1. What's New in PGI 2020

Welcome to Release 2020 of the PGI compilers and tools!

If you read only one thing about this PGI release, make it this chapter. It covers all the new, changed, deprecated, or removed features in PGI products released this year. It is written with you, the user, in mind.

Every PGI release contains user-requested fixes and updates. We keep a complete list of these fixed Technical Problem Reports online for your reference.

1.1. What's New in 20.1

All Compilers

LLVM 9.0 integrated — Upgraded OpenPOWER compilers to use LLVM 9.0 as the default code generator. LLVM 8.0 is still available via compile and link-time command-line options.

cuTENSOR support — Added support for the new cuTENSOR library, including automatic mapping of Fortran transformational intrinsics operating on device data to cuTENSOR calls. See below for details.

New Heterogeneous OpenMP Runtime — Replaced the OpenMP runtime with a new optimized heterogeneous OpenMP runtime. In 20.1 this new runtime is used by default for multicore CPU targeting, and in future releases it will be used for integrated CPU and GPU OpenMP targeting. The new runtime is fully KMPC-compatible, but is not yet GOMP compatible, so mixing of PGI-compiled and GNU-compiled OpenMP objects is not supported and will result in a runtime error message.


Upgraded the PGI C compiler pgcc significantly, including support for the IBM C99 extensions "__real__" and "__imag__" and substantial support for C11.


Improved debugging metadata for PURE, RECURSIVE, and ELEMENTAL procedures.

Improved implementation of the OpenMP API to follow the OpenMP specification. Fortran developers should import the OpenMP API by using the omp_lib module rather than by defining its types (i.e, omp_lock_t) or declaring its functions directly in the application code. Failure to do so (or failure to do so correctly) may cause an application built with the NVIDIA OpenMP runtime to function incorrectly.

OpenACC and CUDA Fortran

Added Fortran interfaces to the NVIDIA cuTENSOR library which is bundled in the PGI packages. Added support for a new cutensorEx Fortran module that maps Fortran intrinsic functions RESHAPE(), TRANSPOSE(), SPREAD() and MATMUL() operating on device data to the appropriate cuTENSOR functionality. This new feature is usable in both CUDA Fortran and OpenACC, and enables use of V100 tensor cores for operations on real(2) data. See the Fortran CUDA Library Interfaces document for more information.

Added support for the CUDA Toolkit version 10.2. See CUDA Toolkit Versions for instructions on selecting alternate CUDA Toolkit versions using CUDA_HOME. CUDA 10.0 is now the default toolkit version if there is no GPU or CUDA driver found on the system. If you are relying on this default, you must specify a version of CUDA Toolkit version 10.0 using CUDA_HOME because the CUDA 10.0 toolchain and libraries are not bundled in this release.

Added support for calling the built-in CUDA math functions sinpi, sinpif, cospi, cospif, sincospi, and sincospif from device code.

Improved support for attributes(host,device) functions in CUDA Fortran, which enables compiling a program unit for both host and device execution.

Implemented support for on_device in CUDA Fortran, which enables dynamic checking for current execution on a GPU device.

Changed copy behavior for OpenACC reductions to adhere to the OpenACC specification. The compilers will now follow the defined behavior for copy and not copy the reduction variable to the device if it is already present. To enable the compiler's previous behavior, use an update device/host if_present directive.

Reorganized the accelerator runtime libraries. Removed the libaccapi, libaccg, libaccg2, libaccn, and libaccnc libraries. Replaced these libraries with three new libraries organized by functionality. The compiler drivers link in the new libraries based on flags used when linking:
  • The compiler adds libaccdevice with –acc –ta=tesla.
  • The compiler adds libacccuda with –acc –ta=tesla –Mcuda.
  • The compiler adds libacchost with –ta=multicore or –ta=host as long as –ta=tesla is not present.

Dropped support for __CUDA_API_VERSION. Use CUDA_VERSION instead.

Updated the profiling interface to conform to the OpenACC 3.0 specification.

Deprecations and Eliminations

CUDA 9.2 and 10.0 are no longer included as part of the PGI compilers installation package. CUDA 10.0 is still supported via CUDA_HOME. Support for CUDA 9.2 has been removed.

