks. The callback can be registered with the ‘rocprofiler_set_hsa_callbacks’ API. The API helps you eliminate some profiling performance impact by invoking the profiler only for kernel dispatches of interest. This optimization will result in significant performance gains.

ROCm Communications Collective Library The ROCm Communications Collective Library (RCCL) consists of the following enhancements:

-Re-enable target 0x803 -Build time improvements for the HIP-Clang compiler

NVIDIA Communications Collective Library Version Compatibility AMD RCCL is now compatible with NVIDIA Communications Collective Library (NCCL) v2.6.4 and provides the following features:

Network interface improvements with API v3 Network topology detection Improved CPU type detection Infiniband adaptive routing support

MIOpen Optional Kernel Package Installation MIOpen provides an optional pre-compiled kernel package to reduce startup latency.

New SMI Event Interface and Library An SMI event interface is added to the kernel and ROCm SMI lib for system administrators to get notified when specific events occur. On the kernel side, AMDKFD_IOC_SMI_EVENTS input/output control is enhanced to allow notifications propagation to user mode through the event channel.

API for CPU Affinity A new API is introduced for aiding applications to select the appropriate memory node for a given accelerator(GPU).

Radeon Performance Primitives Library The new Radeon Performance Primitives (RPP) library is a comprehensive high-performance computer vision library for AMD (CPU and GPU) with the HIP and OpenCL backend. The target operating system is Linux.

Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.3.0

Multi-Version Installation Users can install and access multiple versions of the ROCm toolkit simultaneously. Previously, users could install only a single version of the ROCm toolkit.

GPU Process Information A new functionality to display process information for GPUs is available in this release. For example, you can view the process details to determine if the GPU(s) must be reset.

Support for 3D Pooling Layers AMD ROCm is enhanced to include support for 3D pooling layers. The implementation of 3D pooling layers now allows users to run 3D convolutional networks, such as ResNext3D, on AMD Radeon Instinct GPUs.

ONNX Enhancements Open Neural Network eXchange (ONNX) is a widely-used neural net exchange format. The AMD model compiler & optimizer support the pre-trained models in ONNX, NNEF, & Caffe formats. Currently, ONNX versions 1.3 and below are supported.

This release was not productized.

‘Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.1.0

Change in ROCm Installation Directory Structure

A fresh installation of the ROCm toolkit installs the packages in the /opt/rocm-<version> folder. Previously, ROCm toolkit packages were installed in the /opt/rocm folder.

Reliability, Accessibility, and Serviceability Support for Vega 7nm

The Reliability, Accessibility, and Serviceability (RAS) support for Vega7nm is now available.

SLURM Support for AMD GPU

SLURM (Simple Linux Utility for Resource Management) is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters.

Release Notes: https://github.com/RadeonOpenCompute/ROCm/tree/roc-3.0.0

  • Support for CentOS RHEL v7.7

  • Support is extended for CentOS/RHEL v7.7 in the ROCm v3.0 release. For more information about the CentOS/RHEL v7.7 release, see:

  • CentOS/RHEL

  • Initial distribution of AOMP 0.7-5 in ROCm v3.0

The code base for this release of AOMP is the Clang/LLVM 9.0 sources as of October 8th, 2019. The LLVM-project branch used to build this release is AOMP-191008. It is now locked. With this release, an artifact tarball of the entire source tree is created. This tree includes a Makefile in the root directory used to build AOMP from the release tarball. You can use Spack to build AOMP from this source tarball or build manually without Spack.

  • Fast Fourier Transform Updates

The Fast Fourier Transform (FFT) is an efficient algorithm for computing the Discrete Fourier Transform. Fast Fourier transforms are used in signal processing, image processing, and many other areas. The following real FFT performance change is made in the ROCm v3.0 release:

  • Implement efficient real/complex 2D transforms for even lengths.

Other improvements:

  • More 2D test coverage sizes.

  • Fix buffer allocation error for large 1D transforms.

  • C++ compatibility improvements.

