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Release Notes :: CUDA Toolkit Documentation

The release notes for the NVIDIA® CUDA® Toolkit can be found online at

.

Note: The release notes have been reorganized into two major sections: the general CUDA release notes, and the CUDA libraries release notes including historical information for 11.x releases.

CUDA Components

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

For CUDA 11.5, 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 3. 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-compatibility-and-upgrades.

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

The minimum required driver version for CUDA minor version compatibility is shown below. CUDA minor version compatibility is described in detail in https://docs.nvidia.com/deploy/cuda-compatibility/index.html

Table 2. CUDA Toolkit and Minimum Required Driver Version for CUDA minor version compatibility CUDA Toolkit Minimum Required Driver Version for CUDA minor version compatibility* Linux x86_64 Driver Version Windows x86_64 Driver Version CUDA 11.5.x >=450.80.02 >=456.38 CUDA 11.4.x >=450.80.02 >=456.38 CUDA 11.3.x >=450.80.02 >=456.38 CUDA 11.2.x >=450.80.02 >=456.38 CUDA 11.1 (11.1.0) >=450.80.02 >=456.38 CUDA 11.0 (11.0.3) >=450.36.06** >=456.38

* Using a Minimum Required Version that is different from Toolkit Driver Version could be allowed in compatibility mode -- please read the CUDA Compatibility Guide for details.

** CUDA 11.0 was released with an earlier driver version, but by upgrading to 450.80.02 driver as indicated, minor version compatibility is possible across the CUDA 11.x family of toolkits.

The version of the development NVIDIA GPU Driver packaged in each CUDA Toolkit release is shown 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

11.5

• Device-side caching behavior is now configurable with annotated pointers.
• Prefix sums (scans) for cooperative groups: Added four new functions for inclusive and exclusive scans.
• Support for NvSciBufGeneralAttrKey_EnableGpuCompression in CUDA will be available in r470TRD2.
• Preview release of a new data type, __int128, usable with compatible host compilers. As it is a preview, there is no broad support for math operations, library support, dev tools, and so on.
• Added native support for signed and unsigned normalized 8- and 16-bit types.
• Improved interoperability with graphics frameworks: Added support for normalized integer and block-compressed data types.
• For multi-process sharing of GPUs, CUDA now supports per-process memory access policies.
• Linking is supported with cubins larger than 2 GB.
• GSP-RM is enabled as opt-in for Turing+ Tesla GPUs.
• GA release of CUDA Python.

• CUDA now provides a static version of the NVRTC library.
• builtin_assume can now be used to specify address space to allow for efficient loads and stores.
• Added support for numerous pragmas that offer more control over diagnostic messages.
• To generate code for multiple architectures at the same time, use the -arch=all or -arch=all-major options.
• Deterministic code generation: compiled bits will not change between nvcc invocations without semantic source code changes.

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

• NVIDIA Driver support for Kepler is removed beginning with R495. CUDA Toolkit development support for Kepler continues through CUDA 11.x.

This section covers CUDA Libraries release notes for 11.x releases.

• CUDA Math Libraries toolchain uses C++11 features, and a C++11-compatible standard library (libstdc++ >= 20150422) is required on the host.
• CUDA Math libraries are no longer shipped for SM30 and SM32.
• Support for the following compute capabilities are deprecated for all libraries:
  • sm_35 (Kepler)
  • sm_37 (Kepler)

