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Release Notes :: CUDA Toolkit Documentation

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.

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

This section summarizes the changes in CUDA 11.0 Update 1 since the 11.0 GA release.

New Features

• General CUDA
  • CUDA 11.0 Update 1 is a minor update that is binary compatible with CUDA 11.0. This release will work with all versions of the R450 NVIDIA driver.
  • Added support for SUSE SLES 15.2 on x86_64 and arm64 platforms.
  • A new user stream priority value has been added. This will lower the value of greatestPriority returned from cudaDeviceGetStreamPriorityRange by 1, allowing for applications to create "low, medium, high" priority streams rather than just "low, high".
• CUDA Compiler
  • NVCC now supports new flags --forward-unknown-to-host-compiler and --forward-unknown-to-host-linker to forward unknown flags to the host compiler and linker, respectively. Please see the nvcc documentation or output of nvcc --help for details.
• cuBLAS
  • The cuBLAS API was extended with a new function: cublasSetWorkspace(), which allows the user to set the cuBLAS library workspace to a user-owned device buffer, which will be used by cuBLAS to execute all subsequent calls to the library on the currently set stream.
  • The cuBLASLt experimental logging mechanism can be enabled in two ways:
    • By setting the following environment variables before launching the target application:
      • CUBLASLT_LOG_LEVEL=<level> - where level is one of the following levels:
        • "0" - Off - logging is disabled (default)
        • "1" - Error - only errors will be logged
        • "2" - Trace - API calls that launch CUDA kernels will log their parameters and important information
        • "3" - Hints - hints that can potentially improve the application's performance
        • "4" - Heuristics - heuristics log that may help users to tune their parameters
        • "5" - API Trace - API calls will log their parameter and important information
      • CUBLASLT_LOG_MASK=<mask> - while mask is a combination of the following masks:
        • "0" - Off
        • "1" - Error
        • "2" - Trace
        • "4" - Hints
        • "8" - Heuristics
        • "16" - API Trace
      • CUBLASLT_LOG_FILE=<value> - where value is a file name in the format of "<file_name>.%i"; %i will be replaced with the process ID. If CUBLASLT_LOG_FILE is not defined, the log messages are printed to stdout.
    • By using the runtime API functions defined in the cublasLt header:
      • typedef void(*cublasLtLoggerCallback_t)(int logLevel, const char* functionName, const char* message) - A type of callback function pointer.
      • cublasStatus_t cublasLtLoggerSetCallback(cublasLtLoggerCallback_t callback) - Allows to set a call back functions that will be called for every message that is logged by the library.
      • cublasStatus_t cublasLtLoggerSetFile(FILE* file) - Allows to set the output file for the logger. The file must be open and have write permissions.
      • cublasStatus_t cublasLtLoggerOpenFile(const char* logFile) - Allows to give a path in which the logger should create the log file.
      • cublasStatus_t cublasLtLoggerSetLevel(int level) - Allows to set the log level to one of the above mentioned levels.
      • cublasStatus_t cublasLtLoggerSetMask(int mask) - Allows to set the log mask to a combination of the above mentioned masks.
      • cublasStatus_t cublasLtLoggerForceDisable() - Allows to disable to logger for the entire session. Once this API is being called, the logger cannot be reactivated in the current session.

Resolved Issues

• CUDA Libraries: CURAND
  • Fixed an issue that caused linker errors about the multiple definitions of mtgp32dc_params_fast_11213 and mtgpdc_params_11213_num when including curand_mtgp32dc_p_11213.h in different compilation units.
• CUDA Libraries: cuBLAS
  • Some tensor core accelerated strided batched GEMM routines would result in misaligned memory access exceptions when batch stride wasn't a multiple of 8.
  • Tensor core accelerated cublasGemmBatchedEx (pointer-array) routines would use slower variants of kernels assuming bad alignment of the pointers in the pointer array. Now it assumes that pointers are well aligned, as noted in the documentation.
• Math API
  • nv_bfloat16 comparison functions could trigger a fault with misaligned addresses.
  • Performance improvements in half and nv_bfloat16 basic arithmetic implementations.
• CUDA Tools
  • A non-deterministic hanging issue on calls to cusolverRfBatchSolve() has been resolved.
  • Resolved an issue where using libcublasLt_sparse.a pruned by nvprune caused applications to fail with the error cudaErrorInvalidKernelImage.
  • Fixed an issue that prevented code from building in Visual Studio if placed inside a .cu file.