MemCopy Enhancement for rocProf In the v3.0 release, the rocProf tool is enhanced with an additional capability to dump asynchronous GPU memcopy information into a .csv file. You can use the ‘-hsa-trace’ option to create the results_mcopy.csv file. Future enhancements will include column labels.

rocBLAS Support for Complex GEMM

The rocBLAS library is a gpu-accelerated implementation of the standard Basic Linear Algebra Subroutines (BLAS). rocBLAS is designed to enable you to develop algorithms, including high performance computing, image analysis, and machine learning.

In the AMD ROCm release v2.10, support is extended to the General Matrix Multiply (GEMM) routine for multiple small matrices processed simultaneously for rocBLAS in AMD Radeon Instinct MI50. Both single and double precision, CGEMM and ZGEMM, are now supported in rocBLAS.

Support for SLES 15 SP1

In the AMD ROCm v2.10 release, support is added for SUSE Linux® Enterprise Server (SLES) 15 SP1. SLES is a modular operating system for both multimodal and traditional IT.

Code Marker Support for rocProfiler and rocTracer Libraries

Code markers provide the external correlation ID for the calling thread. This function indicates that the calling thread is entering and leaving an external API region.

Initial release for Radeon Augmentation Library(RALI)

The AMD Radeon Augmentation Library (RALI) is designed to efficiently decode and process images from a variety of storage formats and modify them through a processing graph programmable by the user. RALI currently provides C API.

Quantization in MIGraphX v0.4

MIGraphX 0.4 introduces support for fp16 and int8 quantization. For additional details, as well as other new MIGraphX features, see MIGraphX documentation.

rocSparse csrgemm

csrgemm enables the user to perform matrix-matrix multiplication with two sparse matrices in CSR format.

Singularity Support

ROCm 2.9 adds support for Singularity container version 2.5.2.

Initial release of rocTX

ROCm 2.9 introduces rocTX, which provides a C API for code markup for performance profiling. This initial release of rocTX supports annotation of code ranges and ASCII markers.

  • Added support for Ubuntu 18.04.3

  • Ubuntu 18.04.3 is now supported in ROCm 2.9.

Support for NCCL2.4.8 API

Implements ncclCommAbort() and ncclCommGetAsyncError() to match the NCCL 2.4.x API

This release is a hotfix for ROCm release 2.7.

  • A defect in upgrades from older ROCm releases has been fixed.

  • rocprofiler –hiptrace and –hsatrace fails to load roctracer library

  • In ROCm 2.7.2, rocprofiler –hiptrace and –hsatrace fails to load roctracer library defect has been fixed.

  • To generate traces, please provide directory path also using the parameter: -d <$directoryPath> for example:

/opt/rocm/bin/rocprof –hsa-trace -d $PWD/traces /opt/rocm/hip/samples/0_Intro/bit_extract/bit_extract All traces and results will be saved under $PWD/traces path

To upgrade, please remove 2.7 completely as specified for ubuntu or for centos/rhel, and install 2.7.2 as per instructions install instructions

Other notes To use rocprofiler features, the following steps need to be completed before using rocprofiler:

Step-1: Install roctracer Ubuntu 16.04 or Ubuntu 18.04: sudo apt install roctracer-dev CentOS/RHEL 7.6: sudo yum install roctracer-dev

Step-2: Add /opt/rocm/roctracer/lib to LD_LIBRARY_PATH New features and enhancements in ROCm 2.7 [rocFFT] Real FFT Functional Improved real/complex 1D even-length transforms of unit stride. Performance improvements of up to 4.5x are observed. Large problem sizes should see approximately 2x.

rocRand Enhancements and Optimizations

Added support for new datatypes: uchar, ushort, half.

Improved performance on “Vega 7nm” chips, such as on the Radeon Instinct MI50

mtgp32 uniform double performance changes due generation algorithm standardization. Better quality random numbers now generated with 30% decrease in performance

Up to 5% performance improvements for other algorithms


Added support for RAS on Radeon Instinct MI50, including:

  • Memory error detection

  • Memory error detection counter

  • ROCm-SMI enhancements

  • Added ROCm-SMI CLI and LIB support for FW version, compute running processes, utilization rates, utilization counter, link error counter, and unique ID.