• New Features
  • Vector (and batched) alpha support for per-row scaling in TN int32 math Matmul with int8 output. See CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST and CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE.
  • New epilogue options have been added to support fusion in DLtraining: CUBLASLT_EPILOGUE_BGRADA and CUBLASLT_EPILOGUE_BGRADB which compute bias gradients based on matrices A and B respectively.
  • New auxiliary functions cublasGetStatusName(), cublasGetStatusString() have been added to cuBLAS that return the string representation and the description of the cuBLAS status (cublasStatus_t) respectively. Similarly, cublasLtGetStatusName(), cublasLtGetStatusString() have been added to cuBlasLt.
• Known Issues
  • cublasGemmBatchedEx() and cublas<t>gemmBatched() check the alignment of the input/output arrays of the pointers like they were the pointers to the actual matrices. These checks are irrelevant and will be disabled in future releases. This mostly affects half-precision inputGEMMs which might require 16-byte alignment, and array of pointers could only be aligned by 8-byte boundary.
• Resolved Issues
  • cublasLtMatrixTransform can now operate on matrices with dimensions greater than 65535.
  • Fixed out-of-bound access in GEMM and Matmul functions, when split K or non-default epilogue is used and leading dimension of the output matrix exceeds int32_t limit.
  • NVBLAS now uses lazy loading of the CPU BLAS library on Linux to avoid issues caused by preloading libnvblas.so in complex applications that use fork and similar APIs.
  • Resolved symbols name conflict when using cuBlasLt static library with static TensorRT or cuDNN libraries.

• Resolved Issues
  • Some gemv cases were producing incorrect results if the matrix dimension (n or m) was large, for example 2^20.

• New Features
  • Some new kernels have been added for improved performance but have the limitation that only host pointers are supported for scalars (for example, alpha and beta parameters). This limitation is expected to be resolved in a future release.
  • New epilogues have been added to support fusion in ML training. These include:
    • ReLuBias and GeluBias epilogues that produce an auxiliary output which is used on backward propagation to compute the corresponding gradients.
    • DReLuBGrad and DGeluBGrad epilogues that compute the backpropagation of the corresponding activation function on matrix C, and produce bias gradient as a separate output. These epilogues require auxiliary input mentioned in the bullet above.
• Resolved Issues
  • 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.
• Known Issues
  • To be able to access the fastest possible kernels through cublasLtMatmulAlgoGetHeuristic() you need to set CUBLASLT_MATMUL_PREF_POINTER_MODE_MASK in search preferences to CUBLASLT_POINTER_MODE_MASK_HOST or CUBLASLT_POINTER_MODE_MASK_NO_FILTERING. By default, heuristics query assumes the pointer mode may change later and only returns algo configurations that support both _HOST and _DEVICE modes. Without this, newly added kernels will be excluded and it will likely lead to a performance penalty on some problem sizes.
• Deprecated Features
  • Linking with static cublas and cublasLt libraries on Linux now requires using gcc-5.2 and compatible or higher due to C++11 requirements in these libraries.

• Known Issues
  • The planar complex matrix descriptor for batched matmul has inconsistent interpretation of batch offset.
  • Mixed precision operations with reduction scheme CUBLASLT_REDUCTION_SCHEME_OUTPUT_TYPE (might be automatically selected based on problem size by cublasSgemmEx() or cublasGemmEx() too, unless CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION math mode bit is set) not only stores intermediate results in output type but also accumulates them internally in the same precision, which may result in lower than expected accuracy. Please use CUBLASLT_MATMUL_PREF_REDUCTION_SCHEME_MASK or CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION if this results in numerical precision issues in your application.

• New Features
  • cuBLASLt Logging is officially stable and no longer experimental. cuBLASLt Logging APIs are still experimental and may change in future releases.
• Resolved Issues
  • 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

• Resolved Issues
  • 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.

• New Features
  • 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.
  • 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> -- where 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 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.

• 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.

• New Features
  • Many performance improvements have been implemented for NVIDIA Ampere, Volta, and Turing Architecture based GPUs.
  • 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 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 added new data type support for __nv_bfloat16 (CUDA_R_16BF).
    • 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.
    • 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.
• Deprecated Features
  • 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.

• Known Issues
  • FFTs of certain sizes in single and double precision (multiples of size 6) could fail on future devices. This issue will be fixed in an upcoming release.