Known Issues

• nvJPEG
  • NVJPEG_BACKEND_GPU_HYBRID has an issue when handling bit-streams which have corruption in the scan.

Deprecations

None.

This section summarizes the changes in CUDA 11.0 GA since the 11.0 RC release.

General CUDA

• Added support for Ubuntu 20.04 LTS on x86_64 platforms.
• Arm server platforms (arm64 sbsa) are supported with NVIDIA T4 GPUs.

NPP New Features

• Batched Image Label Markers Compression that removes sparseness between marker label IDs output from LabelMarkers call.
• Image Flood Fill functionality fills a connected region of an image with a specified new value.
• Stability and performance fixes to Image Label Markers and Image Label Markers Compression.

nvJPEG New Features

• nvJPEG allows the user to allocate separate memory pools for each chroma subsampling format. This helps avoid memory re-allocation overhead. This can be controlled by passing the newly added flag NVJPEG_FLAGS_ENABLE_MEMORY_POOLS to the nvjpegCreateEx API.
• nvJPEG encoder now allow compressed bitstream on the GPU Memory.

cuBLAS New Features

• cuBLASLt Matrix Multiplication adds support for fused ReLU and bias operations for all floating point types except double precision (FP64).
• Improved batched TRSM performance for matrices larger than 256.

cuSOLVER New Features

• Add 64-bit API of GESVD. The new routine cusolverDnGesvd_bufferSize() fills the missing parameters in 32-bit API cusolverDn[S|D|C|Z]gesvd_bufferSize() such that it can estimate the size of the workspace accurately.
• Added the single process multi-GPU Cholesky factorization capabilities POTRF, POTRS and POTRI in cusolverMG library.

cuSOLVER Resolved Issues

• Fixed an issue where SYEVD/SYGVD would fail and return error code 7 if the matrix is zero and the dimension is bigger than 25.

cuSPARSE New Features

• Added new Generic APIs for Axpby (cusparseAxpby), Scatter (cusparseScatter), Gather (cusparseGather), Givens rotation (cusparseRot). __nv_bfloat16/ __nv_bfloat162 data types and 64-bit indices are also supported.

• This release adds the following features for cusparseSpMM:

  • Support for row-major layout for cusparseSpMM for both CSR and COO format
  • Support for 64-bit indices
  • Support for __nv_bfloat16 and __nv_bfloat162 data types
  • Support for the following strided batch mode:

    • Ci=A⋅Bi

    • Ci=Ai⋅B

    • Ci=Ai⋅Bi

cuFFT New Features

• cuFFT now accepts __nv_bfloat16 input and output data type for power-of-two sizes with single precision computations within the kernels.

Known Issues

• Note that starting with CUDA 11.0, the minimum recommended GCC compiler is at least GCC 5 due to C++11 requirements in CUDA libraries e.g. cuFFT and CUB. On distributions such as RHEL 7 or CentOS 7 that may use an older GCC toolchain by default, it is recommended to use a newer GCC toolchain with CUDA 11.0. Newer GCC toolchains are available with the Red Hat Developer Toolset.
• cublasGemmStridedBatchedEx() and cublasLtMatmul() may cause misaligned memory access errors in rare cases, when Atype or Ctype is CUDA_R_16F or CUDA_R_16BF and strideA, strideB or strideC are not multiple of 8 and internal heuristics determines to use certain Tensor Core enabled kernels. A suggested work around is to specify CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_<A,B,C,D>_BYTES accordingly to matrix stride used when calling cublasLtMatmulAlgoGetHeuristic().

Deprecations

The following functions have been removed:

• cusparse<t>gemmi()
• cusparseXaxpyi, cusparseXgthr, cusparseXgthrz, cusparseXroti, cusparseXsctr

CUDA Components

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

For CUDA 11.0 Update 1, the table below indicates the versions:

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.

