1. What's New

Welcome to the 21.5 release 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.

Key features that are new in this release of the NVIDIA HPC SDK for Linux include:

  • Production support for a subset of OpenMP target offload features is now available for all supported Linux systems with V100 or later NVIDIA GPUs. Please refer to the "OpenMP" section of the HPC Compiler User's Guide for more information.
  • A subset of Arm Neon intrinsics have been implemented in the HPC Compilers and can be enabled with -Mneon_intrinsics.
  • NVSHMEM has been upgraded to version 2.1.2. As this is a major-version change, there are some breaking API changes; please see the NVSHMEM documentation for details.

2. Release Component Versions

The NVIDIA HPC SDK 21.5 release contains the following versions of each component:

Table 1. HPC SDK Release Components
  Linux_x86_64 Linux_ppc64le Linux_aarch64
  CUDA 10.2 CUDA 11.0 CUDA 11.3 CUDA 10.2 CUDA 11.0 CUDA 11.3 CUDA 10.2 CUDA 11.0 CUDA 11.3
nvc++ 21.5 21.5 21.5
nvc 21.5 21.5 21.5
nvfortran 21.5 21.5 21.5
nvcc 10.2.89 11.0.221 11.3.58 10.2.89 11.0.221 11.3.58 N/A 11.0.221 11.3.58
NCCL 2.8.3-1 2.8.3-1 2.8.3-1 2.8.3-1 2.8.3-1 2.8.3-1 N/A N/A 2.8.3-1
NVSHMEM 2.1.2 2.1.2 2.1.2 2.1.2 2.1.2 2.1.2 N/A N/A N/A
cuBLAS 10.2.2.89 11.2.0.252 11.4.2.10064 10.2.2.89 11.2.0.252 11.4.2.10064 N/A 11.2.0.252 11.4.2.10064
cuFFT 10.1.2.89 10.2.1.245 10.4.2.56 10.1.2.89 10.2.1.245 10.4.2.56 N/A 10.2.1.245 10.4.2.56
cuRAND 10.1.2.89 10.2.1.245 10.2.4.58 10.1.2.89 10.2.1.245 10.2.4.58 N/A 10.2.1.245 10.2.4.58
cuSOLVER 10.3.0.89 10.6.0.245 11.1.1.58 10.3.0.89 10.6.0.245 11.1.1.58 N/A 10.6.0.245 11.1.1.58
cuSPARSE 10.3.1.89 11.1.1.245 11.5.0.58 10.3.1.89 11.1.1.245 11.5.0.58 N/A 11.1.1.245 11.5.0.58
cuTENSOR 1.3 1.3 1.3 1.3 1.3 1.3 N/A 1.3 1.3
Nsight Compute 2021.1.0.18 2021.1.0.18 2021.1.0.18
Nsight Systems 2021.2.1.58 2021.2.1.58 2021.2.1.58
OpenMPI 3.1.5 3.1.5 3.1.5
HPC-X N/A 2.8.1 2.8.1 N/A 2.8.1 2.8.1 N/A 2.8.1 2.8.1
UCX N/A 1.10.0rc1 1.10.0rc1 N/A 1.10.0rc1 1.10.0rc1 N/A 1.10.0rc1 1.10.0rc1
OpenBLAS 0.3.13 0.3.13 0.3.13
Scalapack 2.1.0 2.1.0 2.1.0
Thrust 1.9.7 1.9.9 1.10.0 1.9.7 1.9.9 1.10.0 1.9.7 1.9.10 1.10.0
CUB N/A 1.9.9 1.10.0 N/A 1.9.9 1.10.0 N/A 1.9.9 1.10.0
libcu++ 1.0.0 2.0.0 2.0.0 1.0.0 2.0.0 2.0.0 1.0.0 2.0.0 2.0.0

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
x86_64

CentOS 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8
CentOS 7.9, 8.0, 8.1, 8.2
Fedora 29, 30, 31, 32
OpenSUSE Leap 15.0, 15.1
RHEL 7.0, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9
RHEL 8.0, 8.1, 8.2
SLES 12SP4, 12SP5, 15SP1
Ubuntu 16.04, 18.04, 19.10, 20.04
              

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

440.33
ppc64le

RHEL 7.3, 7.4, 7.5, 7.6, 7.7, 8.0, 8.1
RHEL Pegas 7.5, 7.6
Ubuntu 16.04, 18.04
              

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

440.33
aarch64

RHEL 8.1
Ubuntu 18.04
              

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

450.36

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 21.5 includes the following CUDA toolchain versions:
  • CUDA 10.2
  • CUDA 11.0
  • CUDA 11.3
The minimum required CUDA driver versions are listed in the table in Section 3.1.

4.  Known Limitations

  • The cuda-gdb debugger is included in this release. Currently, Fortran arrays with non-constant bounds are not handled correctly and querying values will yield incorrect results. Stepping through CUDA Fortran and OpenACC kernels is partially supported, but incorrect line numbers are displayed. For additional general limitations with cuda-gdb, please refer to its documentation.
  • 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).
  • When nvc++ -stdpar=multicore is used to generate parallel code, OpenMP pragmas in the same translation unit will also be enabled.

5.  Deprecations

  • Support for the KNL architecture of multicore CPUs in the NVIDIA HPC SDK was deprecated in the 21.3 release.
  • Starting with the 21.5 release 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.
  • Support for the Kepler architecture of NVIDIA GPUs was deprecated starting with the 21.3 release of the NVIDIA HPC SDK.

Notices

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