NVIDIA CUDA Toolkit Release Notes

The Release Notes for the CUDA Toolkit.

1. CUDA 12.4 Release Notes

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

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 12.x releases.

1.1. CUDA Toolkit Major Component Versions

CUDA Components

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

For CUDA 12.4, the table below indicates the versions:

Table 1 CUDA 12.4 Component Versions

Component Name

Version Information

Supported Architectures

Supported Platforms

CUDA C++ Core Compute Libraries

Thrust

2.3.1

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows

CUB

2.3.1

libcu++

2.3.1

Cooperative Groups

12.3.x

CUDA Compatibility

12.4.35475792

aarch64-jetson

Linux

CUDA Runtime (cudart)

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

cuobjdump

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows

CUPTI

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuxxfilt (demangler)

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows

CUDA Demo Suite

12.4.99

x86_64

Linux, Windows

CUDA GDB

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, WSL

CUDA Nsight Eclipse Plugin

12.4.99

x86_64, POWER

Linux

CUDA NVCC

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvdisasm

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows

CUDA NVML Headers

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvprof

12.4.99

x86_64, POWER

Linux, Windows

CUDA nvprune

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NVRTC

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

NVTX

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NVVP

12.4.99

x86_64, POWER

Linux, Windows

CUDA OpenCL

12.4.99

x86_64

Linux, Windows

CUDA Profiler API

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA Compute Sanitizer API

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuBLAS

12.4.2.65

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

cuDLA

12.4.99

aarch64-jetson

Linux

CUDA cuFFT

11.2.0.44

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuFile

1.9.0.20

x86_64, arm64-sbsa, aarch64-jetson

Linux

CUDA cuRAND

10.3.5.119

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuSOLVER

11.6.0.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuSPARSE

12.3.0.142

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NPP

12.2.5.2

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvFatbin

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvJitLink

12.4.99

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvJPEG

12.3.1.89

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL

Nsight Compute

2024.1.0.13

x86_64, arm64-sbsa, POWER, aarch64-jetson

Linux, Windows, WSL (Windows 11)

Nsight Systems

2023.4.4.54

x86_64, arm64-sbsa, POWER

Linux, Windows, WSL

Nsight Visual Studio Edition (VSE)

2024.1.0.23349

x86_64 (Windows)

Windows

nvidia_fs1

2.19.6

x86_64, arm64-sbsa, aarch64-jetson

Linux

Visual Studio Integration

12.4.99

x86_64 (Windows)

Windows

NVIDIA Linux Driver

550.54.14

x86_64, arm64-sbsa, POWER

Linux

NVIDIA Windows Driver

551.61

x86_64 (Windows)

Windows, WSL
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 12.x

>=525.60.13

>=528.33

CUDA 11.8.x CUDA 11.7.x CUDA 11.6.x CUDA 11.5.x CUDA 11.4.x CUDA 11.3.x CUDA 11.2.x CUDA 11.1.x

>=450.80.02

>=452.39

CUDA 11.0 (11.0.3)

>=450.36.06**

>=451.22**

* 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 Tesla Recommended Drivers 450.80.02 (Linux) / 452.39 (Windows), 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.