ROCmInfo enhancements

ROCmInfo was extended to do the following: For ROCr API call errors including initialization determine if the error could be explained by:

ROCk (driver) is not loaded / available User does not have membership in appropriate group - “video” If not above print the error string that is mapped to the returned error code If no error string is available, print the error code in hex Thrust - Functional Support on Vega20

ROCm2.6 contains the first official release of rocThrust and hipCUB. rocThrust is a port of thrust, a parallel algorithm library. hipCUB is a port of CUB, a reusable software component library. Thrust/CUB has been ported to the HIP/ROCm platform to use the rocPRIM library. The HIP ported library works on HIP/ROCm platforms.

Note: rocThrust and hipCUB library replaces https://github.com/ROCmSoftwarePlatform/thrust (hip-thrust), i.e. hip-thrust has been separated into two libraries, rocThrust and hipCUB. Existing hip-thrust users are encouraged to port their code to rocThrust and/or hipCUB. Hip-thrust will be removed from official distribution later this year.

MIGraphX v0.3

MIGraphX optimizer adds support to read models frozen from Tensorflow framework. Further details and an example usage at https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/wiki/Getting-started:-using-the-new-features-of-MIGraphX-0.3

MIOpen 2.0

This release contains several new features including an immediate mode for selecting convolutions, bfloat16 support, new layers, modes, and algorithms.

MIOpenDriver, a tool for benchmarking and developing kernels is now shipped with MIOpen. BFloat16 now supported in HIP requires an updated rocBLAS as a GEMM backend.

Immediate mode API now provides the ability to quickly obtain a convolution kernel.

MIOpen now contains HIP source kernels and implements the ImplicitGEMM kernels. This is a new feature and is currently disabled by default. Use the environmental variable “MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=1” to activation this feature. ImplicitGEMM requires an up to date HIP version of at least 1.5.9211.

A new “loss” catagory of layers has been added, of which, CTC loss is the first. See the API reference for more details. 2.0 is the last release of active support for gfx803 architectures. In future releases, MIOpen will not actively debug and develop new features specifically for gfx803.

System Find-Db in memory cache is disabled by default. Please see build instructions to enable this feature. Additional documentation can be found here: https://rocmsoftwareplatform.github.io/MIOpen/doc/html/

Bloat16 software support in rocBLAS/Tensile

Added mixed precision bfloat16/IEEE f32 to gemm_ex. The input and output matrices are bfloat16. All arithmetic is in IEEE f32.

AMD Infinity Fabric™ Link enablement

The ability to connect four Radeon Instinct MI60 or Radeon Instinct MI50 boards in two hives or two Radeon Instinct MI60 or Radeon Instinct MI50 boards in four hives via AMD Infinity Fabric™ Link GPU interconnect technology has been added.

ROCm-smi features and bug fixes

mGPU & Vendor check

Fix clock printout if DPM is disabled

Fix finding marketing info on CentOS

Clarify some error messages

ROCm-smi-lib enhancements

Documentation updates

Improvements to *name_get functions

RCCL2 Enablement

RCCL2 supports collectives intranode communication using PCIe, Infinity Fabric™, and pinned host memory, as well as internode communication using Ethernet (TCP/IP sockets) and Infiniband/RoCE (Infiniband Verbs). Note: For Infiniband/RoCE, RDMA is not currently supported.

rocFFT enhancements

Added: Debian package with FFT test, benchmark, and sample programs Improved: hipFFT interfaces Improved: rocFFT CPU reference code, plan generation code and logging code

UCX 1.6 support

Support for UCX version 1.6 has been added.