Official support for the nonvvm sub-option -⁠ta=tesla and -⁠Mcuda ended with the PGI 19.10 release. Although the compilers will not reject this sub-option, we recommend moving to the default method for generating device code by removing nonvvm from compilation.

2. Release Overview

This chapter provides an overview of Release 2020 of the PGI Accelerator™ compilers hosted on and targeting OpenPOWER+Tesla processor-based servers and clusters running versions of the Linux operating system.

2.1. About This Release

These PGI compilers generate host CPU code for 64-bit little-endian OpenPOWER CPUs, and GPU device code for NVIDIA Kepler, Pascal, and Volta GPUs.

These compilers include all GPU OpenACC and CUDA Fortran features available in the PGI C/C⁠+⁠+/Fortran compilers for x86-64.

Documentation includes the pgcc, pgc⁠+⁠+ and pgfortran man pages and the -⁠help option. In addition, you can find both HTML and PDF versions of these installation and release notes, the PGI Compiler User’s Guide and PGI Compiler Reference Manual for OpenPOWER on the PGI website.

2.2. Release Components

Release 2020 includes the following components:

  • PGFORTRAN™ native CUDA Fortran, OpenMP, and OpenACC Fortran 2003 compiler.
  • PGCC® native OpenMP and OpenACC ISO C11 and K&R C compiler.
  • PGC++® native OpenMP and OpenACC ISO C++17 compiler.
  • PGI Profiler® OpenACC, CUDA, OpenMP, and multi-thread profiler.
  • Open MPI version 3.1.3 including support for NVIDIA GPUDirect. As NVIDIA GPUDirect depends on InfiniBand support, Open MPI is also configured to use InfiniBand hardware if it is available on the system. InfiniBand support requires OFED 3.18 or later.
  • ScaLAPACK 2.0.2 linear algebra math library for distributed-memory systems for use with Open MPI and the PGI compilers.
  • BLAS and LAPACK library based on the customized OpenBLAS project source.
  • Documentation in man page format and online in both HTML and PDF formats.

2.3. Command-line Environment

The PGI compilers for OpenPOWER are command-line compatible with the corresponding PGI products on Linux x86-64, meaning target-independent compiler options should behave consistently across the two platforms. The intent and expectation is that makefiles and build scripts used to drive PGI compilers on Linux x86-64 should work with little or no modification on OpenPOWER. The -⁠help compiler option lists all compiler options by default, or can be used to check the functionality of a specific option. For example:

% pgcc -help -fast
Reading rcfile /opt/pgi/linuxpower/20.1/bin/.pgccrc
-fast               Common optimizations; includes -O2 -Munroll=c:1 -Mlre -Mautoinline
                    == -Mvect=simd -Mflushz
                    Control automatic vector pipelining
    [no]simd        Generate [don't generate] SIMD instructions
    [no]assoc       Allow [disallow] reassociation
    [no]fuse        Enable [disable] loop fusion
-M[no]flushz        Set SSE to flush-to-zero mode

2.4. Supported Platforms

These OpenPOWER hardware/software platforms have been used in testing:
  • Linux distributions:
    • RHEL 7.3, 7.4, 7.5, 7.5 (Pegas), 7.6, 7.6 (Pegas), 7.7, 8.1
    • Ubuntu 16.04, 18.04
  • GCC versions: 4.8.5, 5.4.0, 7.2.1, 7.3.0, 7.4.0
  • CUDA Toolkit versions:
    • 10.0 driver version 410.45, 410.129
    • 10.1 driver version 418.87
    • 10.2 driver version 440.33.01

2.5. OpenMP

OpenMP 3.1

The PGI Fortran, C, and C⁠+⁠+ compilers support OpenMP 3.1 on all platforms.

The NVIDIA OpenMP runtime implements nested parallelism such that the inner parallel region or regions are run with one thread.

OpenMP 4.5

The PGI Fortran, C, and C⁠+⁠+ compilers compile most OpenMP 4.5 programs for parallel execution across all the cores of a multicore CPU or server. target regions are implemented with default support for the multicore host as the target, and parallel and distribute loops are parallelized across all OpenMP threads.

Current limitations include:
  • The simd construct can be used to provide tuning hints; the simd construct's private, lastprivate, reduction, and collapse clauses are processed and supported.
  • The declare simd construct is ignored.
  • The ordered construct's simd clause is ignored.
  • The task construct's depend and priority clauses are not supported.
  • The loop construct's linear, schedule, and ordered(n) clauses are not supported.
  • The declare reduction directive is not supported.
  • The reduction clause does not accept pointer or reference types.