Table 3 CUDA Toolkit and Corresponding Driver Versions

CUDA Toolkit

Toolkit Driver Version

Linux x86_64 Driver Version

Windows x86_64 Driver Version

CUDA 12.4 GA

>=550.54.14

>=551.61

CUDA 12.3 Update 1

>=545.23.08

>=546.12

CUDA 12.3 GA

>=545.23.06

>=545.84

CUDA 12.2 Update 2

>=535.104.05

>=537.13

CUDA 12.2 Update 1

>=535.86.09

>=536.67

CUDA 12.2 GA

>=535.54.03

>=536.25

CUDA 12.1 Update 1

>=530.30.02

>=531.14

CUDA 12.1 GA

>=530.30.02

>=531.14

CUDA 12.0 Update 1

>=525.85.12

>=528.33

CUDA 12.0 GA

>=525.60.13

>=527.41

CUDA 11.8 GA

>=520.61.05

>=520.06

CUDA 11.7 Update 1

>=515.48.07

>=516.31

CUDA 11.7 GA

>=515.43.04

>=516.01

CUDA 11.6 Update 2

>=510.47.03

>=511.65

CUDA 11.6 Update 1

>=510.47.03

>=511.65

CUDA 11.6 GA

>=510.39.01

>=511.23

CUDA 11.5 Update 2

>=495.29.05

>=496.13

CUDA 11.5 Update 1

>=495.29.05

>=496.13

CUDA 11.5 GA

>=495.29.05

>=496.04

CUDA 11.4 Update 4

>=470.82.01

>=472.50

CUDA 11.4 Update 3

>=470.82.01

>=472.50

CUDA 11.4 Update 2

>=470.57.02

>=471.41

CUDA 11.4 Update 1

>=470.57.02

>=471.41

CUDA 11.4.0 GA

>=470.42.01

>=471.11

CUDA 11.3.1 Update 1

>=465.19.01

>=465.89

CUDA 11.3.0 GA

>=465.19.01

>=465.89

CUDA 11.2.2 Update 2

>=460.32.03

>=461.33

CUDA 11.2.1 Update 1

>=460.32.03

>=461.09

CUDA 11.2.0 GA

>=460.27.03

>=460.82

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 https://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 https://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.

1.2. New Features

This section lists new general CUDA and CUDA compilers features.

1.2.1. General CUDA

  • Green contexts are a lightweight alternative to traditional contexts, with the ability to pass in a set of resources that they should be initialized with. This allows the developer to represent distinct spatial partitions of the GPU, provision resources for them, and target them via the same programming model that CUDA exposes (streams, kernel launches, etc.). For detail, refer to https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html.

  • Access-counter-based memory migration for Grace Hopper systems is now enabled by default. As this is the first release with the capability enabled, developers may find that applications that had been optimized for earlier memory migration algorithms may see a performance regression if optimized for the earlier behaviors. Should this occur, we introduce a supported but temporary flag to opt out of this behavior. You can control the enablement of this feature by unloading and reloading the NVIDIA UVM driver, as follows:

    # modprobe -r nvidia_uvm

# modprobe nvidia_uvm uvm_perf_access_counter_mimc_migration_enable=0

  • This release introduces support for the following new features in CUDA graphs:

    • Graph conditional nodes (enhanced from 12.3)

    • Device-side node parameter update for device graphs

    • Updatable graph node priorities without recompilation

  • Enhanced monitoring capabilities through NVML and nvidia-smi:

    • NVJPG and NVOFA utilization percentage

    • PCIe class and subclass reporting

    • dmon reports are now available in CSV format

    • More descriptive error codes returned from NVML

    • dmon now reports gpm-metrics for MIG (that is, nvidia-smi dmon --gpm-metrics runs in MIG mode)

    • NVML running against older drivers will report FUNCTION_NOT_FOUND in some cases, failing gracefully if NVML is newer than the driver

    • NVML APIs to query protected memory information for Hopper Confidential Computing

  • This release introduces nvFatbin, a new library to create CUDA fat binary files at runtime. For more details, please visit https://docs.nvidia.com/cuda/nvfatbin/index.html.

1.2.2. Confidential Computing General Access

  • Starting in 12.4 with R550.54.14, the Confidential Computing of Hopper will move to General Access for discrete GPU usage.

  • All EA RIM certificates prior to this release will be revoked with status PrivilegeWithdrawn 30 days after posting.

  • For more details, please visit https://docs.nvidia.com/confidential-computing.

1.2.3. CUDA Compilers

  • For changes to PTX, refer to https://docs.nvidia.com/cuda/parallel-thread-execution/#ptx-isa-version-8-4.

