# ROCm Documentation has moved to docs.amd.com

AMD ROCm™ v4.3.1 Point Release Notes

This document describes the features, fixed issues, and information about downloading and installing the AMD ROCm™ software.

It also covers known issues in this release.

List of Supported Operating Systems

The AMD ROCm platform supports the following operating systems:

OS

Kernel

SLES15 SP3

5.3.18-24.49

RHEL 7.9

3.10.0-1160.6.1.el7

CentOS 7.9

3.10.0-1127

RHEL 8.4

4.18.0-193.1.1.el8

CentOS 8.3

4.18.0-193.el8

Ubuntu 18.04.5

5.4.0-71-generic

Ubuntu 20.04.2

5.8.0-48-generic

What’s New in This Release

The ROCm v4.3.1 release consists of the following enhancements:

Support for RHEL V8.4

This release extends support for RHEL v8.4.

Support for SLES V15 Service Pack 3

This release extends support for SLES v15 SP3.

Pass Manager Update

In the AMD ROCm 4.3.1 release, the ROCm compiler uses the legacy pass manager, by default, to provide a better performance experience with some workloads.

Previously, in ROCm v4.3, the default choice for the ROCm compiler was the new pass manager.

For more information about legacy and new pass managers, see http://llvm.org.

Known Issues in This Release

General Userspace and Application Freeze on MI25

For some workloads on MI25, general user space and application freeze are observed, and the GPU resets intermittently. Note, the freeze may take hours to reproduce.

This issue is under active investigation, and no workarounds are available currently.

hipRTC - File Not Found Error

hipRTC may fail, and users may encounter the following error:

<built-in>:1:10: fatal error: '__clang_hip_runtime_wrapper.h' file not found
#include "__clang_hip_runtime_wrapper.h"

Suggested Workarounds

  • Set LLVM_PATH in the environment to /llvm. Note, if ROCm is installed at the default location, then LLVM_PATH must be set to /opt/rocm/llvm.

  • Add “-I /llvm/lib/clang/13.0.0/include/” to compiler options in the call to hiprtcCompileProgram (). Note, this workaround requires the following changes in the code:

    // set NUM_OPTIONS to one more than the number of options that was previously required
const char* options[NUM_OPTIONS];
// fill other options[] here
std::string sarg = "-I/opt/rocm/llvm/lib/clang/13.0.0/include/";
options[NUM_OPTIONS - 1] = sarg.c_str();
hiprtcResult compileResult{hiprtcCompileProgram(prog, NUM_OPTIONS, options)};"

AMD ROCm™ v4.3 Release Notes

This document describes the features, fixed issues, and information about downloading and installing the AMD ROCm™ software. It also covers known issues and deprecations in this release.

ROCm Installation Updates

Supported Operating Systems

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

OS

Kernel

SLES15 SP3

5.3.18-24.49

RHEL 7.9

3.10.0-1160.6.1.el7

CentOS 7.9

3.10.0-1127

RHEL 8.4

4.18.0-193.1.1.el8

CentOS 8.3

4.18.0-193.el8

Ubuntu 18.04.5

5.4.0-71-generic

Ubuntu 20.04.2

5.8.0-48-generic

Support for Enviornment Modules

Environment modules are now supported. This enhancement in the ROCm v4.3 release enables users to switch between ROCm v4.2 and ROCm v4.3 easily and efficiently.

For more information about installing environment modules, refer to

https://modules.readthedocs.io/en/latest/

AMD ROCm Documentation Updates

AMD ROCm Installation Guide

The AMD ROCm Installation Guide in this release includes:

