1. What's New

Welcome to the 23.5 version of the NVIDIA HPC SDK, a comprehensive suite of compilers and libraries enabling developers to program the entire HPC platform, from the GPU foundation to the CPU and out through the interconnect. The 23.5 release of the HPC SDK includes new features as well as important functionality and performance improvements.

  • Environment variables for controlling how the OpenACC runtime controls memory allocations are available. Please refer to the Using OpenACC section of the the HPC Compilers Users Guide for more details.
  • HPC-X and OpenMPI 4 have been updated to work with CUDA 12 and CUDA 11. HPX-X 2.15 is included to work with CUDA 12 and HPC-X 2.14 works with CUDA 11. New modulefiles, "nvhpc-hpcx-cuda12" and "nvhpc-hpcx-cuda11" are included. Updated OpenMPI 4.1.5 libraries are included with CUDA 12 and CUDA 12.
  • The HPC Compilers now provide the -gpu=sm_XY option to include SASS following the behavior of nvcc's --gpu-architecture=sm_80.
  • Using the -MD and -MMD options will now cause the HPC Compilers to output a .d file if the -o is specified.
  • The CUDA compatibility files for CUDA 12 are now being shipped with the HPC SDK. Please refer to the CUDA Documentation for usage instructions.

2. Release Component Versions

The NVIDIA HPC SDK 23.5 release contains the following versions of each component:

Table 1. HPC SDK Release Components
  Linux_x86_64 Linux_ppc64le Linux_aarch64
  CUDA 11.0 CUDA 11.8 CUDA 12.1 CUDA 11.0 CUDA 11.8 CUDA 12.1 CUDA 11.0 CUDA 11.8 CUDA 12.1
nvc++ 23.5 23.5 23.5
nvc 23.5 23.5 23.5
nvfortran 23.5 23.5 23.5
nvcc 11.0.221 11.8.89 12.1.105 11.8.89 11.0.221 12.1.105 11.0.221 11.8.89 12.1.105
NCCL 2.18.1 2.18.1 2.18.1 2.18.1 2.18.1 2.18.1 2.18.1 2.18.1 2.18.1
NVSHMEM 2.9.0 2.9.0 2.9.0 2.9.0 2.9.0 2.9.0 N/A N/A N/A
cuFFTMp N/A 11.0.5 11.0.5 N/A 11.0.5 N/A N/A N/A N/A
cuSOLVERMp N/A 0.4.0 N/A N/A N/A N/A N/A N/A N/A
cuTENSOR 1.7.0 1.7.0 1.7.0 1.7.0 1.7.0 1.7.0 1.7.0 1.7.0 1.7.0
Nsight Compute 2023.1.1 2023.1.1 2023.1.1
Nsight Systems 2023.2.1.122 2023.2.1.122 2023.2.1.122
OpenMPI 3.1.5 3.1.5 3.1.5
HPC-X N/A 2.14 2.15 N/A N/A N/A N/A 2.14 2.15
OpenBLAS 0.3.20 0.3.20 0.3.20
Scalapack 2.2.0 2.2.0 2.2.0
Thrust 1.9.9 1.15.1 2.0.1 1.9.9 1.15.1 2.0.1 1.9.9 1.15.1 2.0.1
CUB 1.9.9 1.15.1 2.0.1 1.9.9 1.15.1 2.0.1 1.9.9 1.15.1 2.0.1
libcu++ 1.0.0 1.8.1 1.9.0 1.0.0 1.8.1 1.9.0 1.0.0 1.8.1 1.9.0

3. Supported Platforms

3.1. Platform Requirements for the HPC SDK

Table 2. HPC SDK Platform Requirements
Architecture Linux Distributions Minimum gcc/glibc Toolchain Minimum CUDA Driver

RHEL/CentOS 7.3 - 7.9
RHEL/CentOS/Rocky 8.0 - 8.7
Fedora 33, 34
OpenSUSE Leap 15.2 - 15.4
SLES 15SP2, 15SP3, 15SP4
Ubuntu 18.04, 20.04, 22.04
Debian 10

C99: 4.8
C11: 4.9
C++03: 4.8
C++11: 4.9
C++14: 5.1
C++17: 7.1
C++20: 10.1


RHEL 7.3 - 7.7
RHEL 8.0 - 8.7

C99: 4.8
C11: 4.9
C++03: 4.8
C++11: 4.9
C++14: 5.1
C++17: 7.1
C++20: 10.1


RHEL/CentOS/Rocky 8.0 - 8.7
Ubuntu 20.04, 22.04
SLES 15SP2, 15SP3, 15SP4
Amazon Linux 2

C99: 4.8
C11: 4.9
C++03: 4.8
C++11: 4.9
C++14: 5.1
C++17: 7.1
C++20: 10.1


Programs generated by the HPC Compilers for x86_64 processors require a minimum of AVX instructions, which includes Sandy Bridge and newer CPUs from Intel, as well as Bulldozer and newer CPUs from AMD. POWER 8 and POWER 9 CPUs from the POWER architecture are supported. The HPC SDK includes support for v8.1+ Server Class Arm CPUs that meet the requirements appendix E specified in the SBSA 7.1 specification.