  • Added the __maxnreg__ kernel function qualifier to allow users to directly specify the maximum number of registers to be allocated to a single thread in a thread block in CUDA C++.

  • Added a new flag -fdevice-syntax-only that ends device compilation after front-end syntax checking. This option can provide rapid feedback (warnings and errors) of source code changes as it will not invoke the optimizer. Note: this option will not generate valid object code.

  • Add a new flag -minimal for NVRTC compilation. The -minimal flag omits certain language features to reduce compile time for small programs. In particular, the following are omitted:

    • Texture and surface functions and associated types (for example, cudaTextureObject_t).

    • CUDA Runtime Functions that are provided by the cudadevrt device code library, typically named with prefix “cuda”, for example, cudaMalloc.

    • Kernel launch from device code.

    • Types and macros associated with CUDA Runtime and Driver APIs, provided by cuda/tools/cudart/driver_types.h, typically named with the prefix “cuda” for example, cudaError_t.

  • Starting in CUDA 12.4, PTXAS enables position independent code (-pic) as default when the compilation mode is whole program compilation. Users can opt out by specifying the -pic=false option to PTXAS. Debug compilation and separate compilation continue to have position independent code disabled by default. In future, position independent code will allow the CUDA Driver to share a single copy of text section across contexts and reduce resident memory usage.

1.2.4. CUDA Developer Tools

  • For changes to nvprof and Visual Profiler, see the changelog.

  • For new features, improvements, and bug fixes in Nsight Systems, see the changelog.

  • For new features, improvements, and bug fixes in Nsight Visual Studio Edition, see the changelog.

  • 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.

  • For new features, improvements, and bug fixes in Compute Sanitizer, see the changelog.

  • For new features, improvements, and bug fixes in CUDA-GDB, see the changelog.

1.3. Resolved Issues

1.3.1. General CUDA

  • Fixed a compiler crash that could occur when inputs to MMA instructions were used before being initialized.

1.3.2. CUDA Compilers

  • In certain cases, dp4a or dp2a instructions would be generated in ptx and cause incorrect behavior due to integer overflow. This has been fixed in CUDA 12.4.

1.4. Deprecated or Dropped Features

Features deprecated in the current release of the CUDA software 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.

1.4.1. Deprecated Architectures

CUDA Toolkit 12.4 deprecates NVIDIA CUDA support for the PowerPC architecture. Support for this architecture is considered deprecated and will be removed in an upcoming release.

1.4.2. Deprecated Operating Systems

CUDA Toolkit 12.4 deprecates support for Red Hat Enterprise Linux 7 and CentOS 7. Support for these operating systems will be removed in an upcoming release.

1.4.3. Deprecated Toolchains

CUDA Toolkit 12.4 deprecates support for the following host compilers:

  • Microsoft Visual C/C++ (MSVC) 2017

  • All GCC versions prior to GCC 7.3

1.4.4. CUDA Tools

  • Support for the macOS host client of CUDA-GDB is deprecated. It will be dropped in an upcoming release.

2. CUDA Libraries

This section covers CUDA Libraries release notes for 12.x releases.

  • CUDA Math Libraries toolchain uses C++11 features, and a C++11-compatible standard library (libstdc++ >= 20150422) is required on the host.

  • Support for the following compute capabilities is removed for all libraries:

    • sm_35 (Kepler)

    • sm_37 (Kepler)

2.1. cuBLAS Library

2.1.1. cuBLAS: Release 12.4

  • New Features

    • cuBLAS adds experimental APIs to support grouped batched GEMM for single precision and double precision. Single precision also supports the math mode, CUBLAS_TF32_TENSOR_OP_MATH. Grouped batch mode allows you to concurrently solve GEMMs of different dimensions (m, n, k), leading dimensions (lda, ldb, ldc), transpositions (transa, transb), and scaling factors (alpha, beta). Please see cublas<t>gemmGroupedBatched <https://docs.nvidia.com/cuda/cublas/index.html#cublas-t-gemmgroupedbatched>__ for more details.

