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README.md

@@ -1,20 +1,21 @@
 
-# AMD ROCm Release Notes v3.7.0
+# AMD ROCm Release Notes v3.8.0
 
 This page describes the features, fixed issues, and information about downloading and installing the ROCm software.
-It also covers known issues and deprecated features in this release.
+It also covers known issues in this release.
 
 - [Supported Operating Systems and Documentation Updates](#Supported-Operating-Systems-and-Documentation-Updates)
   * [Supported Operating Systems](#Supported-Operating-Systems)
   * [AMD ROCm Documentation Updates](#AMD-ROCm-Documentation-Updates)
    
 - [What\'s New in This Release](#Whats-New-in-This-Release)
-  * [AOMP Enhancements](#AOMP-Enhancements)
-  * [Compatibility with NVIDIA Communications Collective Library v2\.7 API](#Compatibility-with-NVIDIA-Communications-Collective-Library-v27-API)
-  * [Singular Value Decomposition of Bi\-diagonal Matrices](#Singular-Value-Decomposition-of-Bi-diagonal-Matrices)
-  * [rocSPARSE_gemmi\() Operations for Sparse Matrices](#rocSPARSE_gemmi-Operations-for-Sparse-Matrices)
+  * [Hipfort-Interface for GPU Kernel Libraries](#Hipfort-Interface-for-GPU-Kernel-Libraries)
+  * [ROCm Data Center Tool](#ROCm-Data-Center-Tool)
+  * [Error-Correcting Code Fields in ROCm Data Center Tool](#Error-Correcting-Code-Fields-in-ROCm-Data-Center-Tool)
+  * [Static Linking Libraries](#Static-Linking-Libraries)
     
- 
+- [Fixed Defects](#Fixed-Defects)
+
 - [Known Issues](#Known-Issues)
 
 - [Deploying ROCm](#Deploying-ROCm)
@@ -29,28 +30,33 @@ It also covers known issues and deprecated features in this release.
 
 # Supported Operating Systems 
 
-## Support for Ubuntu 20.04 
+## Support for Vega 7nm Workstation
 
-
-In this release, AMD ROCm extends support to Ubuntu 20.04, including dual-kernel.
+This release extends support to the Vega 7nm Workstation (Vega20 GL-XE) version.
 
 ## List of Supported Operating Systems
 
-The AMD ROCm v3.7.x platform is designed to support the following operating systems:
+The AMD ROCm platform is designed to support the following operating systems:
 
-* Ubuntu 20.04 and 18.04.4 (Kernel 5.3)	
+* Ubuntu 20.04 (5.4 and 5.6-oem) and 18.04.5 (Kernel 5.4)	
 * 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
 
-## Fresh Installation of AMD ROCm v3.7 Recommended
-A fresh and clean installation of AMD ROCm v3.7 is recommended. An upgrade from previous releases to AMD ROCm v3.7 is not supported.
+## Fresh Installation of AMD ROCm v3.8 Recommended
+A fresh and clean installation of AMD ROCm v3.8 is recommended. An upgrade from previous releases to AMD ROCm v3.8 is not supported.
 
 For more information, refer to the AMD ROCm Installation Guide at:
 https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html
 
 **Note**: AMD ROCm release v3.3 or prior releases are not fully compatible with AMD ROCm v3.5 and higher versions. You must perform a fresh ROCm installation if you want to upgrade from AMD ROCm v3.3 or older to 3.5 or higher versions and vice-versa.
 
