NVIDIA CUDA Toolkit Release Notes
---------------------------------

The Release Notes for the CUDA Toolkit.


1. CUDA Toolkit Major Components
--------------------------------

This section provides an overview of the major components of
the NVIDIA® CUDA® Toolkit and points to their locations
after installation.

Compiler
      The CUDA-C and CUDA-C++ compiler, nvcc, is found in the
      bin/ directory. It is built on top of the NVVM
      optimizer, which is itself built on top of the LLVM
      compiler infrastructure. Developers who want to target
      NVVM directly can do so using the Compiler SDK, which is
      available in the nvvm/ directory.

      Please note that the following files are
      compiler-internal and subject to change without any
      prior notice.

        * any file in include/crt and bin/crt

        * include/common_functions.h,
          include/device_double_functions.h,
          include/device_functions.h, include/host_config.h,
          include/host_defines.h, and include/math_functions.h

        * nvvm/bin/cicc

        * bin/cudafe++, bin/bin2c, and bin/fatbinary

Tools 
      The following development tools are available in the
      bin/ directory (except for Nsight Visual Studio Edition
      (VSE) which is installed as a plug-in to Microsoft
      Visual Studio, Nsight Compute and Nsight Systems are
      available in a separate directory).

        * IDEs: nsight (Linux, Mac), Nsight VSE (Windows)

        * Debuggers: cuda-memcheck, cuda-gdb (Linux), Nsight
          VSE (Windows)

        * Profilers: Nsight Systems, Nsight Compute, nvprof,
          nvvp, ncu, Nsight VSE (Windows)

        * Utilities: cuobjdump, nvdisasm

Libraries
      The scientific and utility libraries listed below are
      available in the lib64/ directory (DLLs on Windows are
      in bin/), and their interfaces are available in the
      include/ directory.

        * cub (High performance primitives for CUDA)

        * cublas (BLAS)

        * cublas_device (BLAS Kernel Interface)

        * cuda_occupancy (Kernel Occupancy Calculation [header
          file implementation])

        * cudadevrt (CUDA Device Runtime)

        * cudart (CUDA Runtime)

        * cufft (Fast Fourier Transform [FFT])

        * cupti (CUDA Profiling Tools Interface)

        * curand (Random Number Generation)

        * cusolver (Dense and Sparse Direct Linear Solvers and
          Eigen Solvers)

        * cusparse (Sparse Matrix)

        * libcu++ (CUDA Standard C++ Library)

        * nvJPEG (JPEG encoding/decoding)

        * npp (NVIDIA Performance Primitives [image and signal
          processing])

        * nvblas ("Drop-in" BLAS)

        * nvcuvid (CUDA Video Decoder [Windows, Linux])

        * nvml (NVIDIA Management Library)

        * nvrtc (CUDA Runtime Compilation)

        * nvtx (NVIDIA Tools Extension)

        * thrust (Parallel Algorithm Library [header file
          implementation])

CUDA Samples

      Code samples that illustrate how to use various CUDA and
      library APIs are available in the samples/ directory on
      Linux and Mac, and are installed to
      C:\ProgramData\NVIDIA Corporation\CUDA Samples on
      Windows. On Linux and Mac, the samples/ directory is
      read-only and the samples must be copied to another
      location if they are to be modified. Further
      instructions can be found in the Getting Started Guides
      for Linux and Mac.

Documentation

      The most current version of these release notes can be
      found online at
      http://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html.
      Also, the version.txt file in the root directory of the
      toolkit will contain the version and build number of the
      installed toolkit.

      Documentation can be found in PDF form in the doc/pdf/
      directory, or in HTML form at doc/html/index.html and
      online at http://docs.nvidia.com/cuda/index.html.

CUDA-GDB Sources
      CUDA-GDB sources are available as follows:

        * For CUDA Toolkit 7.0 and newer, in the installation
          directory extras/. The directory is created by
          default during the toolkit installation unless the
          .rpm or .deb package installer is used. In this
          case, the cuda-gdb-src package must be manually
          installed.

        * For CUDA Toolkit 6.5, 6.0, and 5.5, at
          https://github.com/NVIDIA/cuda-gdb.

        * For CUDA Toolkit 5.0 and earlier, at
          ftp://download.nvidia.com/CUDAOpen64/.