Incompatibility between NVIDIA and GNU OpenMP Runtimes

NVIDIA's OpenMP runtime implements a subset of OpenMP functionality; this OpenMP runtime functionality is provided in a library called libnvomp. The GNU OpenMP runtime also implements a subset of OpenMP functionality. OpenMP applications and libraries compiled with the GNU compilers link in a library called libgomp.

If GNU and PGI compilers are used to compile different modules of an application that each uses the OpenMP API, both the GNU and the NVIDIA OpenMP runtimes will be required for the application to operate. But because these two runtimes implement intersecting subsets of the OpenMP specification and do not share their state, the application's logic may be impaired causing incorrect execution including unexpected application termination.

This incompatibility is a limitation of the NVIDIA OpenMP runtime that will be corrected in a future release. In the meantime, the NVIDIA OpenMP runtime will detect whether libgomp is loaded by the application. If so, the application will print the following message:

WARNING: Another OpenMP runtime library has been detected in this
application; its presence conflicts with NVIDIA's OpenMP runtime library
and may cause incorrect application behavior.

For information about diagnosing and fixing the source of the runtime
library conflict, refer to the compiler's online documentation.

To suppress this check, use the NV_OMP_DISABLE_WARNINGS=true environment

As the message indicates, you can suppress the printing of the warning message by setting the environment variable NV_OMP_DISABLE_WARNINGS to true.

To address this problem in a safe manner, the application developer should determine what part of the application depends on the GNU OpenMP runtime. After identifying the source, which could be in a third-party library, the developer should select one of the following options to remove reliance on the GNU OpenMP runtime:
  • Compile the OpenMP code with the PGI compilers
  • Compile the OpenMP code without enabling the OpenMP pragmas
  • Remove the OpenMP code from compilation or linking altogether

Please contact us if these options do not work.

In a future release of the PGI compilers, the NVIDIA OpenMP runtime will include the functionality of the GNU OpenMP runtime making it safe for applications compiled with PGI compilers to link OpenMP codes compiled with the GNU compilers.

2.6. CUDA Toolkit Versions

The PGI compilers use NVIDIA's CUDA Toolkit when building programs for execution on an NVIDIA GPU. Every PGI installation package puts the required CUDA Toolkit components into a PGI installation directory called 2020/cuda.

An NVIDIA CUDA driver must be installed on a system with a GPU before you can run a program compiled for the GPU on that system. PGI products do not contain CUDA Drivers. You must download and install the appropriate CUDA Driver from NVIDIA. The CUDA Driver version must be at least as new as the version of the CUDA Toolkit with which you compiled your code.

The PGI tool pgaccelinfo prints the driver version as its first line of output. You can use it to find out which version of the CUDA Driver is installed on your system.

PGI 20.1 includes the following versions of the CUDA Toolkit:
  • CUDA 10.1
  • CUDA 10.2

You can let the compiler pick which version of the CUDA Toolkit to use or you can instruct it to use a particular version. The rest of this section describes all of your options.

If you do not specify a version of the CUDA Toolkit, the compiler uses the version of the CUDA Driver installed on the system on which you are compiling to determine which CUDA Toolkit to use. This auto-detect feature was introduced in the PGI 18.7 release; auto-detect is especially convenient when you are compiling and running your application on the same system. In the absence of any other information, the compiler will look for a CUDA Toolkit version in the PGI 2020/cuda directory that matches the version of the CUDA Driver installed on the system. If a match is not found, the compiler searches for the newest CUDA Toolkit version that is not newer than the CUDA Driver version. If there is no CUDA Driver installed, the PGI 20.1 compilers fall back to the default of CUDA 10.0.