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

• CUDA 11.0 adds support for the NVIDIA Ampere GPU microarchitecture (compute_80 and sm_80).
• CUDA 11.0 adds support for NVIDIA A100 GPUs and systems that are based on A100. The A100 GPU adds the following capabilities for compute via CUDA:
  • Alternate floating point data format Bfloat16 (__nv_bfloat16) and compute type TF32 (tf32)
  • Double precision matrix multiply accumulate through the DMMA instruction (see note on WMMA in CUDA C++ and mma in PTX)
  • Support for asynchronous copy instructions that allow copying of data asynchronously (LDGSTS instruction and the corresponding cp.async.* PTX instructions)
  • Cooperative groups improvements, which allow reduction operation across threads in a warp (using the redux.sync instruction)
  • Support for hardware partitioning via Multi-Instance GPU (MIG). See the driver release notes on more information on the corresponding NVML APIs and nvidia-smi CLI tools for configuring MIG instances
• Added the 7.0 version of the Parallel Thread Execution instruction set architecture (ISA). For more details on new (sm_80 target, new instructions, new floating point data types in .bf16, .tf32, and new mma shapes) and deprecated instructions, see this section in the PTX documentation.

• CUDA 11.0 adds support for the Arm server platform (arm64 SBSA). Note that with this release, only the following platforms are supported with Tesla V100 GPU:

  • HPE Apollo 70 (using Marvell ThunderX2™ CN99XX)

  • Gigabyte R2851 (using Marvell ThunderX2™ CN99XX)

  • Huawei TaiShan 2280 V2 (using Huawei Kunpeng 920)

• CUDA supports a wide range of Linux and Windows distributions. For a full list of supported operating systems, see system requirements for more information. The following new Linux distributions are supported in CUDA 11.0.

  For x86 (x86_64):

  • Red Hat Enterprise Linux (RHEL) 8.1

  • Ubuntu 18.04.4 LTS

  For Arm (arm64):

  • SUSE SLES 15.1

  For POWER (ppc64le):

  • Red Hat Enterprise Linux (RHEL) 8.1

• CUDA C++ includes support for new data types to support new 16-bit floating point data (with 1-sign bit, 8-bit exponent and 7-bit mantissa): __nv_bfloat16 and __nv_bfloat162. See include/cuda_bf16.hpp and the CUDA Math API for more information on the datatype definition and supported arithmetic operations.

• CUDA 11.0 adds the following support for WMMA:

  • Added support for double (FP64) to the list of available input/output types for 8x8x4 shapes (DMMA.884)

  • Added support for __nv_bfloat16 and tf32 precision formats for the HMMA 16x16x8 shape

• Added support for cooperative kernels in CUDA graphs, including stream capture for cuLaunchCooperativeKernel.
• The CUDA_VISIBLE_DEVICES variable has been extended to add support for enumerating Multiple Instance GPUs (MIG) in NVIDIA A100/GA100 GPUs.
• CUDA 11.0 adds a specification for inter-task memory ordering in the "API Synchronization" subsection of the PTX memory model and allows CUDA's implementation to be optimized consistent with this addition. In rare cases, code may have assumed a stronger ordering than required by the added specification and may notice a functional regression. The environment variable CUDA_FORCE_INTERTASK_SYSTEM_FENCE may be set to a value of "0" to disable post-10.2 inter-task fence optimizations, or "1" to enable them for 445 and newer drivers. If the variable is not set, code compiled entirely against CUDA 10.2 or older will disable the optimizations and code compiled against 11.0 or newer will enable them. Code with mixed versions may see a combination.

This release of the toolkit includes the following updates:

• CUDA Math libraries toolchain uses C++11 features, and a C++11-compatible standard library is required on the host.
• cuBLAS 11.0.0
• cuFFT 10.1.3
• cuRAND 10.2.0
• cuSPARSE 11.0.0
• cuSOLVER 10.4.0
• NPP 11.0.0
• nvJPEG 11.0.0