        * Upon request by sending an e-mail to
          mailto:oss-requests@nvidia.com.


2. CUDA 11.1 Release Notes
--------------------------

The release notes for the CUDA Toolkit can be found online at
http://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html.


2.1. CUDA Toolkit Major Component Versions

CUDA Components

      Starting with CUDA 11, the various components in the
      toolkit are versioned independently.

      For CUDA 11.1, the table below indicates the versions:

      Table 1. CUDA 11.1 Update 1 Component Versions

      Component Name

      Version Information

      Supported Architectures

      CUDA Runtime (cudart)

      11.1.74

      x86_64, POWER, Arm64

      cuobjdump

      11.1.74

      x86_64, POWER, Arm64

      CUPTI

      11.1.105

      x86_64, POWER, Arm64

      CUDA Demo Suite

      11.1.74

      x86_64

      CUDA GDB

      11.1.105

      x86_64, POWER, Arm64

      CUDA Memcheck

      11.1.105

      x86_64, POWER

      CUDA NVCC

      11.1.105

      x86_64, POWER, Arm64

      CUDA nvdisasm

      11.1.74

      x86_64, POWER, Arm64

      CUDA NVML Headers

      11.1.74

      x86_64, POWER, Arm64

      CUDA nvprof

      11.1.105

      x86_64, POWER, Arm64

      CUDA nvprune

      11.1.74

      x86_64, POWER, Arm64

      CUDA NVRTC

      11.1.105

      x86_64, POWER, Arm64

      CUDA NVTX

      11.1.74

      x86_64, POWER, Arm64

      CUDA NVVP

      11.1.105

      x86_64, POWER

      CUDA Samples

      11.1.105

      x86_64, POWER, Arm64

      CUDA Compute Sanitizer API

      11.1.105

      x86_64, POWER, Arm64

      CUDA cuBLAS

      11.3.0.106

      x86_64, POWER, Arm64

      CUDA cuFFT

      10.3.0.105

      x86_64, POWER, Arm64

      CUDA cuRAND

      10.2.2.105

      x86_64, POWER, Arm64

      CUDA cuSOLVER

      11.0.1.105

      x86_64, POWER, Arm64

      CUDA cuSPARSE

      11.3.0.10

      x86_64, POWER, Arm64

      CUDA NPP

      11.1.2.301

      x86_64, POWER, Arm64

      CUDA nvJPEG

      11.3.0.105

      x86_64, POWER, Arm64

      Nsight Eclipse Plugins

      11.1.74

      x86_64, POWER

      Nsight Compute

      2020.2.1.8

      x86_64, POWER, Arm64

      Nsight Windows NVTX

      1.21018621

      x86_64, POWER, Arm64

      Nsight Systems

      2020.3.4.32

      x86_64, POWER, Arm64

      Nsight Visual Studio Edition (VSE)

      2020.2.0.20284

      x86_64 (Windows)

      NVIDIA Linux Driver

      455.32.00

      x86_64, POWER, Arm64

      NVIDIA Windows Driver

      456.81

      x86_64 (Windows)

CUDA Driver

      Running a CUDA application requires the system with at
      least one CUDA capable GPU and a driver that is
      compatible with the CUDA Toolkit. See Table 2. For more
      information various GPU products that are CUDA capable,
      visit https://developer.nvidia.com/cuda-gpus.

      Each release of the CUDA Toolkit requires a minimum
      version of the CUDA driver. The CUDA driver is backward
      compatible, meaning that applications compiled against a
      particular version of the CUDA will continue to work on
      subsequent (later) driver releases.

      More information on compatibility can be found at
      https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#cuda-runtime-and-driver-api-version.

      Note: Starting with CUDA 11.0, the toolkit components
      are individually versioned, and the toolkit itself is
      versioned as shown in the table below.