+**Note**: *render group* is required only for Ubuntu v20.04. For all other ROCm supported operating systems, continue to use *video group*.
+
+* For ROCm v3.5 and releases thereafter,the *clinfo* path is changed to -  */opt/rocm/opencl/bin/clinfo*.
+
+* For ROCm v3.3 and older releases, the *clinfo* path remains unchanged -  */opt/rocm/opencl/bin/x86_64/clinfo*.
+
 
 # AMD ROCm Documentation Updates
 
@@ -60,36 +66,27 @@ The AMD ROCm Installation Guide in this release includes:
 
 * Updated Supported Environments
 * HIP Installation Instructions
+* Tensorflow ROCm Port: Basic Installations on RHEL v8.2
 
 https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html
 
-
 ## AMD ROCm - HIP Documentation Updates
 
-### Texture and Surface Functions
-The documentation for Texture and Surface functions is updated and available at:
+* HIP Repository Information
 
-https://rocmdocs.amd.com/en/latest/Programming_Guides/Kernel_language.html
+For more information, see 
 
-### Warp Shuffle Functions
-The documentation for Warp Shuffle functions is updated and available at:
+https://rocmdocs.amd.com/en/latest/Programming_Guides/Programming-Guides.html#hip-repository-information
 
-https://rocmdocs.amd.com/en/latest/Programming_Guides/Kernel_language.html
+## ROCm Data Center Tool User Guide
 
-### Compiler Defines and Environment Variables
-The documentation for the updated HIP Porting Guide is available at:
+* Error-Correction Codes Field and Output Documentation
 
-https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-porting-guide.html#hip-porting-guide
+For more information, refer to the AMD ROCm Data Center User Guide at
 
+https://github.com/RadeonOpenCompute/ROCm/blob/master/AMD_ROCm_DataCenter_Tool_User_Guide.pdf
 
-## AMD ROCm Debug Agent
-
-ROCm Debug Agent Library 
-
-https://rocmdocs.amd.com/en/latest/ROCm_Tools/rocm-debug-agent.html
-
-
-## General AMD ROCm Documentatin Links
+## General AMD ROCm Documentation Links
 
 Access the following links for more information:
 
@@ -113,209 +110,96 @@ Access the following links for more information:
 
 # What\'s New in This Release
 
-## AOMP ENHANCEMENTS
+## Hipfort-Interface for GPU Kernel Libraries
 
-AOMP is a scripted build of LLVM. It supports OpenMP target offload on AMD GPUs. Since AOMP is a Clang/LLVM compiler, it also supports GPU offloading with HIP, CUDA, and OpenCL.
+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 following enhancements are made for AOMP in this release: 
-* OpenMP 5.0 is enabled by default. You can use -fopenmp-version=45 for OpenMP 4.5 compliance
-* Restructured to include the ROCm compiler
-* B=Bitcode search path using hip policy HIP_DEVICE_LIB_PATH and hip-devic-lib command line option to enable global_free for kmpc_impl_free
+The hipfort interface library is meant for Fortran developers with a focus on gfortran users. 
 
-Restructured hostrpc, including:
-* Replaced hostcall register functions with handlePayload(service, payload). Note, handlPayload has a simple switch to call the correct service handler function.
-* Removed the WITH_HSA macro
-* Moved the hostrpc stubs and host fallback functions into a single library and the include file. This enables the stubs openmp cpp source instead of hip and reorganizes the directory openmp/libomptarget/hostrpc.
-* Moved hostrpc_invoke.cl to DeviceRTLs/amdgcn.
-* Generalized the vargs processing in printf to work for any vargs function to execute on the host, including a vargs function that uses a function pointer.
-* Reorganized files, added global_allocate and global_free.
-* Fixed llvm TypeID enum to match the current upstream llvm TypeID.
-* Moved strlen_max function inside the declare target #ifdef _DEVICE_GPU in hostrpc.cpp to resolve linker failure seen in pfspecifier_str smoke test.
-* Fixed AOMP_GIT_CHECK_BRANCH in aomp_common_vars to not block builds in Red Hat if the repository is on a specific commit hash.
-* Simplified and reduced the size of openmp host runtime
-* Switched to default OpenMP 5.0
+For information on HIPFort installation and examples, see
+https://github.com/ROCmSoftwarePlatform/hipfort
+  
 
-For more information, see https://github.com/ROCm-Developer-Tools/aomp
-     
+## ROCm Data Center Tool 
 
-## ROCm COMMUNICATIONS COLLECTIVE LIBRARY
+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:
 