• Resolved Issues
  • Since cuFFT 10.3.0 (CUDA Toolkit 11.1), cuFFT may require user to make sure that all operations on input and output buffers are complete before calling cufft[Xt]Exec* if:
    • sm70 or later, 3D FFT, batch > 1, total size of transform is greater than 4.5MB
    • FFT size for all dimensions is in the set of the following sizes: {2, 4, 8, 16, 32, 64, 128, 3, 9, 81, 243, 729, 2187, 6561, 5, 25, 125, 625, 3125, 6, 36, 216, 1296, 7776, 7, 49, 343, 2401, 11, 121}
  • Some V100 FFTs were slower than expected. This issue is resolved.
• Known Issues
  • Some T4 FFTs are slower than expected.
  • Plans for FFTs of certain sizes in single precision (including some multiples of 1024 sizes, and some large prime numbers) could fail on future devices with less than 64 kB of shared memory. This issue will be fixed in an upcoming release.

• Resolved Issues
  • Some cuFFT multi-GPU plans exhibited very long creation times.
  • cuFFT sometimes produced incorrect results for real-to-complex and complex-to-real transforms when the total number of elements across all batches in a single execution exceeded 2147483647.
• Known Issues
  • Some V100 FFTs are slower than expected.
  • Some T4 FFTs are slower than expected.

• New Features
  • Performance improvements.
• Known Issues
  • Some T4 FFTs are slower than expected.
  • cuFFT may produce incorrect results for real-to-complex and complex-to-real transforms when the total number of elements across all batches in a single execution exceeds 2147483647.
  • Some cuFFT multi-GPU plans may exhibit very long creation time. Issue will be fixed in the next update.
  • cuFFT may produce incorrect results for transforms with strides when the index of the last element across all batches exceeds 2147483647 (see Advanced Data Layout).
• Deprecated Features
  • Support for callback functionality using separately compiled device code is deprecated on all GPU architectures. Callback functionality will continue to be supported for all GPU architectures.

• New Features
  • cuFFT shared libraries are now linked statically against libstdc++ on Linux platforms.
  • Improved performance of certain sizes (multiples of large powers of 3, powers of 11) in SM86.
• Known Issues
  • cuFFT planning and plan estimation functions may not restore correct context affecting CUDA driver API applications.
  • Plans with strides, primes larger than 127 in FFT size decomposition and total size of transform including strides bigger than 32GB produce incorrect results.

• Known Issues
  • cuFFT planning and plan estimation functions may not restore correct context affecting CUDA driver API applications.
  • Plans with strides, primes larger than 127 in FFT size decomposition and total size of transform including strides bigger than 32GB produce incorrect results.

• Resolved Issues
  • Previously, reduced performance of power-of-2 single precision FFTs was observed on GPUs with sm_86 architecture. This issue has been resolved.
  • Large prime factors in size decomposition and real to complex or complex to real FFT type no longer cause cuFFT plan functions to fail.
• Known Issues
  • cuFFT planning and plan estimation functions may not restore correct context affecting CUDA driver API applications.
  • Plans with strides, primes larger than 127 in FFT size decomposition and total size of transform including strides bigger than 32GB produce incorrect results.

• New Features
  • Multi-GPU plans can be associated with a stream using the cufftSetStream API function call.
  • Performance improvements for R2C/C2C/C2R transforms.
  • Performance improvements for multi-GPU systems.
• Resolved Issues
  • cuFFT is no longer stuck in a bad state if previous plan creation fails with CUFFT_ALLOC_FAILED.
  • Previously, single dimensional multi-GPU FFT plans ignored user input on cufftXtSetGPUswhichGPUs argument and assumed that GPUs IDs are always numbered from 0 to N-1. This issue has been resolved.
  • Plans with primes larger than 127 in FFT size decomposition or FFT size being a prime number bigger than 4093 do not perform calculations on second and subsequent cufftExecute* calls. The regression was introduced in cuFFT 11.1.
• Known Issues
  • cuFFT planning and plan estimation functions may not restore correct context affecting CUDA driver API applications.

• New Features
  • 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).
• Resolved Issues
  • 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.
• Known Issues
  • 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.

• New Features
  • 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.
• Resolved Issues
  • 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.
• Known Issues
  • 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.

• Resolved Issues
  • Fixed inconsistency between random numbers generated by GPU and host generators when CURAND_ORDERING_PSEUDO_LEGACY ordering is selected for certain generator types.