      Table 2. CUDA Toolkit and Compatible Driver Versions

      CUDA Toolkit

      Linux x86_64 Driver Version

      Windows x86_64 Driver Version

      CUDA 11.1.1 Update 1

      >=455.32

      >=456.81

      CUDA 11.1 GA

      >=455.23

      >=456.38

      CUDA 11.0.3 Update 1

      >= 450.51.06

      >= 451.82

      CUDA 11.0.2 GA

      >= 450.51.05

      >= 451.48

      CUDA 11.0.1 RC

      >= 450.36.06

      >= 451.22

      CUDA 10.2.89

      >= 440.33

      >= 441.22

      CUDA 10.1 (10.1.105 general release, and updates)

      >= 418.39

      >= 418.96

      CUDA 10.0.130

      >= 410.48

      >= 411.31

      CUDA 9.2 (9.2.148 Update 1)

      >= 396.37

      >= 398.26

      CUDA 9.2 (9.2.88)

      >= 396.26

      >= 397.44

      CUDA 9.1 (9.1.85)

      >= 390.46

      >= 391.29

      CUDA 9.0 (9.0.76)

      >= 384.81

      >= 385.54

      CUDA 8.0 (8.0.61 GA2)

      >= 375.26

      >= 376.51

      CUDA 8.0 (8.0.44)

      >= 367.48

      >= 369.30

      CUDA 7.5 (7.5.16)

      >= 352.31

      >= 353.66

      CUDA 7.0 (7.0.28)

      >= 346.46

      >= 347.62

      For convenience, the NVIDIA driver is installed as part
      of the CUDA Toolkit installation. Note that this driver
      is for development purposes and is not recommended for
      use in production with Tesla GPUs.

      For running CUDA applications in production with Tesla
      GPUs, it is recommended to download the latest driver
      for Tesla GPUs from the NVIDIA driver downloads site at
      http://www.nvidia.com/drivers.

      During the installation of the CUDA Toolkit, the
      installation of the NVIDIA driver may be skipped on
      Windows (when using the interactive or silent
      installation) or on Linux (by using meta packages).

      For more information on customizing the install process
      on Windows, see
      http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#install-cuda-software.

      For meta packages on Linux, see
      https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#package-manager-metas


2.2. What's New in CUDA 11.1 Update 1

This section summarizes the changes in CUDA 11.1 Update 1
since the 11.1 GA release.

New Features

  * General CUDA

      * CUDA 11.1 Update 1 is a minor update that is binary
        compatible with CUDA 11.1. This release will work with
        all versions of the R450 NVIDIA driver.

  * nvJPEG

      * Added error handling capabilities for nonstandard JPEG
        images.

  * cuBLAS

      * cuBLASLt Logging is officially stable and no longer
        experimental. cuBLASLt Logging APIs are still
        experimental and may change in future releases.

  * cuSPARSE

      * cusparseSparseToDense

          * CSR, CSC, or COO conversion to dense
            representation

          * Support row-major and column-major layouts

          * Support all data types

          * Support 32-bit and 64-bit indices

          * Provide performance 3x higher than
            cusparseXcsc2dense, cusparseXcsr2dense

      * cusparseDenseToSparse

          * Dense representation to CSR, CSC, or COO

          * Support row-major and column-major layouts

          * Support all data types

          * Support 32-bit and 64-bit indices

          * Provide performance 3x higher than
            cusparseXcsc2dense, cusparseXcsr2dense

Known Issues

  * This toolkit release contains security fixes. Please refer
    to the Security Bulletinfor more information on the
    security fixes provided in this toolkit release

  * cuSPARSE

      * cusparseXdense2csr provides incorrect results for some
        matrix sizes.

Resolved Issues

  * cuBLAS

      * cublasLt Matmul fails on Volta architecture GPUs with
        CUBLAS_STATUS_EXECUTION_FAILED when n dimension >
        262,137 and epilogue bias feature is being used. This
        issue exists in 11.0 and 11.1 releases but has been
        corrected in 11.1 Update 1.

  * cuSOLVER

      * cusolverDnDDgels reports IRS_NOT_SUPPORTED when m > n.
        The issue has been fixed in release 11.1 U1, so
        cusolverDnDDgels will support m > n.

      * cusolverMgDeviceSelect can consume over 1GB device
        memory. The issue has been fixed in release 11.1 U1.
        The hidden memory allocation inside cusolverMG handle
        is about 30 MB per device.

Deprecations

  * cuSPARSE

      * Legacy conversion routines: cusparseXcsc2dense,
        cusparseXcsr2dense, cusparseXdense2csc,
        cusparseXdense2csr


2.3. General CUDA

  *  Added support for NVIDIA Ampere GPU architecture based
    GA10x GPUs GPUs (compute capability 8.6), including the
    GeForce RTX-30 series.