BFloat16 GEMM in rocBLAS/Tensile

Software support for BFloat16 on Radeon Instinct MI50, MI60 has been added. This includes:

Mixed precision GEMM with BFloat16 input and output matrices, and all arithmetic in IEEE32 bit

Input matrix values are converted from BFloat16 to IEEE32 bit, all arithmetic and accumulation is IEEE32 bit. Output values are rounded from IEEE32 bit to BFloat16

Accuracy should be correct to 0.5 ULP

ROCm-SMI enhancements

CLI support for querying the memory size, driver version, and firmware version has been added to ROCm-smi.

[PyTorch] multi-GPU functional support (CPU aggregation/Data Parallel)

Multi-GPU support is enabled in PyTorch using Dataparallel path for versions of PyTorch built using the 06c8aa7a3bbd91cda2fd6255ec82aad21fa1c0d5 commit or later.

rocSparse optimization on Radeon Instinct MI50 and MI60

This release includes performance optimizations for csrsv routines in the rocSparse library.

[Thrust] Preview

Preview release for early adopters. rocThrust is a port of thrust, a parallel algorithm library. Thrust has been ported to the HIP/ROCm platform to use the rocPRIM library. The HIP ported library works on HIP/ROCm platforms.

Note: This library will replace https://github.com/ROCmSoftwarePlatform/thrust in a future release. The package for rocThrust (this library) currently conflicts with version 2.5 package of thrust. They should not be installed together.

Support overlapping kernel execution in same HIP stream

HIP API has been enhanced to allow independent kernels to run in parallel on the same stream.

AMD Infinity Fabric™ Link enablement

The ability to connect four Radeon Instinct MI60 or Radeon Instinct MI50 boards in one hive via AMD Infinity Fabric™ Link GPU interconnect technology has been added.

TensorFlow 2.0 support

ROCm 2.4 includes the enhanced compilation toolchain and a set of bug fixes to support TensorFlow 2.0 features natively

AMD Infinity Fabric™ Link enablement

ROCm 2.4 adds support to connect two Radeon Instinct MI60 or Radeon Instinct MI50 boards via AMD Infinity Fabric™ Link GPU interconnect technology.

Mem usage per GPU

Per GPU memory usage is added to rocm-smi. Display information regarding used/total bytes for VRAM, visible VRAM and GTT, via the –showmeminfo flag

MIVisionX, v1.1 - ONNX

ONNX parser changes to adjust to new file formats

MIGraphX, v0.2

MIGraphX 0.2 supports the following new features:

New Python API

  • Support for additional ONNX operators and fixes that now enable a large set of Imagenet models

  • Support for RNN Operators

  • Support for multi-stream Execution

  • [Experimental] Support for Tensorflow frozen protobuf files

See: Getting-started:-using-the-new-features-of-MIGraphX-0.2 for more details

MIOpen, v1.8 - 3d convolutions and int8

This release contains full 3-D convolution support and int8 support for inference. Additionally, there are major updates in the performance database for major models including those found in Torchvision. See: MIOpen releases

Caffe2 - mGPU support

Multi-gpu support is enabled for Caffe2.

rocTracer library, ROCm tracing API for collecting runtimes API and asynchronous GPU activity traces HIP/HCC domains support is introduced in rocTracer library.

BLAS - Int8 GEMM performance, Int8 functional and performance Introduces support and performance optimizations for Int8 GEMM, implements TRSV support, and includes improvements and optimizations with Tensile.

Prioritized L1/L2/L3 BLAS (functional) Functional implementation of BLAS L1/L2/L3 functions

BLAS - tensile optimization Improvements and optimizations with tensile

MIOpen Int8 support Support for int8

rocSparse Optimization on Vega20 Cache usage optimizations for csrsv (sparse triangular solve), coomv (SpMV in COO format) and ellmv (SpMV in ELL format) are available.