• Resolved Issues
  • 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.

• Resolved Issues
  • 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.

• Resolved Issues
  • 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.

• New Features
  • Introducing cusolverDnXtrtri, a new generic API for triangular matrix inversion (trtri).
  • Introducing cusolverDnXsytrs, a new generic API for solving systems of linear equations using a given factorized symmetric matrix from SYTRF.

• Known Issues
  • For values N<=16, cusolverDn[S|D|C|Z]syevjBatched hits out-of-bound access and may deliver the wrong result. The workaround is to pad the matrix A with a diagonal matrix D such that the dimension of [A 0 ; 0 D] is bigger than 16. The diagonal entry D(j,j) must be bigger than maximum eigenvalue of A, for example, norm(A, ‘fro’). After the syevj, W(0:n-1) contains the eigenvalues and A(0:n-1,0:n-1) contains the eigenvectors.

• New Features
  • New singular value decomposition (GESVDR) is added. GESVDR computes partial spectrum with random sampling, an order of magnitude faster than GESVD.
  • libcusolver.so no longer links libcublas_static.a; instead, it depends on libcublas.so. This reduces the binary size of libcusolver.so. However, it breaks backward compatibility. The user has to link libcusolver.so with the correct version of libcublas.so.

• Resolved Issues
  • cusolverDnIRSXgels sometimes returned CUSOLVER_STATUS_INTERNAL_ERROR when the precision is ‘z’. This issue has been fixed in CUDA 11.2; now cusolverDnIRSXgels works for all precisions.
  • ZSYTRF sometimes returned CUSOLVER_STATUS_INTERNAL_ERROR due to insufficient resources to launch the kernel. This issue has been fixed in CUDA 11.2.
  • GETRF returned early without finishing the whole factorization when the matrix was singular. This issue has been fixed in CUDA 11.2.

• Resolved Issues
  • 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.
• Known Issues

  • cusolverDnIRSXgels may return CUSOLVER_STATUS_INTERNAL_ERROR. when the precision is ‘z’ due to insufficient workspace which causes illegal memory access.

    The cusolverDnIRSXgels_bufferSize() does not report the correct size of workspace. To workaround the issue, the user has to add more workspace than what is reported by cusolverDnIRSXgels_bufferSize(). For example, if x is the size of workspace returned by cusolverDnIRSXgels_bufferSize(), then the user has to allocate (x + min(m,n)*sizeof(cuDoubleComplex)) bytes.

• New Features
  • 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.
• Deprecated Features

  The following 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

• 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.
• 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.

• Resolved Issues
  • cusparseSpSV and cusparseSpSM could produce wrong results
  • cusparseSpSV and cusparseSpSM do not work correctly when vecX == vecY or matB == matC.

• Known Issues
  • cusparseSpSV and cusparseSpSM could produce wrong results
  • cusparseSpSV and cusparseSpSM do not work correctly when vecX == vecY or matB == matC.

• New Features
  • Introduced a new routine for sparse matrix - sparse matrix multiplication (cusparseSpGEMMreuse) where the output matrix structure is reused for multiple computation. The new routine supports CSR storage format and mixed-precision computation.
  • Sparse triangular solver adds support for COO format.
  • Introduced a new routine for sparse triangular solver with multiple right-hand sides cusparseSpSM().
  • cusparseDenseToSparse() routine adds the conversion from dense matrix (row-major/column-major) to Blocked-ELL format.
  • Blocke-ELL format now support empty blocks
  • Better performance for Blocked-ELL SpMM with block size > 64, double data type, and alignments smaller than 128-byte on NVIDIA Ampere sm80.
  • All cuSPARSE APIs are now asynchronous on platforms that support stream ordered memory allocators https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#stream-ordered-querying-memory-support.
  • Improved NTVX trace with distinction between light calls and kernel routines
• Resolved Issues
  • cusparseCnnz_compress produced wrong results when the number of rows are greater than 128 * resident CTAs.
  • cusparseSnnz produced wrong results for some particular sparsity pattern.
• Deprecated Features
  • cusparseXcsrsm2_zeroPivot, cusparseXcsrsm2_solve, cusparseXcsrsm2_analysis, and cusparseScsrsm2_bufferSizeExt have been deprecated in favor of cusparseSpSM Generic APIs