• cuBLASLt Matrix Multiplication adds support for fused ReLU and bias operations for all floating point types except double precision (FP64).
• Improved batched TRSM performance for matrices larger than 256.
• Many performance improvements have been implemented for the NVIDIA Ampere, Volta, and Turing Architecture based GPUs.
• With this release, on Linux systems, the cuBLAS libraries listed below are now installed in the /usr/local/cuda-11.0 (./lib64/ for lib and ./include/ for headers) directories as shared and static libraries.
• The cuBLASLt logging mechanism can be enabled by setting the following environment variables before launching the target application:
  • CUBLASLT_LOG_LEVEL=<level> - while level is one of the following levels:
    • "0" - Off - logging is disabled (default)
    • "1" - Error - only errors will be logged
    • "2" - Trace - API calls will be logged with their parameters and important information
  • CUBLASLT_LOG_FILE=<value> - while value is a file name in the format of "<file_name>.%i", %i will be replaced with the process id. If CUBLASLT_LOG_FILE is not defined, the log messages are printed to stdout.
• For matrix multiplication APIs:
  • cublasGemmEx, cublasGemmBatchedEx, cublasGemmStridedBatchedEx and cublasLtMatmul has new data type support for BFLOAT16 (CUDA_R_16BF).
  • The newly introduced computeType_t changes function prototypes on the API: cublasGemmEx, cublasGemmBatchedEx, and cublasGemmStridedBatchedEx have a new signature that uses cublasComputeType_t for the computeType parameter. Backward compatibility is ensured with internal mapping for C users and with added overload for C++ users.
  • cublasLtMatmulDescCreate, cublasLtMatmulAlgoGetIds, and cublasLtMatmulAlgoInit have new signatures that use cublasComputeType_t.
  • A new compute type TensorFloat32 (TF32) has been added to provide tensor core acceleration for FP32 matrix multiplication routines with full dynamic range and increased precision compared to BFLOAT16.
  • New compute modes Default, Pedantic, and Fast have been introduced to offer more control over compute precision used.
  • *Init versions of *Create functions are introduced in cublasLt to allow for simple wrappers that hold all descriptors on stack.
  • Experimental feature of cuBLASLt API logging is introduced.
  • Tensor cores are now enabled by default for half-, and mixed-precision- matrix multiplications.
  • Double precision tensor cores (DMMA) are used automatically.
  • Tensor cores can now be used for all sizes and data alignments and for all GPU architectures:
    • Selection of these kernels through cuBLAS heuristics is automatic and will depend on factors such as math mode setting as well as whether it will run faster than the non-tensor core kernels.
    • Users should note that while these new kernels that use tensor cores for all unaligned cases are expected to perform faster than non-tensor core based kernels but slower than kernels that can be run when all buffers are well aligned.

• cuFFT now accepts __nv_bfloat16 input and output data type for power-of-two sizes with single precision computations within the kernels.
• Reoptimized power of 2 FFT kernels on Volta and Turing architectures.

• Added new Generic APIs for Axpby (cusparseAxpby), Scatter (cusparseScatter), Gather (cusparseGather), Givens rotation (cusparseRot). __nv_bfloat16/ __nv_bfloat162 data types and 64-bit indices are also supported.

• This release adds the following features for cusparseSpMM:

  • Support for row-major layout for cusparseSpMM for both CSR and COO format
  • Support for 64-bit indices
  • Support for __nv_bfloat16 and __nv_bfloat162 data types
  • Support for the following strided batch mode:

    • Ci=A⋅Bi

    • Ci=Ai⋅B

    • Ci=Ai⋅Bi

• Added new generic APIs and improved performance for sparse matrix-sparse matrix multiplication (SpGEMM): cusparseSpGEMM_workEstimation, cusparseSpGEMM_compute, and cusparseSpGEMM_copy.
• SpVV: added support for __nv_bfloat16.

• Add 64-bit API of GESVD. The new routine cusolverDnGesvd_bufferSize() fills the missing parameters in 32-bit API cusolverDn[S|D|C|Z]gesvd_bufferSize() such that it can estimate the size of the workspace accurately.
• Added the single process multi-GPU Cholesky factorization capabilities POTRF, POTRS and POTRI in cusolverMG library.
• Added 64-bit APIs for getrf, getrs, potrf, potrs, geqrf, syevd and syevdx.

• This release adds more control and helpful functionalities for the Tensor Cores Accelerated Iterative Refinement Solver TCAIRS.