  • Known Issues

    • When the current context has been created using cuGreenCtxCreate(), cuBLAS does not properly detect the number of SMs available. The user may provide the corrected SM count to cuBLAS using an API such as cublasSetSmCountTarget().

    • BLAS level 2 and 3 functions might not treat alpha in a BLAS compliant manner when alpha is zero and the pointer mode is set to CUBLAS_POINTER_MODE_DEVICE. This is the same known issue documented in cuBLAS 12.3 Update 1.

    • cublasLtMatmul with K equals 1 and epilogue CUBLASLT_EPILOGUE_D{RELU,GELU}_BGRAD could out-of-bound access the workspace. The issue exists since cuBLAS 11.3 Update 1.

2.1.2. cuBLAS: Release 12.3 Update 1

  • New Features

    • Improved performance of heuristics cache for workloads that have a high eviction rate.

  • Known Issues

    • BLAS level 2 and 3 functions might not treat alpha in a BLAS compliant manner when alpha is zero and the pointer mode is set to CUBLAS_POINTER_MODE_DEVICE. The expected behavior is that the corresponding computations would be skipped. You may encounter the following issues: (1) HER{,2,X,K,2K} may zero the imaginary part on the diagonal elements of the output matrix; and (2) HER{,2,X,K,2K}, SYR{,2,X,K,2K} and others may produce NaN resulting from performing computation on matrices A and B which would otherwise be skipped. If strict compliance with BLAS is required, the user may manually check for alpha value before invoking the functions or switch to CUBLAS_POINTER_MODE_HOST.

  • Resolved Issues

    • cuBLASLt matmul operations might have computed the output incorrectly under the following conditions: the data type of matrices A and B is FP8, the data type of matrices C and D is FP32, FP16, or BF16, the beta value is 1.0, the C and D matrices are the same, the epilogue contains GELU activation function.

    • When an application compiled with cuBLASLt from CUDA Toolkit 12.2 update 1 or earlier runs with cuBLASLt from CUDA Toolkit 12.2 update 2 or CUDA Toolkit 12.3, matrix multiply descriptors initialized using cublasLtMatmulDescInit() sometimes did not respect attribute changes using cublasLtMatmulDescSetAttribute().

    • Fixed creation of cuBLAS or cuBLASLt handles on Hopper GPUs under the Multi-Process Service (MPS).

    • cublasLtMatmul with K equals 1 and epilogue CUBLASLT_EPILOGUE_BGRAD{A,B} might have returned incorrect results for the bias gradient.

2.1.3. cuBLAS: Release 12.3

  • New Features

    • Improved performance on NVIDIA L40S Ada GPUs.

  • Known Issues

    • cuBLASLt matmul operations may compute the output incorrectly under the following conditions: the data type of matrices A and B is FP8, the data type of matrices C and D is FP32, FP16, or BF16, the beta value is 1.0, the C and D matrices are the same, the epilogue contains GELU activation function.

    • When an application compiled with cuBLASLt from CUDA Toolkit 12.2 update 1 or earlier runs with cuBLASLt from CUDA Toolkit 12.2 update 2 or later, matrix multiply descriptors initialized using cublasLtMatmulDescInit() may not respect attribute changes using cublasLtMatmulDescSetAttribute(). To workaround this issue, create the matrix multiply descriptor using cublasLtMatmulDescCreate() instead of cublasLtMatmulDescInit(). This will be fixed in an upcoming release.

2.1.4. cuBLAS: Release 12.2 Update 2

  • New Features

    • cuBLASLt will now attempt to decompose problems that cannot be run by a single gemm kernel. It does this by partitioning the problem into smaller chunks and executing the gemm kernel multiple times. This improves functional coverage for very large m, n, or batch size cases and makes the transition from the cuBLAS API to the cuBLASLt API more reliable.