-### Compatibility with NVIDIA Communications Collective Library v2\.7 API
+* GPU telemetry  
 
-ROCm Communications Collective Library (RCCL) is now compatible with the NVIDIA Communications Collective Library (NCCL) v2.7 API.
+* GPU statistics for jobs 
 
-RCCL (pronounced "Rickle") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, gather, scatter, and all-to-all. There is also initial support for direct GPU-to-GPU send and receive operations. It has been optimized to achieve high bandwidth on platforms using PCIe, xGMI as well as networking using InfiniBand Verbs or TCP/IP sockets. RCCL supports an arbitrary number of GPUs installed in a single node or multiple nodes, and can be used in either single- or multi-process (e.g., MPI) applications.
+* Integration with third-party tools 
 
-The collective operations are implemented using ring and tree algorithms and have been optimized for throughput and latency. For best performance, small operations can be either batched into larger operations or aggregated through the API.
+* Open source 
 
-For more information about RCCL APIs and compatibility with NCCL v2.7, see
-https://rccl.readthedocs.io/en/develop/index.html
+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. 
+
+![ScreenShot](https://github.com/Rmalavally/ROCm/blob/master/RDCComponentsrevised.png)
 
 
-## Singular Value Decomposition of Bi\-diagonal Matrices
+Refer to the ROCm Data Center Tool™ User Guide for more details on the different modes of operation.  
 
-Rocsolver_bdsqr now computes the Singular Value Decomposition (SVD) of bi-diagonal matrices. It is an auxiliary function for the SVD of general matrices (function rocsolver_gesvd). 
+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. 
 
-BDSQR computes the singular value decomposition (SVD) of a n-by-n bidiagonal matrix B.
+For installation information on different distributions, refer to the ROCm Data Center User Guide at
 
-The SVD of B has the following form:
-
-B = Ub * S * Vb'
-where 
-•	S is the n-by-n diagonal matrix of singular values of B
-•	the columns of Ub are the left singular vectors of B
-•	the columns of Vb are its right singular vectors
-
-The computation of the singular vectors is optional; this function accepts input matrices U (of size nu-by-n) and V (of size n-by-nv) that are overwritten with U*Ub and Vb’*V. If nu = 0 no left vectors are computed; if nv = 0 no right vectors are computed.
-
-Optionally, this function can also compute Ub’*C for a given n-by-nc input matrix C.
-
-PARAMETERS
-
-•	[in] handle: rocblas_handle.
-
-•	[in] uplo: rocblas_fill.
-
-Specifies whether B is upper or lower bidiagonal.
-
-•	[in] n: rocblas_int. n >= 0.
-
-The number of rows and columns of matrix B.
-
-•	[in] nv: rocblas_int. nv >= 0. 
-
-The number of columns of matrix V.
-
-•	[in] nu: rocblas_int. nu >= 0. 
-
-The number of rows of matrix U.
-
-•	[in] nc: rocblas_int. nu >= 0. 
-
-The number of columns of matrix C.
-
-•	[inout] D: pointer to real type. Array on the GPU of dimension n.
-
-On entry, the diagonal elements of B. On exit, if info = 0, the singular values of B in decreasing order; if info > 0, the diagonal elements of a bidiagonal matrix orthogonally equivalent to B.
-
-•	[inout] E: pointer to real type. Array on the GPU of dimension n-1.
-
-On entry, the off-diagonal elements of B. On exit, if info > 0, the off-diagonal elements of a bidiagonal matrix orthogonally equivalent to B (if info = 0 this matrix converges to zero).
-
-•	[inout] V: pointer to type. Array on the GPU of dimension ldv*nv.
-
-On entry, the matrix V. On exit, it is overwritten with Vb’*V. (Not referenced if nv = 0).
-
-•	[in] ldv: rocblas_int. ldv >= n if nv > 0, or ldv >=1 if nv = 0.
-
-Specifies the leading dimension of V.
-
-•	[inout] U: pointer to type. Array on the GPU of dimension ldu*n.
-
-On entry, the matrix U. On exit, it is overwritten with U*Ub. (Not referenced if nu = 0).
-
-•	[in] ldu: rocblas_int. ldu >= nu.
-
-Specifies the leading dimension of U.
-
-•	[inout] C: pointer to type. Array on the GPU of dimension ldc*nc.
-
-On entry, the matrix C. On exit, it is overwritten with Ub’*C. (Not referenced if nc = 0).
-
-•	[in] ldc: rocblas_int. ldc >= n if nc > 0, or ldc >=1 if nc = 0.
-
-Specifies the leading dimension of C.
-
-•	[out] info: pointer to a rocblas_int on the GPU.
-
-If info = 0, successful exit. If info = i > 0, i elements of E have not converged to zero.
-
-For more information, see
-https://rocsolver.readthedocs.io/en/latest/userguide_api.html#rocsolver-type-bdsqr
+https://github.com/RadeonOpenCompute/ROCm/blob/master/AMD_ROCm_DataCenter_Tool_User_Guide.pdf
 