  • In addition to the previously released TCAIRS-LU based solver a new TCAIRS-QR based solver for real and complex systems with one or multiple right hand sides is introduced.
  • In addition to the FP64, FP32 and FP16 computational precisions two new computational precisions types are supported: the BFLOAT16 and the TensorFloat32 (TF32). Both TCAIRS-LU and TCAIRS-QR come with the five computational precisions options. Tensor Float (TF32), introduced with NVIDIA Ampere Architecture GPUs, is the most robust tensor core accelerated compute mode for the iterative refinement solver. It is able to solve the widest range of problems in HPC arising from different applications and provides up to 4X and 5X speedup for real and complex systems, respectively. On Volta and Turing architecture GPUs, half precision tensor core acceleration is recommended. In cases where the iterative refinement solver fails to converge to the desired accuracy (double precision in most cases), it is recommended to use full double precision factorization and solve (such as [D,Z]GETRF and [D,Z]GETRS or cusolverDn[DD,ZZ]gesv).
  • TCAIRS (LU and QR) are released with easy LAPACK-style APIs (drop-in replacement) as well as expert generic APIs that give users a lot of control of the internal of the solver. These support all five computational precisions.
  • Simple and Expert APIs now support all five computational precisions.
  • Expert TCAIRS solvers APIs allow users to choose between 4 methods of refinement.
  • Expert TCAIRS solvers APIs now support a no-refinement option which means they behave as standard Xgesv/Xgels solvers without refinement.
• Performance improvements of the TCAIRS solver for NVIDIA Ampere, Volta, and Turing Architecture based GPUs.

• Batched Image Label Markers Compression that removes sparseness between marker label IDs output from LabelMarkers call.
• Image Flood Fill functionality fills a connected region of an image with a specified new value.
• Added batching support for nppiLabelMarkersUF functions.
• Added the nppiCompressMarkerLabelsUF_32u_C1IR function.

• Added nppiSegmentWatershed functions.

• Added sample apps on GitHub demonstrating the use of NPP application managed stream contexts along with watershed segmentation and batched and compressed UF image label markers functions.
• Added support for non-blocking streams.

• nvJPEG allows the user to allocate separate memory pools for each chroma subsampling format. This helps avoid memory re-allocation overhead. This can be controlled by passing the newly added flag NVJPEG_FLAGS_ENABLE_MEMORY_POOLS to the nvjpegCreateEx API.
• nvJPEG encoder now allow compressed bitstream on the GPU Memory.
• Hardware accelerated decode is now supported on NVIDIA A100.
• The nvJPEG decode API (nvjpegDecodeJpeg()) now has the flexibility to select the backend when creating nvjpegJpegDecoder_t object. The user has the option to call this API instead of making three separate calls to nvjpegDecodeJpegHost(), nvjpegDecodeJpegTransferToDevice(), and nvjpegDecodeJpegDevice().

• Add arithmetic support for __nv_bfloat16 floating-point data type with 8 bits of exponent, 7 explicit bits of mantissa.
• Performance and accuracy improvements in single precision math functions: fmodf, expf, exp10f, sinhf, and coshf.

The following features are deprecated or dropped in the current release of the CUDA software. Deprecated 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 Red Hat Enterprise Linux (RHEL) and CentOS 6.x is dropped.
• Support for Kepler sm_30 and sm_32 architecture based products is dropped.

• Support for the following compute capabilities are deprecated in the CUDA Toolkit:

  • sm_35 (Kepler)

  • sm_37 (Kepler)

  • sm_50 (Maxwell)

  For more information on GPU products and compute capability, see https://developer.nvidia.com/cuda-gpus.

• Support for Linux cluster packages is dropped.
• CUDA 11.0 does not support macOS for developing and running CUDA applications. Note that some of the CUDA developer tools are still supported on macOS hosts for remote (target) debugging and profiling. See the CUDA Tools section for more information.

• CUDA 11.0 no longer supports development of CUDA applications on the following Windows distributions:

  • Windows 7
  • Windows 8
  • Windows Server 2012 R2
• nvGraph is no longer included as part of the CUDA Toolkit installers. See the cuGraph project as part of RAPIDS; the project includes algorithms from nvGraph and more.
• The context creation flag CU_CTX_MAP_HOST (to support mapped pinned allocations) is deprecated and will be removed in a future release of CUDA.