  • Known Issues

    • cuBLASLt matmul operations may compute the output incorrectly under the following conditions: the data type of matrices A and B is FP8, the data type of matrices C and D is FP32, FP16, or BF16, the beta value is 1.0, the C and D matrices are the same, the epilogue contains GELU activation function.

2.1.5. cuBLAS: Release 12.2

  • Known Issues

    • cuBLAS initialization fails on Hopper architecture GPUs when MPS is in use with CUDA_MPS_ACTIVE_THREAD_PERCENTAGE set to a value less than 100%. There is currently no workaround for this issue.

    • Some Hopper kernels produce incorrect results for batched matmuls with CUBLASLT_EPILOGUE_RELU_BIAS or CUBLASLT_EPILOGUE_GELU_BIAS and a non-zero CUBLASLT_MATMUL_DESC_BIAS_BATCH_STRIDE. The kernels apply the first batch’s bias vector to all batches. This will be fixed in a future release.

2.1.6. cuBLAS: Release 12.1 Update 1

  • New Features

    • Support for FP8 on NVIDIA Ada GPUs.

    • Improved performance on NVIDIA L4 Ada GPUs.

    • Introduced an API that instructs the cuBLASLt library to not use some CPU instructions. This is useful in some rare cases where certain CPU instructions used by cuBLASLt heuristics negatively impact CPU performance. Refer to https://docs.nvidia.com/cuda/cublas/index.html#disabling-cpu-instructions.

  • Known Issues

    • When creating a matrix layout using the cublasLtMatrixLayoutCreate() function, the object pointed at by cublasLtMatrixLayout_t is smaller than cublasLtMatrixLayoutOpaque_t (but enough to hold the internal structure). As a result, the object should not be dereferenced or copied explicitly, as this might lead to out of bound accesses. If one needs to serialize the layout or copy it, it is recommended to manually allocate an object of size sizeof(cublasLtMatrixLayoutOpaque_t) bytes, and initialize it using cublasLtMatrixLayoutInit() function. The same applies to cublasLtMatmulDesc_t and cublasLtMatrixTransformDesc_t. The issue will be fixed in future releases by ensuring that cublasLtMatrixLayoutCreate() allocates at least sizeof(cublasLtMatrixLayoutOpaque_t) bytes.

2.1.7. cuBLAS: Release 12.0 Update 1

  • New Features

    • Improved performance on NVIDIA H100 SXM and NVIDIA H100 PCIe GPUs.

  • Known Issues

    • For optimal performance on NVIDIA Hopper architecture, cuBLAS needs to allocate a bigger internal workspace (64 MiB) than on the previous architectures (8 MiB). In the current and previous releases, cuBLAS allocates 256 MiB. This will be addressed in a future release. A possible workaround is to set the CUBLAS_WORKSPACE_CONFIG environment variable to :32768:2 when running cuBLAS on NVIDIA Hopper architecture.

  • Resolved Issues

    • Reduced cuBLAS host-side overheads caused by not using the cublasLt heuristics cache. This began in the CUDA Toolkit 12.0 release.

    • Added forward compatible single precision complex GEMM that does not require workspace.

2.1.8. cuBLAS: Release 12.0

  • New Features

    • cublasLtMatmul now supports FP8 with a non-zero beta.

    • Added int64 APIs to enable larger problem sizes; refer to 64-bit integer interface.

    • Added more Hopper-specific kernels for cublasLtMatmul with epilogues:

      • CUBLASLT_EPILOGUE_BGRAD{A,B}

      • CUBLASLT_EPILOGUE_{RELU,GELU}_AUX

      • CUBLASLT_EPILOGUE_D{RELU,GELU}

    • Improved Hopper performance on arm64-sbsa by adding Hopper kernels that were previously supported only on the x86_64 architecture for Windows and Linux.

  • Known Issues

    • There are no forward compatible kernels for single precision complex gemms that do not require workspace. Support will be added in a later release.