 
-### rocSPARSE_gemmi\() Operations for Sparse Matrices
+### Error Correcting Code Fields in ROCm Data Center Tool
 
-This enhancement provides a dense matrix sparse matrix multiplication using the CSR storage format.
-rocsparse_gemmi multiplies the scalar αα with a dense m×km×k matrix AA and the sparse k×nk×n matrix BB defined in the CSR storage format, and adds the result to the dense m×nm×n matrix CC that is multiplied by the scalar ββ, such that
-C:=α⋅op(A)⋅op(B)+β⋅CC:=α⋅op(A)⋅op(B)+β⋅C
-with
+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. 
 
-op(A)=⎧⎩⎨⎪⎪A,AT,AH,if trans_A == rocsparse_operation_noneif trans_A == rocsparse_operation_transposeif trans_A == rocsparse_operation_conjugate_transposeop(A)={A,if trans_A == rocsparse_operation_noneAT,if trans_A == rocsparse_operation_transposeAH,if trans_A == rocsparse_operation_conjugate_transpose
+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. 
 
-and
+![ScreenShot](https://github.com/Rmalavally/ROCm/blob/master/forweb.PNG)
 
-op(B)=⎧⎩⎨⎪⎪B,BT,BH,if trans_B == rocsparse_operation_noneif trans_B == rocsparse_operation_transposeif trans_B == rocsparse_operation_conjugate_transposeop(B)={B,if trans_B == rocsparse_operation_noneBT,if trans_B == rocsparse_operation_transposeBH,if trans_B == rocsparse_operation_conjugate_transpose
-Note: This function is non-blocking and executed asynchronously with the host. It may return before the actual computation has finished.
+## Static Linking Libraries 
 
-For more information and examples, see
-https://rocsparse.readthedocs.io/en/master/usermanual.html#rocsparse-gemmi
- 
+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: 
+
+![ScreenShot](https://github.com/Rmalavally/ROCm/blob/master/staticlinkinglib.PNG)
+
+# 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 
-The following are the known issues in this release.
 
-## (AOMP) ‘Undefined Hidden Symbol’ Linker Error Causes Compilation Failure in HIP
+## Undefined Reference Issue in Statically Linked Libraries
 
-The HIP example device_lib fails to compile due to unreferenced symbols with Link Time Optimization resulting in ‘undefined hidden symbol’ errors. 
+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.  
 
-This issue is under investigation and there is no known workaround at this time.
+Client applications may require linking with an additional library -lgcc_s to resolve the undefined reference to symbol '_Unwind_Resume@@GCC_3.0'.
 
+## MIGraphX Pooling Operation Fails for Some Models
 
-## MIGraphX Fails for fp16 Datatype
-The MIGraphX functionality does not work for the fp16 datatype.  
+MIGraphX does not work for some models with pooling operations and the following error appears:
 
-The following workaround is recommended: 
+*‘test_gpu_ops_test FAILED’*
 
-Use the AMD ROCm v3.3 of MIGraphX 
+This issue is currently under investigation and there is no known workaround currently. 
 