  * Enhanced CUDA compatibility across minor releases of CUDA
    will enable CUDA applications to be compatible with all
    versions of a particular CUDA major release.

  * CUDA 11.1 adds a new PTX Compiler static library that
    allows compilation of PTX programs using set of APIs
    provided by the library. See
    https://docs.nvidia.com/cuda/ptx-compiler-api/index.html
    for details.

  * Added the 7.1 version of the Parallel Thread Execution
    instruction set architecture (ISA). For more details on
    new (sm_86 target, mma.sp) and deprecated instructions,
    see
    https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#ptx-isa-version-7-1
    in the PTX documentation.

  * Added support for Fedora 32 and Debian 10.3 Buster on
    x86_64 platforms.

  * Unified programming model for:

      * async-copy

      * async-pipeline

      * async-barrier (cuda::barrier)

  * Added hardware accelerated sparse texture support.

  * Added support for read-only mapping for cudaHostRegister.

  * CUDA Graphs enhancements:

      * improved graphExec update

      * external dependencies

      * extended memcopy APIs

      * presubmit

  * Introduced new system level interface using /dev based
    capabilities for cgroups style isolation with MIG.

  * Improved MPS error handling when using multi-GPUs.

  * A fatal GPU exception generated by a Volta+ MPS client
    will be contained within the devices affected by it and
    other clients using those devices. Clients running on the
    other devices managed by the same MPS server can continue
    running as normal.

  * 

    Users can now configure and query the per-context time
    slice duration for a GPU via nvidia-smi. Configuring the
    time slice will require administrator privileges and the
    allowed settings are default, short, medium and long. The
    time slice will only be applicable to CUDA applications
    that are executed after the configuration is applied.

  * Improved detection and reporting of unsupported
    configurations.


2.4. CUDA Tools


2.4.1. CUDA Compilers

  * PTX Compiler is provided as a redistributable library.

  *  The following compilers are supported as host compilers
    in nvcc:

      * GCC 10.0

      * Clang 10.0


2.4.2. CUDA Developer Tools

  * For new features, improvements, and bug fixes in CUPTI,
    see the changelog.

  * For new features, improvements, and bug fixes in Nsight
    Compute, see the changelog.

  * Application replay for metric collection.


2.5. CUDA Libraries


2.5.1. cuFFT Library

  * cuFFT is now L2-cache aware and uses L2 cache for GPUs
    with more than 4.5MB of L2 cache. Performance may improve
    in certain single-GPU 3D C2C FFT cases.

  * After successfully creating a plan, cuFFT now enforces a
    lock on the cufftHandle. Subsequent calls to any planning
    function with the same cufftHandle will fail.

  * Added support for very large sizes (3k cube) to multi-GPU
    cuFFT on DGX-2.

  * Improved performance on multi-gpu cuFFT for certain sizes
    (1k cube).


2.5.2. cuSOLVER Library

  * Added new 64-bit APIs:

      * cusolverDnXpotrf_bufferSize

      * cusolverDnXpotrf

      * cusolverDnXpotrs

      * cusolverDnXgeqrf_bufferSize

      * cusolverDnXgeqrf

      * cusolverDnXgetrf_bufferSize

      * cusolverDnXgetrf

      * cusolverDnXgetrs

      * cusolverDnXsyevd_bufferSize

      * cusolverDnXsyevd

      * cusolverDnXsyevdx_bufferSize

      * cusolverDnXsyevdx

      * cusolverDnXgesvd_bufferSize

      * cusolverDnXgesvd

  * Added a new SVD algorithm based on polar decomposition,
    called GESVDP which uses the new 64-bit API, including
    cusolverDnXgesvdp_bufferSize and cusolverDnXgesvdp.


2.5.3. CUDA Math Library

  * Added host support for half and nv_bfloat16 converts
    to/from integer types.

  * Added __hcmadd() device only API for fast half2 and
    nv_bfloat162 based complex multiply-accumulate.


2.6. Deprecated Features

The following features are deprecated in the current release
of the CUDA software. The features still work in the current
release, but their documentation may have been removed, and
they will become officially unsupported in a future release.
We recommend that developers employ alternative solutions to
these features in their software.