If the only PGI compiler you have installed is PGI 20.1, then:
  • If your CUDA Driver is 10.2, the compilers use CUDA Toolkit 10.2.
  • If your CUDA Driver is 10.1, the compilers use CUDA Toolkit 10.1.
  • If your CUDA Driver is 10.0, the compilers will issue an error that CUDA Toolkit 10.0 was not found; CUDA Toolkit 10.0 is not bundled with PGI 20.1
  • If you do not have a CUDA driver installed on the compilation system, the compilers use the default CUDA Toolkit version 10.0.
  • If your CUDA Driver is newer than CUDA 10.2, the compilers will still use the CUDA Toolkit 10.2. The compiler selects the newest CUDA Toolkit it finds that is not newer than the CUDA Driver.
You can change the compiler's default selection for CUDA Toolkit version using one of the following methods:
  • Use a compiler option. Add the cudaX.Y sub-option to -⁠Mcuda or -⁠ta=tesla where X.Y denotes the CUDA version. For example, to compile a C file with the CUDA 10.2 Toolkit you would use:
    pgcc -ta=tesla:cuda10.2
    Using a compiler option changes the CUDA Toolkit version for one invocation of the compiler.
  • Use an rcfile variable. Add a line defining DEFCUDAVERSION to the siterc file in the installation bin/ directory or to a file named .mypgirc in your home directory. For example, to specify the CUDA 10.2 Toolkit as the default, add the following line to one of these files:
    set DEFCUDAVERSION=10.2;
    Using an rcfile variable changes the CUDA Toolkit version for all invocations of the compilers reading the rcfile.

When you specify a CUDA Toolkit version, you can additionally instruct the compiler to use a CUDA Toolkit installation different from the defaults bundled with the current PGI compilers. While most users do not need to use any other CUDA Toolkit installation than those provided with PGI, situations do arise where this capability is needed. Developers working with pre-release CUDA software may occasionally need to test with a CUDA Toolkit version not included in a PGI release. Conversely, some developers might find a need to compile with a CUDA Toolkit older than the oldest CUDA Toolkit installed with a PGI release. For these users, PGI compilers can interoperate with components from a CUDA Toolkit installed outside of the PGI installation directories.

PGI tests extensively using the co-installed versions of the CUDA Toolkits and fully supports their use. Use of CUDA Toolkit components not included with a PGI install is done with your understanding that functionality differences may exist.

To use a CUDA toolkit that is not installed with a PGI release, such as CUDA 10.0 with PGI 20.1, there are three options:

  • Use the rcfile variable DEFAULT_CUDA_HOME to override the base default
    set DEFAULT_CUDA_HOME = /opt/cuda-10.0;
  • Set the environment variable CUDA_HOME
    export CUDA_HOME=/opt/cuda-10.0
  • Use the compiler compilation line assignment CUDA_HOME=
    pgfortran CUDA_HOME=/opt/cuda-10.0

The PGI compilers use the following order of precedence when determining which version of the CUDA Toolkit to use.

  1. If you do not tell the compiler which CUDA Toolkit version to use, the compiler picks the CUDA Toolkit from the PGI installation directory 2020/cuda that matches the version of the CUDA Driver installed on your system. If the PGI installation directory does not contain a direct match, the newest version in that directory which is not newer than the CUDA driver version is used. If there is no CUDA driver installed on your system, the compiler falls back on an internal default; in PGI 20.1, this default is CUDA 10.0.
  2. The rcfile variable DEFAULT_CUDA_HOME will override the base default.
  3. The environment variable CUDA_HOME will override all of the above defaults.
  4. The environment variable PGI_CUDA_HOME overrides all of the above; it is available for advanced users in case they need to override an already-defined CUDA_HOME.
  5. A user-specified cudaX.Y sub-option to -⁠Mcuda and -⁠ta=tesla will override all of the above defaults and the CUDA Toolkit located in the PGI installation directory 2020/cuda will be used.
  6. The compiler compilation line assignment CUDA_HOME= will override all of the above defaults (including the cudaX.Y sub-option).

2.7. Compute Capability

The compilers can generate code for NVIDIA GPU compute capabilities 3.0 through 7.5. The compilers construct a default list of compute capabilities that matches the compute capabilities supported by the GPUs found on the system used in compilation. If there are no GPUs detected, the compilers select cc35, cc60, and cc70.

You can override the default by specifying one or more compute capabilities using either command-line options or an rcfile.

To change the default with a command-line option, provide a comma-separated list of compute capabilities to -⁠ta=tesla: for OpenACC or -⁠Mcuda= for CUDA Fortran.