-Or
+## MIVisionX Installation Error on CentOS/RHEL8.2 and SLES 15
 
-Build MIGraphX v3.7 from the source using AMD ROCm v3.3
+Installing ROCm on MIVisionX results in the following error on CentOS/RHEL8.2 and SLES 15:
 
-## Missing Google Test Installation May Cause RCCL Unit Test Compilation Failure
-Users of the RCCL install.sh script may encounter an RCCL unit test compilation error. It is recommended to use CMAKE directly instead of install.sh to compile RCCL. Ensure Google Test 1.10+ is available in the CMAKE search path.
-
-
-As a workaround, use the latest RCCL from the GitHub development branch at:
-https://github.com/ROCmSoftwarePlatform/rccl/pull/237
-
-
-## Issue with Peer-to-Peer Transfers 
-Using peer-to-peer (P2P) transfers on systems without the hardware P2P assistance may produce incorrect results.
-
-Ensure the hardware supports peer-to-peer transfers and enable the peer-to-peer setting in the hardware to resolve this issue. 
-
-
-## Partial Loss of Tracing Events for Large Applications
-An internal tracing buffer allocation issue can cause a partial loss of some tracing events for large applications.
-
-As a workaround, rebuild the roctracer/rocprofiler libraries from the GitHub ‘roc-3.7’ branch at:
-•	https://github.com/ROCm-Developer-Tools/rocprofiler
-•	https://github.com/ROCm-Developer-Tools/roctracer
-
-
-## GPU Kernel C++ Names Not Demangled
-GPU kernel C++ names in the profiling traces and stats produced by ‘—hsa-trace’ option are not demangled.
-
-As a workaround, users may choose to demangle the GPU kernel C++ names as required.
-
-
-## ‘rocprof’ option ‘--parallel-kernels’ Not Supported in This Release
-
-‘rocprof’ option ‘--parallel-kernels’ is available in the options list, however,  it is not fully validated and supported in this release.
-
-## Random Soft Hang Observed When Running ResNet-Based Models
-
-A random soft hang is observed when running ResNet-based models for a loop run of more than 25 to 30 hours.  The issue is observed on both PyTorch and TensorFlow frameworks.
-You can terminate the unresponsive process to temporarily resolve the issue.
-
-There is no known workaround at this time.
+*"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 v3.7.x packages. 
+AMD hosts both Debian and RPM repositories for the ROCm v3.8.x packages. 
 
 For more information on ROCM installation on all platforms, see
 
@@ -394,6 +278,7 @@ does not require or take advantage of PCIe Atomics. However, we still recommend
 from the list provided above for compatibility purposes.
 
 #### Not supported or limited support under ROCm
+
 ##### Limited support
 
 * ROCm 2.9.x should support PCIe 2.0 enabled CPUs such as the AMD Opteron, Phenom, Phenom II, Athlon, Athlon X2, Athlon II and older Intel Xeon and Intel Core Architecture and Pentium CPUs. However, we have done very limited testing on these configurations, since our test farm has been catering to CPUs listed above. This is where we need community support. _If you find problems on such setups, please report these issues_.
@@ -445,4 +330,4 @@ For users that have the option of using either AMD's or the upstreamed driver, t
 
 For an updated version of the software stack for AMD GPU, see
 
-https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html#machine-learning-and-high-performance-computing-software-stack-for-amd-gpu-v3-5-0
+https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html#software-stack-for-amd-gpu