  • Supported Environments

  • Installation Instructions

  • HIP Installation Instructions

For more information, refer to the ROCm documentation website at:

https://rocmdocs.amd.com/en/latest/

AMD ROCm - HIP Documentation Updates

To access the following documentation, see

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

  • HIP Programming Guide v4.3

  • HIP API Guide v4.3

  • HIP-Supported CUDA API Reference Guide v4.3

  • NEW - AMD ROCm Compiler Reference Guide v4.3

  • HIP FAQ

https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-FAQ.html#hip-faq

ROCm Data Center User and API Guide

To access the following documentation, see

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

  • ROCm Data Center Tool User Guide

    • Prometheus (Grafana) Integration with Automatic Node Detection

  • ROCm Data Center Tool API Guide

ROCm SMI API Documentation Updates

To access the following documentation, see

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

  • ROCm SMI API Guide

ROC Debugger User and API Guide

To access the following documentation, see

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

  • ROC Debugger User Guide

  • Debugger API Guide

What’s New in This Release

HIP Enhancements

HIP Versioning Update

The HIP version definition is updated from the ROCm v4.2 release as follows:

HIP_VERSION=HIP_VERSION_MAJOR * 10000000 + HIP_VERSION_MINOR * 100000 +
HIP_VERSION_PATCH)

The HIP version can be queried from a HIP API call

hipRuntimeGetVersion(&runtimeVersion);

Note: The version returned will be greater than the version in previous ROCm releases.

Support for Managed Memory Allocation

HIP now supports and automatically manages Heterogeneous Memory Management (HMM) allocation. The HIP application performs a capability check before making the managed memory API call hipMallocManaged.

Note: The managed keyword is unsupported currently.

int managed_memory = 0;
HIPCHECK(hipDeviceGetAttribute(&managed_memory,
 hipDeviceAttributeManagedMemory,p_gpuDevice));
if (!managed_memory ) {
printf ("info: managed memory access not supported on the device %d\n Skipped\n", p_gpuDevice);
}
else {
 HIPCHECK(hipSetDevice(p_gpuDevice));
HIPCHECK(hipMallocManaged(&Hmm, N * sizeof(T)));
. . .
}

Kernel Enqueue Serialization

Developers can control kernel command serialization from the host using the following environment variable, AMD_SERIALIZE_KERNEL

  • AMD_SERIALIZE_KERNEL = 1, Wait for completion before enqueue,

  • AMD_SERIALIZE_KERNEL = 2, Wait for completion after enqueue,

  • AMD_SERIALIZE_KERNEL = 3, Both.

This environment variable setting enables HIP runtime to wait for GPU idle before/after any GPU command.

NUMA-aware Host Memory Allocation

The Non-Uniform Memory Architecture (NUMA) policy determines how memory is allocated and selects a CPU closest to each GPU.

NUMA also measures the distance between the GPU and CPU devices. By default, each GPU selects a Numa CPU node that has the least NUMA distance between them; the host memory is automatically allocated closest to the memory pool of the NUMA node of the current GPU device.

Note, using the hipSetDevice API with a different GPU provides access to the host allocation. However, it may have a longer NUMA distance.

New Atomic System Scope Atomic Operations

HIP now provides new APIs with _system as a suffix to support system scope atomic operations. For example, atomicAnd atomic is dedicated to the GPU device, and atomicAnd_system allows developers to extend the atomic operation to system scope from the GPU device to other CPUs and GPU devices in the system.

For more information, refer to the HIP Programming Guide at,

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

Indirect Function Call and C++ Virtual Functions

While the new release of the ROCm compiler supports indirect function calls and C++ virtual functions on a device, there are some known limitations and issues.

Limitations

  • An address to a function is device specific. Note, a function address taken on the host can not be used on a device, and a function address taken on a device can not be used on the host. On a system with multiple devices, an address taken on one device can not be used on a different device.

  • C++ virtual functions only work on the device where the object was constructed.

  • Indirect call to a device function with function scope shared memory allocation is not supported. For example, LDS.

  • Indirect call to a device function defined in a source file different than the calling function/kernel is only supported when compiling the entire program with -fgpu-rdc.

Known Issues in This Release

  • Programs containing kernels with different launch bounds may crash when making an indirect function call. This issue is due to a compiler issue miscalculating the register budget for the callee function.

  • Programs may not work correctly when making an indirect call to a function that uses more resources. For example, scratch memory, shared memory, registers made available by the caller.