• New Features

  Added new routine

  cusparesSpSV

  for sparse triangular solver with better performance. The new Generic API supports:

  • CSR storage format
  • Non-transpose, transpose, and transpose-conjugate operations
  • Upper, lower fill mode
  • Unit, non-unit diagonal type
  • 32-bit and 64-bit indices
  • Uniform data type computation
• Deprecated Features
  • cusparseScsrsv2_analysis, cusparseScsrsv2_solve, cusparseXcsrsv2_zeroPivot, and cusparseScsrsv2_bufferSize have been deprecated in favor of cusparseSpSV.

• Resolved Issues
  • cusparseDestroy(NULL) no longer crashes on Windows.
• Known Issues
  • cusparseDestroySpVec, cusparseDestroyDnVec, cusparseDestroySpMat, cusparseDestroyDnMat, cusparseDestroy with NULL argument could cause segmentation fault on Windows.

• New Features
  • New Tensor Core-accelerated Block Sparse Matrix - Matrix Multiplication (cusparseSpMM) and introduction of the Blocked-Ellpack storage format.
  • New algorithms for CSR/COO Sparse Matrix - Vector Multiplication (cusparseSpMV) with better performance.
  • Extended functionalities for cusparseSpMV:
    • Support for the CSC format.
    • Support for regular/complex bfloat16 data types for both uniform and mixed-precision computation.
    • Support for mixed regular-complex data type computation.
    • Support for deterministic and non-deterministic computation.
  • New algorithm (CUSPARSE_SPMM_CSR_ALG3) for Sparse Matrix - Matrix Multiplication (cusparseSpMM) with better performance especially for small matrices.
  • New routine for Sampled Dense Matrix - Dense Matrix Multiplication (cusparseSDDMM) which deprecated cusparseConstrainedGeMM and provides better performance.
  • Better accuracy of cusparseAxpby, cusparseRot, cusparseSpVV for bfloat16 and half regular/complex data types.
  • All routines support NVTX annotation for enhancing the profiler time line on complex applications.
• Resolved Issues
  • cusparseAxpby, cusparseGather, cusparseScatter, cusparseRot, cusparseSpVV, cusparseSpMV now support zero-size matrices.
  • cusparseCsr2cscEx2 now correctly handles empty matrices (nnz = 0).
  • cusparseXcsr2csr_compress now uses 2-norm for the comparison of complex values instead of only the real part.
• Known Issues

  cusparseDestroySpVec, cusparseDestroyDnVec, cusparseDestroySpMat, cusparseDestroyDnMat, cusparseDestroy with NULL argument could cause segmentation fault on Windows.

• Deprecated Features
  • cusparseConstrainedGeMM has been deprecated in favor of cusparseSDDMM.
  • cusparseCsrmvEx has been deprecated in favor of cusparseSpMV.
  • COO Array of Structure (CooAoS) format has been deprecated including cusparseCreateCooAoS, cusparseCooAoSGet, and its support for cusparseSpMV.

• New Features
  • 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
  • cusparseXdense2csr provides incorrect results for some matrix sizes.
• Deprecated Features
  • Legacy conversion routines: cusparseXcsc2dense, cusparseXcsr2dense, cusparseXdense2csc, cusparseXdense2csr