To change the default with an rcfile, set the DEFCOMPUTECAP value to a blank-separated list of compute capabilities in the siterc file located in your installation's bin directory:


Alternatively, if you don't have permissions to change the siterc file, you can add the DEFCOMPUTECAP definition to a separate .mypgirc file in your home directory.

The generation of device code can be time consuming, so you may notice an increase in compile time as the number of compute capabilities increases.

2.8. Precompiled Open-Source Packages

Many open-source software packages have been ported for use with PGI compilers on Linux for OpenPOWER.

The following PGI-compiled open-source software packages are included in the PGI OpenPOWER download package:

  • OpenBLAS 0.3.3 – customized BLAS and LAPACK libraries based on the OpenBLAS project source.
  • Open MPI 3.1.3 – open-source MPI implementation.
  • ScaLAPACK 2.0.2 – a library of high-performance linear algebra routines for parallel distributed memory machines. ScaLAPACK uses Open MPI 3.1.3.

The following list of open-source software packages have been precompiled for execution on OpenPOWER targets using the PGI compilers and are available to download from the PGI website.

  • ESMF 7.1.0r for Open MPI 3.1.3 – The Earth System Modeling Framework for building climate, numerical weather prediction, data assimilation, and other Earth science software applications.
  • NetCDF 4.6.2 – A set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data, written in C. Included in this package are the following components:
    • NetCDF-C++ 4.3.0 – C++ interfaces to NetCDF libraries.
    • NetCDF-Fortran 4.4.4 – Fortran interfaces to NetCDF libraries.
    • Parallel NetCDF 1.11.0 – for Open MPI 3.1.3.
    • HDF5 1.10.4 – data model, library, and file format for storing and managing data.
    • SZIP 2.1.1 – extended-Rice lossless compression algorithm.
    • ZLIB 1.2.11 – file compression library.

In addition, these software packages have also been ported to PGI on OpenPOWER but due to licensing restrictions, they are not available in binary format directly from PGI. You can find instructions for building them in the Porting & Tuning Guides section of the PGI website.

  • FFTW 2.1.5 – version 2 of the Fast Fourier Transform library, includes MPI bindings built with Open MPI 3.1.3.
  • FFTW 3.3.8 – version 3 of the Fast Fourier Transform library, includes MPI bindings built with Open MPI 3.1.3.

For additional information about building these and other packages, please see the Porting & Tuning Guides section of the PGI website.

3. Installation and Configuration

Follow these steps to install PGI 20.1 compilers on an OpenPOWER system. The default installation directory is /opt/pgi, but it can be any directory:

% tar zxpf pgilinux-2020-201-ppc64le.tar.gz
% ./install
<answer installation questions, assent to licenses>

Typically for this release, you will want to choose the following during the installation:

  1. Choose a "Single-system install", not a "Network install".
  2. Install the PGI software in the default /opt/pgi directory.
  3. Install the CUDA toolkit. This installs CUDA components in the PGI directory tree, and will not affect a standard CUDA installation on the same system in any way.
  4. Create links in the 2020 directory. This is the directory where CUDA is installed, along with example programs; links are created to the subdirectories of /opt/pgi/linuxpower/20.1.
  5. Install Open MPI.

3.1. License Management

Installation may place a temporary license key in a file named license.pgi in the PGI installation directory if no such file already exists.

If you purchased a perpetual license and have obtained your new license key, either replace the contents of license.pgi with your new license key, or set the environment variable LM_LICENSE_FILE to the full path of the desired license file.

If you have not yet obtained your new license key, please consult your PGI order confirmation email for instructions for obtaining and installing your permanent license key. Contact PGI Sales at sales@pgroup.com if you need assistance.

Usage Logging: This release provides per-user records of most recent use in the .pgiusage subdirectory inside the main installation directory. Set the environment variable PGI_LOG_DIRECTORY to specify a different directory for usage logging.

3.2. Environment Initialization

Assuming the software is installed in /opt/pgi, use these commands in csh to initialize your environment for use of the PGI compilers:

% setenv PGI /opt/pgi
% setenv MANPATH "$MANPATH":$PGI/linuxpower/2020/man
% set path=($PGI/linuxpower/2020/bin $path)
% which pgc++

In bash, sh or ksh, use these commands:

% export PGI=/opt/pgi
% export MANPATH=$MANPATH:$PGI/linuxpower/2020/man
% export PATH=$PGI/linuxpower/2020/bin:$PATH
% which pgc++

The PGI directory structure is designed to accommodate co-installation of multiple PGI versions. When 20.1 is installed, it will be installed by default in the directory /opt/pgi/linuxpower/20.1 and links can optionally be created to its sub-directories to make 20.1 default without affecting a previous (e.g., 19.10) install. Non-default versions of PGI compilers that are installed can be used by specifying the -⁠V<ver> option on the compiler command line.