  • Resolved Issues

    • Fixed an issue on NVIDIA Ampere architecture and newer GPUs where cublasLtMatmul with epilogue CUBLASLT_EPILOGUE_BGRAD{A,B} and a nontrivial reduction scheme (that is, not CUBLASLT_REDUCTION_SCHEME_NONE) could return incorrect results for the bias gradient.

    • cublasLtMatmul for gemv-like cases (that is, m or n equals 1) might ignore bias with the CUBLASLT_EPILOGUE_RELU_BIAS and CUBLASLT_EPILOGUE_BIAS epilogues.

    Deprecations

    • Disallow including cublas.h and cublas_v2.h in the same translation unit.

    • Removed:

      • CUBLAS_MATMUL_STAGES_16x80 and CUBLAS_MATMUL_STAGES_64x80 from cublasLtMatmulStages_t. No kernels utilize these stages anymore.

      • cublasLt3mMode_t, CUBLASLT_MATMUL_PREF_MATH_MODE_MASK, and CUBLASLT_MATMUL_PREF_GAUSSIAN_MODE_MASK from cublasLtMatmulPreferenceAttributes_t. Instead, use the corresponding flags from cublasLtNumericalImplFlags_t.

      • CUBLASLT_MATMUL_PREF_POINTER_MODE_MASK, CUBLASLT_MATMUL_PREF_EPILOGUE_MASK, and CUBLASLT_MATMUL_PREF_SM_COUNT_TARGET from cublasLtMatmulPreferenceAttributes_t. The corresponding parameters are taken directly from cublasLtMatmulDesc_t.

      • CUBLASLT_POINTER_MODE_MASK_NO_FILTERING from cublasLtPointerModeMask_t. This mask was only applicable to CUBLASLT_MATMUL_PREF_MATH_MODE_MASK which was removed.

2.2. cuFFT Library

2.2.1. cuFFT: Release 12.4

  • New Features

    • Added Just-In-Time Link-Time Optimized (JIT LTO) kernels for improved performance in FFTs with 64-bit indexing.

    • Added per-plan properties to the cuFFT API. These new routines can be leveraged to give users more control over the behavior of cuFFT. Currently they can be used to enable JIT LTO kernels for 64-bit FFTs.

    • Improved accuracy for certain single-precision (fp32) FFT cases, especially involving FFTs for larger sizes.

  • Known Issues

    • A routine from the cuFFT LTO EA library was added by mistake to the cuFFT Advanced API header (cufftXt.h). This routine is not supported by cuFFT, and will be removed from the header in a future release.

  • Resolved Issues

    • Fixed an issue that could cause overwriting of user data when performing out-of-place real-to-complex (R2C) transforms with user-specified output strides (i.e. using the ostride component of the Advanced Data Layout API).

    • Fixed inconsistent behavior between libcufftw and FFTW when both inembed and onembed are nullptr / NULL. From now on, as in FFTW, passing nullptr / NULL as inembed/onembed parameter is equivalent to passing n, that is, the logical size for that dimension.

2.2.2. cuFFT: Release 12.3 Update 1

  • Known Issues

    • Executing a real-to-complex (R2C) or complex-to-real (C2R) plan in a context different to the one used to create the plan could cause undefined behavior. This issue will be fixed in an upcoming release of cuFFT.

  • Resolved Issues

    • Complex-to-complex (C2C) execution functions (cufftExec and similar) now properly error-out in case of error during kernel launch, for example due to a missing CUDA context.

2.2.3. cuFFT: Release 12.3

  • New Features

    • Callback kernels are more relaxed in terms of resource usage, and will use fewer registers.

    • Improved accuracy for double precision prime and composite FFT sizes with factors larger than 127.

    • Slightly improved planning times for some FFT sizes.

2.2.4. cuFFT: Release 12.2

  • New Features

    • cufftSetStream can be used in multi-GPU plans with a stream from any GPU context, instead of from the primary context of the first GPU listed in cufftXtSetGPUs.