  • Compiling a program with objects with pure or deleted virtual functions on the device will result in a linker error. This issue is due to the missing implementation of some C++ runtime functions on the device.

  • Constructing an object with virtual functions in private or shared memory may crash the program due to a compiler issue when generating code for the constructor.

ROCm Data Center Tool

Prometheus (Grafana) Integration with Automatic Node Detection

The ROCm Data Center (RDC) tool enables you to use Consul to discover the rdc_prometheus service automatically. Consul is “a service mesh solution providing a full-featured control plane with service discovery, configuration, and segmentation functionality.” For more information, refer to their website at https://www.consul.io/docs/intro.

The ROCm Data Center Tool uses Consul for health checks of RDC’s integration with the Prometheus plug-in (rdc_prometheus), and these checks provide information on its efficiency.

Previously, when a new compute node was added, users had to change prometheus_targets.json to use Consul manually. Now, with the Consul agent integration, a new compute node can be discovered automatically.

https://github.com/RadeonOpenCompute/ROCm/tree/roc-4.3.x

Coarse Grain Utilization

This feature provides a counter that displays the coarse grain GPU usage information, as shown below.

Sample output

$ rocm_smi.py --showuse
============================== % time GPU is busy =============================
       GPU[0] : GPU use (%): 0
       GPU[0] : GFX Activity: 3401

Add 64-bit Energy Accumulator In-band

This feature provides an average value of energy consumed over time in a free-flowing RAPL counter, a 64-bit Energy Accumulator.

Sample output

$ rocm_smi.py --showenergycounter
=============================== Consumed Energy ================================
GPU[0] : Energy counter: 2424868
GPU[0] : Accumulated Energy (uJ): 0.0

Support for Continuous Clocks Values

ROCm SMI will support continuous clock values instead of the previous discrete levels. Moving forward the updated sysfs file will consist of only MIN and MAX values and the user can set the clock value in the given range.

Sample output:

$ rocm_smi.py --setsrange 551 1270
Do you accept these terms? [y/N] y
============================= Set Valid sclk Range=======
GPU[0]          : Successfully set sclk from 551(MHz) to 1270(MHz)
GPU[1]          : Successfully set sclk from 551(MHz) to 1270(MHz)
=========================================================================

$ rocm_smi.py --showsclkrange
============================ Show Valid sclk Range======

GPU[0]          : Valid sclk range: 551Mhz - 1270Mhz
GPU[1]          : Valid sclk range: 551Mhz - 1270Mhz

Memory Utilization Counters

This feature provides a counter display memory utilization information as shown below.

Sample output

   $ rocm_smi.py --showmemuse
========================== Current Memory Use ==============================

GPU[0] : GPU memory use (%): 0
GPU[0] : Memory Activity: 0

Performance Determinism

ROCm SMI supports performance determinism as a unique mode of operation. Performance variations are minimal as this enhancement allows users to control the entry and exit to set a soft maximum (ceiling) for the GFX clock.

Sample output

$ rocm_smi.py --setperfdeterminism 650
cat pp_od_clk_voltage
GFXCLK:
0: 500Mhz
1: 650Mhz *
2: 1200Mhz
$ rocm_smi.py --resetperfdeterminism

Note: The idle clock will not take up higher clock values if no workload is running. After enabling determinism, users can run a GFX workload to set performance determinism to the desired clock value in the valid range.

* GFX clock could either be less than or equal to the max value set in this mode. GFX clock will be at the max clock set in this mode only when required by the running     workload.

* VDDGFX will be higher by an offset (75mv or so based on PPTable) in the determinism mode.

HBM Temperature Metric Per Stack

This feature will enable ROCm SMI to report all HBM temperature values as shown below.