3.3. Network Installations

PGI compilers for OpenPOWER may be installed locally on each machine on a network or they may be installed once on a shared file system available to every machine. With the shared file system method, after the initial installation you can run a simple script on each machine to add that system to the family of machines using the common compiler installation. Using this approach, you can create a common installation that works on multiple linuxpower systems even though each system may have different versions of gcc/libc.

Follow these steps to create a shared file system installation on OpenPOWER systems:

  1. Create a commonly-mounted directory accessible to every system using the same directory path (for example, /opt/pgi).
  2. Define a locally-mounted directory with a pathname that is identical on all systems. That is, each system has a local directory path with the same pathname (for example /local/pgi/20.1/share_objects). Runtime libraries which are libc-version dependent will be stored here. This will ensure that executable files built on one system will work on other systems on the same network.
  3. Run the install script for the first installation:
    % tar zxpf pgilinux-2020-201-ppc64le.tar.gz
    % ./install
    <answer installation questions, assent to licenses>
    At the "Please choose install option:" prompt, choose "Network install".
  4. Once the initial PGI installation is complete, configure the environment as described in the preceding section.
  5. On each subsequent system, follow these steps:
    1. Set up the environment as described in the preceding section.
    2. Run the add_network_host script which is now in your $PATH:
      $ add_network_host
      and the compilers should now work.

4. Troubleshooting Tips and Known Limitations

This section contains information about known limitations, documentation errors, and corrections. Wherever possible, a work-around is provided.

For up-to-date information about the state of the current release, please see the PGI frequently asked questions (FAQ) webpage.

4.1. Release Specific Limitations

The following PGI features are limited or are not implemented in the 20.1 release for OpenPOWER+Tesla:

  • -Mipa is not enabled (no PGI inter-procedural analysis/optimization); the command-line option is accepted and silently ignored.
  • -Mpfi/-Mpfo are not enabled (no profile-feedback optimization); the command-line options are accepted and silently ignored.
  • Passing -M[no]llvm to MPI wrappers (mpicc, mpifort, etc.) is not supported. Doing so will cause unresolved symbol errors and segmentation faults when compiling.
  • Using -V<version> with the PGI 2019 or PGI 2020 compilers to target PGI 2018 or earlier releases is not supported. This is a known limitation that is the result of the switch to using LLVM compilers as the default code generator for the PGI 2019 compilers.

4.2. Profiler-related Issues

Some specific issues related to the PGI Profiler:

  • Debugging information is not always available on OpenPower which can cause the profiler to crash when attempting to unwind the call-stack. We recommend you use the --cpu-profiling-unwind-stack off option to disable call-stack tracing if you encounter any problem profiling on OpenPower.
  • The Profiler relies on being able to directly call 'dlsym'. If this system call is intercepted by the program being profiled or by some other library the profiler may hang at startup. We have encountered this specific problem with some implementations of MPI. We recommend you disable any features that may be intercepting the 'dlsym' system call or disable CPU profiling with the --cpu-profiling off option.
    • To disable 'dlsym' interception when using IBM's spectrum MPI set the environment variable: PAMI_DISABLE_CUDA_HOOK=1, omit the following option: -gpu and add the options: -x PAMI_DISABLE_CUDA_HOOK and -disable_gpu_hooks.

5. Contact Information

You can contact NVIDIA's PGI compilers and tools team at:

  • 9030 NE Walker Road, Suite 100
  • Hillsboro, OR 97006

Or electronically using any of the following means:

The PGI User Forum is monitored by members of the PGI engineering and support teams as well as other PGI customers. The forums contain answers to many commonly asked questions. Log in to the PGI website to access the forums.

Many questions and problems can be resolved by following instructions and the information available in the PGI frequently asked questions (FAQ).

Submit support requests using the PGI Technical Support Request form.




Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.


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