General CUDA

        * Support for Ubuntu in IBM’s ppc64le platforms is
          deprecated in this release and will be dropped in a
          future CUDA release.

CUDA Tools

        * Support for VS2015 is deprecated. Older Visual
          Studio versions including VS2012 and VS2013 are also
          deprecated and support may be dropped in a future
          release of CUDA.

CUDA Libraries

        * The following cuSOLVER 64-bit APIs are deprecated:

            * cusolverDnPotrf_bufferSize

            * cusolverDnPotrf

            * cusolverDnPotrs

            * cusolverDnGeqrf_bufferSize

            * cusolverDnGeqrf

            * cusolverDnGetrf_bufferSize

            * cusolverDnGetrf

            * cusolverDnGetrs

            * cusolverDnSyevd_bufferSize

            * cusolverDnSyevd

            * cusolverDnSyevdx_bufferSize

            * cusolverDnSyevdx

            * cusolverDnGesvd_bufferSize

            * cusolverDnGesvd


2.7. Resolved Issues


2.7.1. General CUDA

  * Fixed an issue that caused cuD3D11GetDevices() to return a
    misleading error code.

  * Fixed an issue that caused cuda_ipc_open to fail with
    CUDA_ERROR_INVALID_HANDLE. (

  * Fixed an issue that caused the nvidia-ml library to be
    installed in a different location from the one specified
    in pkg-config.

  * Fixed an issue that caused some streaming apps to trigger
    CUDA safe detection.

  * Fixed an issue that caused unexpectedly large host memory
    usage when loading cubin.

  * Fixed an issue with the paths for .pc files in the CUDA
    SLES15 repo.

  * Fixed an issue that caused warnings to be considered fatal
    when installing nvidia-drivers modules with kickstart.

  * Resolved a memory issue when using cudaGraphInstantiate.

  * Read-only OS_DESCRIPTOR allocations are now supported.

  * Loading an application against the libcuda.so stub library
    now returns a helpful error message.

  * The cudaOccupancy* API is now available even when
    __CUDA_ACC__ is not defined.


2.7.2. CUDA Tools

  * When tracing graphs, grid/block dimensions showed in nvvp
    and nsight-sys were not always correct. This has been
    resolved.

  * Fixed an issue that prevented profiling with nvprof
    without setting LD_LIBRARY_PATH to the lib64 folder.

  * The Visual Profiler "Varying Register Count" graph's
    x-axis has changed from 65536 to 255 and the device limit
    is now 255.

  * Added nvswitch init error checking improvements for DMA,
    MSI, and SOE.

  * Improved detection and reporting of unsupported
    configurations.


2.7.3. cuBLAS Library

  * A performance regression in the cublasCgetrfBatched and
    cublasCgetriBatched routines has been fixed.

  * The IMMA kernels do not support padding in matrix C and
    may corrupt the data when matrix C with padding is
    supplied to cublasLtMatmul. A suggested work around is to
    supply matrix C with leading dimension equal to 32 times
    the number of rows when targeting the IMMA kernels:
    computeType = CUDA_R_32I and CUBLASLT_ORDER_COL32 for
    matrices A,C,D, and CUBLASLT_ORDER_COL4_4R2_8C (on NVIDIA
    Ampere GPU architecture or Turing architecture) or
    CUBLASLT_ORDER_COL32_2R_4R4 (on NVIDIA Ampere GPU
    architecture) for matrix B. Matmul descriptor must specify
    CUBLAS_OP_T on matrix B and CUBLAS_OP_N (default) on
    matrix A and C. The data corruption behavior was fixed so
    that CUBLAS_STATUS_NOT_SUPPORTED is returned instead.

  * Fixed an issue that caused an Address out of bounds error
    when calling cublasSgemm().

  * A performance regression in the cublasCgetrfBatched and
    cublasCgetriBatched routines has been fixed.


2.7.4. cuFFT Library

  * Resolved an issue that caused cuFFT to crash when reusing
    a handle after clearing a callback.

  * Fixed an error which produced incorrect results / NaN
    values when running a real-to-complex FFT in half
    precision.


2.8. Known Issues


2.8.1. cuFFT Library

  * cuFFT will always overwrite the input for out-of-place C2R
    transform.