default.xml

@@ -12,7 +12,7 @@
             fetch="https://github.com/GPUOpen-Tools/" />
     <remote name="KhronosGroup"
             fetch="https://github.com/KhronosGroup/" />
-    <default revision="refs/tags/rocm-3.7.0"
+    <default revision="refs/tags/rocm-3.8.0"
              remote="roc-github"
              sync-c="true"
              sync-j="4" />
@@ -39,7 +39,7 @@
     <project name="ROCm-Device-Libs" />
     <project name="atmi" />
     <project name="ROCm-CompilerSupport" />
-    <project name="rocr_debug_agent" remote="rocm-devtools" revision="refs/tags/roc-3.7.0" />
+    <project name="rocr_debug_agent" remote="rocm-devtools" />
     <project name="rocm_bandwidth_test" />
     <project name="RCP" remote="gpuopen-tools" revision="3a49405a1500067c49d181844ec90aea606055bb" />
     <!-- gdb projects -->
@@ -61,7 +61,7 @@
     <project name="rocThrust" remote="rocm-swplat" />
     <project name="hipCUB" remote="rocm-swplat" />
     <project name="rocPRIM" remote="rocm-swplat" />
-    <project name="AMDMIGraphX" remote="rocm-swplat" revision="e66968a25f9342a28af1157b06cbdbf8579c5519" />
+    <project name="hipfort" remote="rocm-swplat" />
     <project name="ROCmValidationSuite" remote="rocm-devtools" />
     <!-- Projects for AOMP -->
     <project name="ROCT-Thunk-Interface" path="aomp/roct-thunk-interface" remote="roc-github" />