The HPC Compilers are compatible with gcc and g++ and use the GCC C and C++ libraries; the minimum compatible versions of GCC are listed in Table 2. The minimum system requirements for CUDA and NVIDIA Math Library requirements are available in the NVIDIA CUDA Toolkit documentation.

3.2. Supported CUDA Toolchain Versions

The NVIDIA HPC SDK uses elements of the CUDA toolchain when building programs for execution with NVIDIA GPUs. Every HPC SDK installation package puts the required CUDA components into an installation directory called [install-prefix]/[arch]/[nvhpc-version]/cuda.

An NVIDIA CUDA GPU device driver must be installed on a system with a GPU before you can run a program compiled for the GPU on that system. The NVIDIA HPC SDK does not contain CUDA Drivers. You must download and install the appropriate CUDA Driver from NVIDIA , including the CUDA Compatibility Platform if that is required.

The nvaccelinfo tool prints the CUDA Driver version in its output. You can use it to find out which version of the CUDA Driver is installed on your system.

The NVIDIA HPC SDK 23.5 includes the following CUDA toolchain versions:
  • CUDA 11.0
  • CUDA 11.8
  • CUDA 12.1
The minimum required CUDA driver versions are listed in the table in Section 3.1.

4.  Known Limitations

  • The -Mipa option has been disabled starting with the 23.3 version of the HPC Compilers.
  • The latest version of cuSolverMp bundled with this release has two new dependencies on UCC and UCX libraries. To execute a program linked against cuSolverMP, please use the “nvhpc-hpcx-cuda12” environment module for the HPC-X library, or set the environment variable LD_LIBRARY_PATH as follows: LD_LIBRARY_PATH=${NVHPCSDK_HOME}/comm_libs/hpcx/latest/ucc/lib:${NVHPCSDK_HOME}/comm_libs/11.8/hpcx/latest/ucx/lib:$LD_LIBRARY_PATH
  • If not using the provided modulefiles, prior to using HPC-X, users should take care to source the hpcx-init.sh script: $ . /[install-path]/Linux_x86_64/dev/comm_libs/hpcx/hpcx-2.11/hpcx-init.sh Then, run the hpcx_load function defined by this script: $ hpcx_load These actions will set important environment variables that are needed when running HPC-X. The following warning from HPC-X while running an MPI job – “WARNING: Open MPI tried to bind a process but failed. This is a warning only; your job will continue, though performance may be degraded” – is a known issue, and may be worked around as follows: export OMPI_MCA_hwloc_base_binding_policy=""
  • Fortran derived type objects with zero-size derived type allocatable components that are used in sourced allocation or allocatable assignment may result in a runtime segmentation violation.
  • When using -⁠stdpar to accelerate C++ parallel algorithms, the algorithm calls cannot include virtual function calls or function calls through a function pointer, cannot use C++ exceptions, can only dereference pointers that point to the heap, and must use random access iterators (raw pointers as iterators work best).

5.  Deprecations and Changes

  • Beginning with the HPC SDK 23.7, the deprecated CUDA_HOME environment variable will not affect the HPC Compilers.
  • The -ta=tesla, -Mcuda, -Mcudalib options for the HPC Compilers have been deprecated.
  • Support for the RHEL 7-based operating systems will be removed in the HPC SDK version 23.7, corresponding with the upstream end-of-life (EOL).
  • In an upcoming release the HPC SDK will bundle only CUDA 11.8 and the latest version of the CUDA 12.x series. Codepaths in the HPC Compilers that support CUDA versions older than 11.0 will no longer be tested or maintained.
  • Support for the Ubuntu 18.04 operating system will be removed in the HPC SDK version 23.5, corresponding with the upstream end-of-life (EOL).
  • Support for CUDA Fortran textures is deprecated in CUDA 11.0 and 11.8, and has been removed from CUDA 12.
  • cudaDeviceSynchronize() in CUDA Fortran has been deprecated, and support has been removed from device code. It is still supported in host code.
  • Starting with the 21.11 version of the NVIDIA HPC SDK, the HPC-X package is no longer shipped as part of the packages made available for the POWER architecture.
  • Starting with the 21.5 version of the NVIDIA HPC SDK, the -cuda option for NVC++ and NVFORTRAN no longer automatically links the NVIDIA GPU math libraries. Please refer to the -cudalib option.
  • HPC Compiler support for the Kepler architecture of NVIDIA GPUs was deprecated starting with the 21.3 version of the NVIDIA HPC SDK.
  • Support for the KNL architecture of multicore CPUs in the NVIDIA HPC SDK was removed in the HPC SDK version 21.3.




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