  * Single dimensional multi-GPU FFT plans ignore user input
    on the whichGPUs parameter of cufftXtSetGPUs() and assume
    that GPUs IDs are always numbered from 0 to N-1.


Notices
-------


Notice

This document is provided for information purposes only and
shall not be regarded as a warranty of a certain
functionality, condition, or quality of a product. NVIDIA
Corporation (“NVIDIA”) makes no representations or
warranties, expressed or implied, as to the accuracy or
completeness of the information contained in this document and
assumes no responsibility for any errors contained herein.
NVIDIA shall have no liability for the consequences or use of
such information or for any infringement of patents or other
rights of third parties that may result from its use. This
document is not a commitment to develop, release, or deliver
any Material (defined below), code, or functionality.

NVIDIA reserves the right to make corrections, modifications,
enhancements, improvements, and any other changes to this
document, at any time without notice.

Customer should obtain the latest relevant information before
placing orders and should verify that such information is
current and complete.

NVIDIA products are sold subject to the NVIDIA standard terms
and conditions of sale supplied at the time of order
acknowledgement, unless otherwise agreed in an individual
sales agreement signed by authorized representatives of NVIDIA
and customer (“Terms of Sale”). NVIDIA hereby expressly
objects to applying any customer general terms and conditions
with regards to the purchase of the NVIDIA product referenced
in this document. No contractual obligations are formed either
directly or indirectly by this document.

NVIDIA products are not designed, authorized, or warranted to
be suitable for use in medical, military, aircraft, space, or
life support equipment, nor in applications where failure or
malfunction of the NVIDIA product can reasonably be expected
to result in personal injury, death, or property or
environmental damage. NVIDIA accepts no liability for
inclusion and/or use of NVIDIA products in such equipment or
applications and therefore such inclusion and/or use is at
customer’s own risk.

NVIDIA makes no representation or warranty that products based
on this document will be suitable for any specified use.
Testing of all parameters of each product is not necessarily
performed by NVIDIA. It is customer’s sole responsibility to
evaluate and determine the applicability of any information
contained in this document, ensure the product is suitable and
fit for the application planned by customer, and perform the
necessary testing for the application in order to avoid a
default of the application or the product. Weaknesses in
customer’s product designs may affect the quality and
reliability of the NVIDIA product and may result in additional
or different conditions and/or requirements beyond those
contained in this document. NVIDIA accepts no liability
related to any default, damage, costs, or problem which may be
based on or attributable to: (i) the use of the NVIDIA product
in any manner that is contrary to this document or (ii)
customer product designs.

No license, either expressed or implied, is granted under any
NVIDIA patent right, copyright, or other NVIDIA intellectual
property right under this document. Information published by
NVIDIA regarding third-party products or services does not
constitute a license from NVIDIA to use such products or
services or a warranty or endorsement thereof. Use of such
information may require a license from a third party under the
patents or other intellectual property rights of the third
party, or a license from NVIDIA under the patents or other
intellectual property rights of NVIDIA.

Reproduction of information in this document is permissible
only if approved in advance by NVIDIA in writing, reproduced
without alteration and in full compliance with all applicable
export laws and regulations, and accompanied by all associated
conditions, limitations, and notices.

THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE
BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER
DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING
PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED,
IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE
MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF
NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR
PURPOSE. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL
NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION
ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR
CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE
THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT,
EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
DAMAGES. Notwithstanding any damages that customer might incur
for any reason whatsoever, NVIDIA’s aggregate and cumulative
liability towards customer for the products described herein
shall be limited in accordance with the Terms of Sale for the
product.


VESA DisplayPort

DisplayPort and DisplayPort Compliance Logo, DisplayPort
Compliance Logo for Dual-mode Sources, and DisplayPort
Compliance Logo for Active Cables are trademarks owned by the
Video Electronics Standards Association in the United States
and other countries.


HDMI

HDMI, the HDMI logo, and High-Definition Multimedia Interface
are trademarks or registered trademarks of HDMI Licensing LLC.


OpenCL

OpenCL is a trademark of Apple Inc. used under license to the
Khronos Group Inc.


Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered
trademarks of NVIDIA Corporation in the U.S. and other
countries. Other company and product names may be trademarks
of the respective companies with which they are associated.


Copyright

© 2007-2020 NVIDIA Corporation. All rights reserved.


-------------------------------------------------------------