version_history.md

@@ -1,503 +0,0 @@
-## ROCm Version History
-This file contains archived version history information for the [ROCm project](https://github.com/RadeonOpenCompute/ROCm)
-
-### Current ROCm Version: 3.3
-- [New features and enhancements in ROCm v3.1](#new-features-and-enhancements-in-rocm-v31)
-- [New features and enhancements in ROCm v3.0](#new-features-and-enhancements-in-rocm-v30)
-- [New features and enhancements in ROCm v2.10](#new-features-and-enhancements-in-rocm-v210)
-- [New features and enhancements in ROCm 2.9](#new-features-and-enhancements-in-rocm-29)
-- [New features and enhancements in ROCm 2.8](#new-features-and-enhancements-in-rocm-28)
-- [New features and enhancements in ROCm 2.7.2](#new-features-and-enhancements-in-rocm-272)
-- [New features and enhancements in ROCm 2.7](#new-features-and-enhancements-in-rocm-27)
-- [New features and enhancements in ROCm 2.6](#new-features-and-enhancements-in-rocm-26)
-- [New features and enhancements in ROCm 2.5](#new-features-and-enhancements-in-rocm-25)
-- [New features and enhancements in ROCm 2.4](#new-features-and-enhancements-in-rocm-24)
-- [New features and enhancements in ROCm 2.3](#new-features-and-enhancements-in-rocm-23)
-- [New features and enhancements in ROCm 2.2](#new-features-and-enhancements-in-rocm-22)
-- [New features and enhancements in ROCm 2.1](#new-features-and-enhancements-in-rocm-21)
-- [New features and enhancements in ROCm 2.0](#new-features-and-enhancements-in-rocm-20)
-- [New features and enhancements in ROCm 1.9.2](#new-features-and-enhancements-in-rocm-192)
-- [New features and enhancements in ROCm 1.9.2](#new-features-and-enhancements-in-rocm-192-1)
-- [New features and enhancements in ROCm 1.9.1](#new-features-and-enhancements-in-rocm-191)
-- [New features and enhancements in ROCm 1.9.0](#new-features-and-enhancements-in-rocm-190)
-- [New features as of ROCm 1.8.3](#new-features-as-of-rocm-183)
-- [New features as of ROCm 1.8](#new-features-as-of-rocm-18)
-- [New Features as of ROCm 1.7](#new-features-as-of-rocm-17)
-- [New Features as of ROCm 1.5](#new-features-as-of-rocm-15)
-
-
-## New features and enhancements in ROCm v3.2
-The AMD ROCm v3.2 release was not productized.
-
-## New features and enhancements in ROCm v3.1
-### 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.
-
-
-## New features and enhancements in ROCm v3.0
-### Support for CentOS RHEL v7.7 <a id="centos-anchor"></a> 
-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](https://centos.org/forums/viewtopic.php?t=71657)
-
-
-### Initial distribution of AOMP 0.7-5 in ROCm v3.0 <a id="aomp-anchor"></a> 
-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.
-
-For more information about AOMP 0.7-5, see: [AOMP](https://github.com/ROCm-Developer-Tools/aomp/tree/roc-3.0.0)
-
-
-### 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.
-
-
-### New features and enhancements in ROCm v2.10
-#### 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.
-
-### New features and enhancements in ROCm 2.9
-
-#### 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](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/wiki/Getting-started:-using-the-new-features-of-MIGraphX-0.4).
-
-#### 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.  For an example, see this [code](https://github.com/ROCm-Developer-Tools/roctracer/blob/amd-master/test/MatrixTranspose_test/MatrixTranspose.cpp).
-
-#### Added support for Ubuntu 18.04.3
-Ubuntu 18.04.3 is now supported in ROCm 2.9.
-
-
-
-### New features and enhancements in ROCm 2.8
-
-#### Support for NCCL2.4.8 API
-Implements ncclCommAbort() and ncclCommGetAsyncError() to match the NCCL 2.4.x API
-
-### New features and enhancements in ROCm 2.7.2
-
-This release is a hotfix for ROCm release 2.7.
-
-#### Issues fixed in ROCm 2.7.2
-
-##### 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:
-```shell
-/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
-
-#### Upgrading from ROCm 2.7 to 2.7.2
-
-To upgrade, please remove 2.7 completely as specified [for ubuntu](#how-to-uninstall-from-ubuntu-1604-or-Ubuntu-1804) or [for centos/rhel](#how-to-uninstall-rocm-from-centosrhel-76), and install 2.7.2 as per instructions [install instructions](#installing-from-amd-rocm-repositories)
-
-#### 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:
-
-  ```shell
-  sudo apt install roctracer-dev
-  ```
-
-######  CentOS/RHEL 7.6:
-
-  ```shell
-  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
-
-#### RAS
-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.
-
-### New features and enhancements in ROCm 2.6
-
-#### 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
-
-### New features and enhancements in ROCm 2.5
-
-#### 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&#x2122; 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.
-### New features and enhancements in ROCm 2.4
-
-#### 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&#x2122; Link enablement
-ROCm 2.4 adds support to connect two Radeon Instinct MI60 or Radeon Instinct MI50 boards via AMD Infinity Fabric&#x2122; Link GPU interconnect technology.
-
-### New features and enhancements in ROCm 2.3
-
-#### 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](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/wiki/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](https://github.com/ROCmSoftwarePlatform/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
-
-### New features and enhancements in ROCm 2.2
-
-#### 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
-
-### New features and enhancements in ROCm 2.1
-
-#### 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)
-
-### New features and enhancements in ROCm 2.0
-
-#### 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
-
-### 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
-
-### 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
-
-### New features and enhancements in ROCm 1.9.1
-#### 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
-
-### New features and enhancements in ROCm 1.9.0
-
-#### 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
-```
-
-### New features as of ROCm 1.8.3
-
-* ROCm 1.8.3 is a minor update meant to fix compatibility issues on Ubuntu releases running kernel 4.15.0-33
-
-### New features as of ROCm 1.8
-
-#### DKMS driver installation
-
- * Debian packages are provided for DKMS on Ubuntu
- * RPM packages are provided for CentOS/RHEL 7.4 and 7.5 support
- * See the [ROCT-Thunk-Interface](https://github.com/RadeonOpenCompute/ROCT-Thunk-Interface/tree/roc-1.8.x) and [ROCK-Kernel-Driver](https://github.com/RadeonOpenCompute/ROCK-Kernel-Driver/tree/roc-1.8.x) 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
- * ROCm RDMA
-
-### New Features as of ROCm 1.7
-
-#### 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](https://github.com/RadeonOpenCompute/ROCT-Thunk-Interface/tree/roc-1.7.x) and [ROCK-Kernel-Driver](https://github.com/RadeonOpenCompute/ROCK-Kernel-Driver/tree/roc-1.7.x) for additional documentation on driver setup
-
-### New Features as of ROCm 1.5
-
-#### 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