• 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

• 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

  • 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.
• Deprecated Features

  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

• Deprecations
  • The following undocumented CUDA Math APIs are deprecated and will be removed in a future release. Please consider switching to similar intrinsic APIs documented here: https://docs.nvidia.com/cuda/cuda-math-api/index.html
    • __device__ int mulhi(const int a, const int b)
    • __device__ unsigned int mulhi(const unsigned int a, const unsigned int b)
    • __device__ unsigned int mulhi(const int a, const unsigned int b)
    • __device__ unsigned int mulhi(const unsigned int a, const int b)
    • __device__ long long int mul64hi(const long long int a, const long long int b)
    • __device__ unsigned long long int mul64hi(const unsigned long long int a, const unsigned long long int b)
    • __device__ unsigned long long int mul64hi(const long long int a, const unsigned long long int b)
    • __device__ unsigned long long int mul64hi(const unsigned long long int a, const long long int b)
    • __device__ int float_as_int(const float a)
    • __device__ float int_as_float(const int a)
    • __device__ unsigned int float_as_uint(const float a)
    • __device__ float uint_as_float(const unsigned int a)
    • __device__ float saturate(const float a)
    • __device__ int mul24(const int a, const int b)
    • __device__ unsigned int umul24(const unsigned int a, const unsigned int b)
    • __device__ int float2int(const float a, const enum cudaRoundMode mode = cudaRoundZero)
    • __device__ unsigned int float2uint(const float a, const enum cudaRoundMode mode = cudaRoundZero)
    • __device__ float int2float(const int a, const enum cudaRoundMode mode = cudaRoundNearest)
    • __device__ float uint2float(const unsigned int a, const enum cudaRoundMode mode = cudaRoundNearest)

• Resolved Issues
  • Previous releases of CUDA were potentially delivering incorrect results in some Linux distributions for the following host Math APIs: sinpi, cospi, sincospi, sinpif, cospif, sincospif. If passed huge inputs like 7.3748776e+15 or 8258177.5 the results were not equal to 0 or 1. These have been corrected with this release.

• New Features
  • 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.

• Resolved Issues
  • nv_bfloat16 comparison functions could trigger a fault with misaligned addresses.
  • Performance improvements in half and nv_bfloat16 basic arithmetic implementations.

• New Features
  • 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.
• Resolved Issues

  • 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.

• New Features
  • New APIs added to compute Signed Anti-aliased Distance Transform using PBA, the anti-aliased Euclidean distance between pixel sites in images. This will improve the accuracy of distance transform.
    • nppiSignedDistanceTransformAbsPBA_xxxxx_C1R_Ctx() – Input and output combination supports (xxxxxx) - 32f, 32f64f, 64f
  • New API for Absolute Manhattan distance transform; another method to improve the accuracy of distance transform using Manhattan distance transform between pixels.
    • nppiDistanceTransformAbsPBA_xxxxx_C1R_Ctx() – Input and output combination supports (xxxxxx) - 8u16u, 8s16u, 16u16u, 16s16u, 8u32f, 8s32f, 16u32f, 16s32f, 8u64f, 8s64f, 16u64f, 16s64f, 32f64f, 64f
• Resolved Issues
  • Fix an issue in FilterMedian() API with add interpolation when mask even size.
  • Improved Contour function performance by parallelizing more of it and also improving quality.
  • Resolved an issue with Alpha composition used to accumulate output buffers multiple times.
  • Resolved an issue with nppiLabelMarkersUF_8u32u_C1R column processing incorrect results.

• New Features
  • New API FindContours .FindContours can be explained simply as a curve joining all the continuous points (along the boundary), having the same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.

• New Features

  New APIs added to compute Distance Transform using Parallel Banding Algorithm (PBA):

  • nppiDistanceTransformPBA_xxxxx_C1R_Ctx() – where xxxxx specifies the input and output combination: 8u16u, 8s16u, 16u16u, 16s16u, 8u32f, 8s32f, 16u32f, 16s32f
  • nppiSignedDistanceTransformPBA_32f_C1R_Ctx()
• Resolved Issues
  • Fixed the issue in which Label Markers adds zero pixel as object region.

• 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.

• 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.
  • 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.
• Resolved Issues
  • 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.
• Known Issues
  • 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.

• New Features

  nvJPEG decoder added new APIs to support region of interest (ROI) based decoding for batched hardware decoder:

  • nvjpegDecodeBatchedEx()
  • nvjpegDecodeBatchedSupportedEx()

• 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.

• 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.
  • 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().
• Known Issues
  • NVJPEG_BACKEND_GPU_HYBRID has an issue when handling bit-streams which have corruption in the scan.
• Deprecated Features

  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

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