    • Improved performance of 1000+ of FFTs of sizes ranging from 62 to 16380. The improved performance spans hundreds of single precision and double precision cases for FFTs with contiguous data layout, across multiple GPU architectures (from Maxwell to Hopper GPUs) via PTX JIT.

    • Reduced the size of the static libraries when compared to cuFFT in the 12.1 release.

  • Resolved Issues

    • cuFFT no longer exhibits a race condition when threads simultaneously create and access plans with more than 1023 plans alive.

    • cuFFT no longer exhibits a race condition when multiple threads call cufftXtSetGPUs concurrently.

2.2.5. cuFFT: Release 12.1 Update 1

  • Known Issues

    • cuFFT exhibits a race condition when one thread calls cufftCreate (or cufftDestroy) and another thread calls any API (except cufftCreate or cufftDestroy), and when the total number of plans alive exceeds 1023.

    • cuFFT exhibits a race condition when multiple threads call cufftXtSetGPUs concurrently on different plans.

2.2.6. cuFFT: Release 12.1

  • New Features

    • Improved performance on Hopper GPUs for hundreds of FFTs of sizes ranging from 14 to 28800. The improved performance spans over 542 cases across single and double precision for FFTs with contiguous data layout.

  • Known Issues

    • Starting from CUDA 11.8, CUDA Graphs are no longer supported for callback routines that load data in out-of-place mode transforms. An upcoming release will update the cuFFT callback implementation, removing this limitation. cuFFT deprecated callback functionality based on separate compiled device code in cuFFT 11.4.

  • Resolved Issues

    • cuFFT no longer produces errors with compute-sanitizer at program exit if the CUDA context used at plan creation was destroyed prior to program exit.

2.2.7. cuFFT: Release 12.0 Update 1

  • Resolved Issues

    • Scratch space requirements for multi-GPU, single-batch, 1D FFTs were reduced.

2.2.8. cuFFT: Release 12.0

  • New Features

    • PTX JIT kernel compilation allowed the addition of many new accelerated cases for Maxwell, Pascal, Volta and Turing architectures.

  • Known Issues

  • Resolved Issues

    • cuFFT plans had an unintentional small memory overhead (of a few kB) per plan. This is resolved.

2.3. cuSOLVER Library

2.3.1. cuSOLVER: Release 12.4

  • New Features

    • cusolverDnXlarft and cusolverDnXlarft_bufferSize APIs were introduced. cusolverDnXlarft forms the triangular factor of a real block reflector, while cusolverDnXlarft_bufferSize returns its required workspace sizes in bytes.

  • Known Issues

    • cusolverDnXtrtri_bufferSize` returns an incorrect required device workspace size. As a workaround the returned size can be multiplied by the size of the data type (for example, 8 bytes if matrix A is of type double) to obtain the correct workspace size.

2.3.2. cuSOLVER: Release 12.2 Update 2

  • Resolved Issues

    • Fixed an issue with cusolverDn<t>gesvd(), cusolverDnGesvd(), and cusolverDnXgesvd(), which could cause wrong results for matrices larger than 18918 if jobu or jobvt was unequal to ‘N’.

2.3.3. cuSOLVER: Release 12.2

  • New Features

    • A new API to ensure deterministic results or allow non-deterministic results for improved performance. See cusolverDnSetDeterministicMode() and cusolverDnGetDeterministicMode(). Affected functions are: cusolverDn<t>geqrf(), cusolverDn<t>syevd(), cusolverDn<t>syevdx(), cusolverDn<t>gesvdj(), cusolverDnXgeqrf(), cusolverDnXsyevd(), cusolverDnXsyevdx(), cusolverDnXgesvdr(), and cusolverDnXgesvdp().

  • Known Issues

    • Concurrent executions of cusolverDn<t>getrf() or cusolverDnXgetrf() in different non-blocking CUDA streams on the same device might result in a deadlock.