Sample output

$ rocm_smi.py –showtemp
================================= Temperature =================================
GPU[0] : Temperature (Sensor edge) (C): 29.0
GPU[0] : Temperature (Sensor junction) (C): 36.0
GPU[0] : Temperature (Sensor memory) (C): 45.0
GPU[0] : Temperature (Sensor HBM 0) (C): 43.0
GPU[0] : Temperature (Sensor HBM 1) (C): 42.0
GPU[0] : Temperature (Sensor HBM 2) (C): 44.0
GPU[0] : Temperature (Sensor HBM 3) (C): 45.0

ROCm Math and Communication Libraries

rocBLAS

Optimizations

  • Improved performance of non-batched and batched rocblas_Xgemv for gfx908 when m <= 15000 and n <= 15000

  • Improved performance of non-batched and batched rocblas_sgemv and rocblas_dgemv for gfx906 when m <= 6000 and n <= 6000

  • Improved the overall performance of non-batched and batched rocblas_cgemv for gfx906

  • Improved the overall performance of rocblas_Xtrsv

For more information, refer to

https://rocblas.readthedocs.io/en/master/

rocRAND

Enhancements

  • gfx90a support added

  • gfx1030 support added

  • gfx803 supported re-enabled

Fixed

  • Memory leaks in Poisson tests has been fixed.

  • Memory leaks when generator has been created but setting seed/offset/dimensions display an exception has been fixed.

For more information, refer to

https://rocrand.readthedocs.io/en/latest/

rocSOLVER

Enhancements

Linear solvers for general non-square systems:

  • GELS now supports underdetermined and transposed cases

  • Inverse of triangular matrices

  • TRTRI (with batched and strided_batched versions)

  • Out-of-place general matrix inversion

  • GETRI_OUTOFPLACE (with batched and strided_batched versions)

  • Argument names for the benchmark client now match argument names from the public API

Fixed Issues

  • Known issues with Thin-SVD. The problem was identified in the test specification, not in the thin-SVD implementation or the rocBLAS gemm_batched routines.

  • Benchmark client no longer crashes as a result of leading dimension or stride arguments not being provided on the command line.

Optimizations

  • Improved general performance of matrix inversion (GETRI)

For more information, refer to

https://rocsolver.readthedocs.io/en/latest/

rocSPARSE

Enhancements

  • (batched) tridiagonal solver with and without pivoting

  • dense matrix sparse vector multiplication (gemvi)

  • support for gfx90a

  • sampled dense-dense matrix multiplication (sddmm)

Improvements

  • client matrix download mechanism

  • boost dependency in clients removed

For more information, refer to

https://rocsparse.readthedocs.io/en/latest/usermanual.html#rocsparse-gebsrmv

hipBLAS

Enhancements

  • Added hipblasStatusToString

Fixed

  • Added catch() blocks around API calls to prevent the leak of C++ exceptions

rocFFT

Changes

  • Re-split device code into single-precision, double-precision, and miscellaneous kernels.

Fixed Issues

  • double-precision planar->planar transpose.

  • 3D transforms with unusual strides, for SBCC-optimized sizes.

  • Improved buffer placement logic.

For more information, refer to

https://rocfft.readthedocs.io/en/rocm-4.3.0/

hipFFT

Fixed Issues

  • CMAKE updates

  • Added callback API in hipfftXt.h header.

rocALUTION

Enhancements

  • Support for gfx90a target

  • Support for gfx1030 target

Improvements

  • Install script

For more information, refer to

rocTHRUST

Enhancements

  • Updated to match upstream Thrust 1.11

  • gfx90a support added

  • gfx803 support re-enabled

hipCUB

Enhancements

  • DiscardOutputIterator to backend header

ROCProfiler Enhancements

Tracing Multiple MPI Ranks

When tracing multiple MPI ranks in ROCm v4.3, users must use the form:

mpirun ... <mpi args> ... rocprof ... <rocprof args> ... application ... <application args>

NOTE: This feature differs from ROCm v4.2 (and lower), which used “rocprof … mpirun … application”.

This change was made to enable ROCProfiler to handle process forking better and launching via mpirun (and related) executables.

From a user perspective, this new execution mode requires:

  1. Generation of trace data per MPI (or process) rank.

  2. Use of a new “merge_traces.sh” utility script to combine traces from multiple processes into a unified trace for profiling.