DGEMM and DTRSM Optimization Improved DGEMM performance for reduced matrix sizes (k=384, k=256)

Caffe2 Added support for multi-GPU training

RocTracer v1.0 preview release – ‘rocprof’ HSA runtime tracing and statistics support - Supports HSA API tracing and HSA asynchronous GPU activity including kernels execution and memory copy

Improvements to ROCM-SMI tool - Added support to show real-time PCIe bandwidth usage via the -b/–showbw flag

DGEMM Optimizations - Improved DGEMM performance for large square and reduced matrix sizes (k=384, k=256)

Adds support for RHEL 7.6 / CentOS 7.6 and Ubuntu 18.04.1

Adds support for Vega 7nm, Polaris 12 GPUs

Introduces MIVisionX A comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit. Improvements to ROCm Libraries rocSPARSE & hipSPARSE rocBLAS with improved DGEMM efficiency on Vega 7nm

MIOpen This release contains general bug fixes and an updated performance database Group convolutions backwards weights performance has been improved

RNNs now support fp16 Tensorflow multi-gpu and Tensorflow FP16 support for Vega 7nm TensorFlow v1.12 is enabled with fp16 support PyTorch/Caffe2 with Vega 7nm Support

fp16 support is enabled

Several bug fixes and performance enhancements

Known Issue: breaking changes are introduced in ROCm 2.0 which are not addressed upstream yet. Meanwhile, please continue to use ROCm fork at https://github.com/ROCmSoftwarePlatform/pytorch

Improvements to ROCProfiler tool

Support for Vega 7nm

Support for hipStreamCreateWithPriority

Creates a stream with the specified priority. It creates a stream on which enqueued kernels have a different priority for execution compared to kernels enqueued on normal priority streams. The priority could be higher or lower than normal priority streams.

OpenCL 2.0 support

ROCm 2.0 introduces full support for kernels written in the OpenCL 2.0 C language on certain devices and systems. Applications can detect this support by calling the “clGetDeviceInfo” query function with “parame_name” argument set to “CL_DEVICE_OPENCL_C_VERSION”.

In order to make use of OpenCL 2.0 C language features, the application must include the option “-cl-std=CL2.0” in options passed to the runtime API calls responsible for compiling or building device programs. The complete specification for the OpenCL 2.0 C language can be obtained using the following link: https://www.khronos.org/registry/OpenCL/specs/opencl-2.0-openclc.pdf

Improved Virtual Addressing (48 bit VA) management for Vega 10 and later GPUs

Fixes Clang AddressSanitizer and potentially other 3rd-party memory debugging tools with ROCm

Small performance improvement on workloads that do a lot of memory management

Removes virtual address space limitations on systems with more VRAM than system memory Kubernetes support

RDMA(MPI) support on Vega 7nm

Support ROCnRDMA based on Mellanox InfiniBand

Improvements to HCC

Improved link time optimization

Improvements to ROCProfiler tool

General bug fixes and implemented versioning APIs

New features and enhancements in ROCm 1.9.2

RDMA(MPI) support on Vega 7nm

Support ROCnRDMA based on Mellanox InfiniBand

Improvements to HCC

Improved link time optimization

Improvements to ROCProfiler tool

General bug fixes and implemented versioning APIs

Critical bug fixes

Added DPM support to Vega 7nm

Dynamic Power Management feature is enabled on Vega 7nm.

Fix for ‘ROCm profiling’ that used to fail with a “Version mismatch between HSA runtime and libhsa-runtime-tools64.so.1” error

Preview for Vega 7nm Enables developer preview support for Vega 7nm

System Management Interface Adds support for the ROCm SMI (System Management Interface) library, which provides monitoring and management capabilities for AMD GPUs.

Improvements to HIP/HCC Support for gfx906

Added deprecation warning for C++AMP. This will be the last version of HCC supporting C++AMP.