CUDA Developer Tools

• Nsight Eclipse Edition standalone is dropped in CUDA 11.0.
• Nsight Compute does not support profiling on Pascal architectures.
• Nsight VSE, Nsight EE Plugin, cuda-gdb, nvprof, Visual Profiler, and memcheck are reducing support for the following architectures:
  • Support for Kepler sm_30 and sm_32 architecture based products (deprecated since CUDA 10.2) has beeen dropped.
  • Support for the following compute capabilities (deprecated since CUDA 10.2) will be dropped in an upcoming CUDA release:
    • sm_35 (Kepler)
    • sm_37 (Kepler)
    • sm_50 (Maxwell)

CUDA Libraries - cuBLAS

• Algorithm selection in cublasGemmEx APIs (including batched variants) is non-functional for NVIDIA Ampere Architecture GPUs. Regardless of selection it will default to a heuristics selection. Users are encouraged to use the cublasLt APIs for algorithm selection functionality.
• The matrix multiply math mode CUBLAS_TENSOR_OP_MATH is being deprecated and will be removed in a future release. Users are encouraged to use the new cublasComputeType_t enumeration to define compute precision.

CUDA Libraries -- cuSOLVER

• TCAIRS-LU expert cusolverDnIRSXgesv() and some of its configuration functions undergo a minor API change.

CUDA Libraries -- cuSPARSE

The following functions have been removed:

• cusparse<t>gemmi()
• cusparseXaxpyi, cusparseXgthr, cusparseXgthrz, cusparseXroti, cusparseXsctr
• Hybrid format enums and helper functions: cusparseHybPartition_t, cusparseHybPartition_t, cusparseCreateHybMat, cusparseDestroyHybMat
• Triangular solver enums and helper functions: cusparseSolveAnalysisInfo_t, cusparseCreateSolveAnalysisInfo, cusparseDestroySolveAnalysisInfo
• Sparse dot product: cusparseXdoti, cusparseXdotci
• Sparse matrix-vector multiplication: cusparseXcsrmv, cusparseXcsrmv_mp
• Sparse matrix-matrix multiplication: cusparseXcsrmm, cusparseXcsrmm2
• Sparse triangular-single vector solver: cusparseXcsrsv_analysis, cusparseCsrsv_analysisEx, cusparseXcsrsv_solve, cusparseCsrsv_solveEx
• Sparse triangular-multiple vectors solver: cusparseXcsrsm_analysis, cusparseXcsrsm_solve
• Sparse hybrid format solver: cusparseXhybsv_analysis, cusparseShybsv_solve
• Extra functions: cusparseXcsrgeamNnz, cusparseScsrgeam, cusparseXcsrgemmNnz, cusparseXcsrgemm
• Incomplete Cholesky Factorization, level 0: cusparseXcsric0
• Incomplete LU Factorization, level 0: cusparseXcsrilu0, cusparseCsrilu0Ex
• Tridiagonal Solver: cusparseXgtsv, cusparseXgtsv_nopivot
• Batched Tridiagonal Solver: cusparseXgtsvStridedBatch
• Reordering: cusparseXcsc2hyb, cusparseXcsr2hyb, cusparseXdense2hyb, cusparseXhyb2csc, cusparseXhyb2csr, cusparseXhyb2dense

The following functions have been deprecated:

• SpGEMM: cusparseXcsrgemm2_bufferSizeExt, cusparseXcsrgemm2Nnz, cusparseXcsrgemm2

CUDA Libraries -- nvJPEG

• The following multiphase APIs have been removed:

  • nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseOne

  • nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseTwo

  • nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseThree

  • nvjpegStatus_t NVJPEGAPI nvjpegDecodeBatchedPhaseOne

  • nvjpegStatus_t NVJPEGAPI nvjpegDecodeBatchedPhaseTwo

• Fixed an issue where GPU passthrough on arm64 systems was not functional. GPU passthrough is now supported on arm64, but there may be a small performance impact to workloads (compared to bare-metal) on some system configurations.
• Fixed an issue where starting X on systems with arm64 CPUs and NVIDIA GPUs would result in a crash.

• Reduced R2C/C2R plan memory usage to previous levels.
• Resolved bug introduced in 10.1 update 1 that caused incorrect results when using custom strides, batched 2D plans and certain sizes on Volta and later.