For example, to accomplish step #1, ROCm provides a simple bash wrapper that demonstrates how to generate a unique output directory per process:

$ cat wrapper.sh
#! /usr/bin/env bash
if [[ -n ${OMPI_COMM_WORLD_RANK+z} ]]; then
  # mpich
  export MPI_RANK=${OMPI_COMM_WORLD_RANK}
elif [[ -n ${MV2_COMM_WORLD_RANK+z} ]]; then
  # ompi
  export MPI_RANK=${MV2_COMM_WORLD_RANK}
fi
args="$*"
pid="$$"
outdir="rank_${pid}_${MPI_RANK}"
outfile="results_${pid}_${MPI_RANK}.csv"
eval "rocprof -d ${outdir} -o ${outdir}/${outfile} $*"

This script:

  • Determines the global MPI rank (implemented here for OpenMPI and MPICH only)

  • Determines the process id of the MPI rank

  • Generates a unique output directory using the two

To invoke this wrapper, use the following command:

mpirun <mpi args> ./wrapper.sh --hip-trace <application> <args>

This generates an output directory for each used MPI rank. For example,

$ ls -ld rank_* | awk {'print $5" "$9'}
4096 rank_513555_0
4096 rank_513556_1

Finally, these traces may be combined using the merge traces script. For example,

$  ./merge_traces.sh -h
Script for aggregating results from multiple rocprofiler out directries.
Full path: /opt/rocm/bin/merge_traces.sh
Usage:
merge_traces.sh -o <outputdir> [<inputdir>...]

Use the following input arguments to the merge_traces.sh script to control which traces are merged and where the resulting merged trace is saved.

  • -o <outputdir> - output directory where the results are aggregated.

  • <inputdir>… - space-separated list of rocprofiler directories. If not specified, CWD is used.

For example, if an output directory named “unified” was supplied to the merge_traces.sh script, the file ‘unified/results.json’ will be generated, and the contains trace data from both MPI ranks.

Known issue for ROCProfiler

Collecting several counter collection passes (multiple “pmc:” lines in an counter input file) is not supported in a single run.
The workaround is to break the multiline counter input file into multiple single-line counter input files and execute runs.

Known Issues in This Release

The following are the known issues in this release.

An upgrade from previous releases to AMD ROCm v4.2 is not supported. Complete uninstallation of previous ROCm versions is required before installing a new version of ROCm.

The HIP runtime returns the hipErrorLaunchFailure error code when an application tries to launch kernel with a block size larger than the launch bounds mentioned during compile time. If no launch bounds were specified during the compile time, the default value of 1024 is assumed. Refer to the HIP trace for more information about the failing kernel. A sample error in the trace is shown below:

Snippet of the HIP trace

:3:devprogram.cpp           :2504: 2227377746776 us: Using Code Object V4.
:3:hip_module.cpp           :361 : 2227377768546 us: 7670 : [7f7c6eddd180] ihipModuleLaunchKernel ( 0x0x16fe080, 2048, 1, 1,        1024, 1, 1, 0, stream:<null>, 0x7ffded8ad260, char array:<null>, event:0, event:0, 0, 0 )
:1:hip_module.cpp           :254 : 2227377768572 us: Launch params (1024, 1, 1) are larger than launch bounds (64) for      kernel _Z8MyKerneliPd
:3:hip_platform.cpp         :667 : 2227377768577 us: 7670 : [7f7c6eddd180] ihipLaunchKernel: Returned hipErrorLaunchFailure         :
:3:hip_module.cpp           :493 : 2227377768581 us: 7670 : [7f7c6eddd180] hipLaunchKernel: Returned hipErrorLaunchFailure :

There is no known workaround at this time.

Users may observe that the /opt/rocm-x/bin/pycache folder continues to exist even after the rocm_smi_lib uninstallation. Workaround: Delete the /opt/rocm-x/bin/pycache folder manually before uninstalling rocm_smi_lib.