Improved optimization for global address space pointers passing into a GPU kernel

Fixed several race conditions in the HCC runtime

Performance tuning to the unpinned copy engine

Several codegen enhancement fixes in the compiler backend

Preview for rocprof Profiling Tool

Developer preview (alpha) of profiling tool rocProfiler. It includes a command-line front-end, rpl_run.sh, which enables:

Cmd-line tool for dumping public per kernel perf-counters/metrics and kernel timestamps

Input file with counters list and kernels selecting parameters

Multiple counters groups and app runs supported

Output results in CSV format

The tool can be installed from the rocprofiler-dev package. It will be installed into: /opt/rocm/bin/rpl_run.sh

Preview for rocr Debug Agent rocr_debug_agent

The ROCr Debug Agent is a library that can be loaded by ROCm Platform Runtime to provide the following functionality:

Print the state for wavefronts that report memory violation or upon executing a “s_trap 2” instruction. Allows SIGINT (ctrl c) or SIGTERM (kill -15) to print wavefront state of aborted GPU dispatches. It is enabled on Vega10 GPUs on ROCm1.9. The ROCm1.9 release will install the ROCr Debug Agent library at /opt/rocm/lib/librocr_debug_agent64.so

New distribution support Binary package support for Ubuntu 18.04 ROCm 1.9 is ABI compatible with KFD in upstream Linux kernels. Upstream Linux kernels support the following GPUs in these releases: 4.17: Fiji, Polaris 10, Polaris 11 4.18: Fiji, Polaris 10, Polaris 11, Vega10

Some ROCm features are not available in the upstream KFD:

More system memory available to ROCm applications Interoperability between graphics and compute RDMA IPC To try ROCm with an upstream kernel, install ROCm as normal, but do not install the rock-dkms package. Also add a udev rule to control /dev/kfd permissions:

echo ‘SUBSYSTEM==”kfd”, KERNEL==”kfd”, TAG+=”uaccess”, GROUP=”video”’ | sudo tee /etc/udev/rules.d/70-kfd.rules

ROCm 1.8.3 is a minor update meant to fix compatibility issues on Ubuntu releases running kernel 4.15.0-33

DKMS driver installation

Debian packages are provided for DKMS on Ubuntu

RPM packages are provided for CentOS/RHEL 7.4 and 7.5

See the ROCT-Thunk-Interface and ROCK-Kernel-Driver for additional documentation on driver setup

New distribution support

Binary package support for Ubuntu 16.04 and 18.04

Binary package support for CentOS 7.4 and 7.5

Binary package support for RHEL 7.4 and 7.5

Improved OpenMPI via UCX support

UCX support for OpenMPI


DKMS driver installation

New driver installation uses Dynamic Kernel Module Support (DKMS)

Only amdkfd and amdgpu kernel modules are installed to support AMD hardware

Currently only Debian packages are provided for DKMS (no Fedora suport available)

See the ROCT-Thunk-Interface and ROCK-Kernel-Driver for additional documentation on driver setup

Developer preview of the new OpenCL 1.2 compatible language runtime and compiler

OpenCL 2.0 compatible kernel language support with OpenCL 1.2 compatible runtime

Supports offline ahead of time compilation today; during the Beta phase we will add in-process/in-memory compilation.

Binary Package support for Ubuntu 16.04

Binary Package support for Fedora 24 is not currently available

Dropping binary package support for Ubuntu 14.04, Fedora 23

IPC support


The information contained herein is for informational purposes only and is subject to change without notice. While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. Advanced Micro Devices, Inc. makes no representations or warranties with respect to the accuracy or completeness of the contents of this document, and assumes no liability of any kind, including the implied warranties of noninfringement, merchantability or fitness for particular purposes, with respect to the operation or use of AMD hardware, software or other products described herein. No license, including implied or arising by estoppel, to any intellectual property rights is granted by this document. Terms and limitations applicable to the purchase or use of AMD’s products are as set forth in a signed agreement between the parties or in AMD’s Standard Terms and Conditions of Sale. S AMD, the AMD Arrow logo, Radeon, Ryzen, Epyc, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Google® is a registered trademark of Google LLC. PCIe® is a registered trademark of PCI-SIG Corporation. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. Ubuntu and the Ubuntu logo are registered trademarks of Canonical Ltd. Other product names used in this publication are for identification purposes only and may be trademarks of their respective companies.