• Introduced CURAND_ORDERING_PSEUDO_LEGACY ordering. Starting with CUDA 10.0, the ordering of random numbers returned by MTGP32 and MRG32k3a generators are no longer the same as previous releases despite being guaranteed by the documentation for the CURAND_ORDERING_PSEUDO_DEFAULT setting. The CURAND_ORDERING_PSEUDO_LEGACY provides pre-CUDA 10.0 ordering for MTGP32 and MRG32k3a generators.
• Starting with CUDA 11.0 CURAND_ORDERING_PSEUDO_DEFAULT is the same as CURAND_ORDERING_PSEUDO_BEST for all generators except MT19937. Only CURAND_ORDERING_PSEUDO_LEGACY is guaranteed to provide the same for all future cuRAND releases.

• Fixed an issue where SYEVD/SYGVD would fail and return error code 7 if the matrix is zero and the dimension is bigger than 25.
• Fixed a race condition of GETRF when running with other kernels concurrently.
• Fixed the pivoting strategy of [c|z]getrf to be compliant with LAPACK.
• Fixed NAN and INF values that might result in the TCAIRS-LU solver when FP16 was used and matrix entries are outside FP16 range.
• Fixed the pivoting strategy of [c|z]getrf to be compliant with LAPACK.
• Previously, cusolverSpDcsrlsvchol could overflow 32-bit signed integer when zero fill-in is huge. Such overflow causes memory corruption. cusolverSpDcsrlsvchol now returns CUSOLVER_STATUS_ALLOC_FAILED when integer overflow happens.

• Corrected documented maximum ulp error thresholds in erfcinvf and powf.

• Improved cuda_fp16.h interoperability with Visual Studio C++ compiler.
• Updated libdevice user guide and CUDA math API definitions for j1, j1f, fmod, fmodf, ilogb, and ilogbf math functions.

• Stability and performance fixes to Image Label Markers and Image Label Markers Compression.
• Improved quality of nppiLabelMarkersUF functions.
• nppiCompressMarkerLabelsUF_32u_C1IR can now handle a huge number of labels generated by the nppiLabelMarkersUF function.

• The nanosleep PTX instruction for Volta and Turing is not supported in this release of CUDA. It may be fully supported in a future releaseof CUDA. There may be references to nanosleep in the compiler headers (such as include/crt/sm_70_rt*). Developers are encouraged to not use this instruction in their CUDA applications on Volta and Turing until it is fully supported.
• Read-only memory mappings (via CU_MEM_ACCESS_FLAGS_PROT_READ in CUmemAccess_flags) with cuMemSetAccess() API will result in an error. Read-only memory mappings are currently not supported and may be added in a future release of CUDA.
• Note that the R450 driver bundled with this release of CUDA 11 does not officially support Windows 10 May 2020 Update and may have issues
• GPU workloads are executed on GPU hardware engines. On Windows, these engines are represented by “nodes”. With Hardware Scheduling disabled for Windows 10 May 2020 Update, some NVIDIA GPU engines are represented by virtual nodes, and multiple virtual nodes may represent more than one GPU hardware engine. This is done to achieve better parallel execution of workloads. Examples of these virtual nodes are “Cuda”, “Compute_0”, “Compute_1”, and “Graphics_1” as shown in Windows Task Manager. These correspond to the same underlying hardware engines as the “3D” node in Windows Task Manager. With Hardware Scheduling enabled, the virtual nodes are no longer needed, and Task Manager shows only the “3D”node for the previous “3D” node and multiple virtual nodes shown before, combined. CUDA is still supported in this scenario.

• Sample 0_Simple/simpleSeparateCompilation fails to build with the error "cc: unknown target 'gcc_ntox86". The workaround to allow the build to pass is by passing additionally EXTRA_NVCCFLAGS="-arbin $QNX_HOST/usr/bin/aarch64-unknown-nto-qnx7.0.0-ar".

• cuFFT modifies C2R input buffer for some non-strided FFT plans.
• There is a known issue with certain cuFFT plans that causes an assertion in the execution phase of certain plans. This applies to plans with all of the following characteristics: real input to complex output (R2C), in-place, native compatibility mode, certain even transform sizes, and more than one batch.

• The nppiCopy API is limited by CUDA thread for large image size. Maximum image limits is a minimum of 16 * 65,535 = 1,048,560 horizontal pixels of any data type and number of channels and 8 * 65,535 = 524,280 vertical pixels for a maximum total of 549,739,036,800 pixels.

• NVJPEG_BACKEND_GPU_HYBRID has an issue when handling bit-streams which have corruption in the scan.

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