2.4. cuSPARSE Library

2.4.1. cuSPARSE: Release 12.4

  • New Features

    • Added the preprocessing step for sparse matrix-vector multiplication cusparseSpMV_preprocess().

    • Added support for mixed real and complex types for cusparseSpMM().

    • Added a new API cusparseSpSM_updateMatrix() to update the sparse matrix between the analysis and solving phase of cusparseSpSM().

  • Known Issues

    • cusparseSpMV() introduces invalid memory accesses when the output vector is not aligned to 16 bytes.

  • Resolved Issues

    • cusparseSpVV() provided incorrect results when the sparse vector has many non-zeros.

2.4.2. cuSPARSE: Release 12.3 Update 1

  • New Features

    • Added support for block sizes of 64 and 128 in BSR SDDMM.

    • Added a preprocessing step for BSR SDDMM that helps improve performance of the main computing stage.

2.4.3. cuSPARSE: Release 12.3

  • New Features

    • The cusparseSpSV_bufferSize() and cusparseSpSV_analysis() routines now accept NULL pointers for the dense vector.

    • The cusparseSpSM_bufferSize() and cusparseSpSM_analysis() routines now accept dense matrix descriptors with NULL pointer for values.

  • Known Issues

    • The cusparseSpSV_analysis() and cusparseSpSM_analysis() routines are blocking calls/not asynchronous.

    • Wrong results can occur for cusparseSpSV() using sliced ELLPACK format and transpose/transpose conjugate operation on matrix A.

  • Resolved Issues

    • cusparseSpMV() now supports output vector with the minimum alignment.

    • cusparseSpSV() provided indeterministic results in some cases.

    • Fixed an issue that caused cusparseSpSV_analysis() to hang sometimes in a multi-thread environment.

    • Fixed an issue with cusparseSpSV() and cusparseSpSV() that somtimes yielded wrong output when the output vector/matrix or input matrix contained NaN.

2.4.4. cuSPARSE: Release 12.2 Update 1

  • New Features

  • Resolved Issues

    • Removed CUSPARSE_SPMM_CSR_ALG3 fallback to avoid confusion in the algorithm selection process.

    • Clarified the supported operations for cusparseSDDMM().

    • cusparseCreateConstSlicedEll() now uses const pointers.

    • Fixed wrong results in rare edge cases of cusparseCsr2CscEx2() with base 1 indexing.

    • cusparseSpSM_bufferSize() could ask slightly less memory than needed.

    • cusparseSpMV() now checks the validity of the buffer pointer only when it is strictly needed.

  • Deprecations

    • Several legacy APIs have been officially deprecated. A compile-time warning has been added to all of them.

2.4.5. cuSPARSE: Release 12.1 Update 1

  • New Features

    • Introduced Block Sparse Row (BSR) sparse matrix storage for the Generic APIs with support for SDDMM routine (cusparseSDDMM).

    • Introduced Sliced Ellpack (SELL) sparse matrix storage format for the Generic APIs with support for sparse matrix-vector multiplication (cusparseSpMV) and triangular solver with a single right-hand side (cusparseSpSV).

    • Added a new API call (cusparseSpSV_updateMatrix) to update matrix values and/or the matrix diagonal in the sparse triangular solver with a single right-hand side after the analysis step.

2.4.6. cuSPARSE: Release 12.0 Update 1

  • New Features

    • cusparseSDDMM() now supports mixed precision computation.

    • Improved cusparseSpMM() alg2 mixed-precision performance on some matrices on NVIDIA Ampere architecture GPUs.

    • Improved cusparseSpMV() performance with a new load balancing algorithm.

    • cusparseSpSV() and cusparseSpSM() now support in-place computation, namely the output and input vectors/matrices have the same memory address.

  • Resolved Issues

    • cusparseSpSM() could produce wrong results if the leading dimension (ld) of the RHS matrix is greater than the number of columns/rows.

2.4.7. cuSPARSE: Release 12.0

  • New Features

    • JIT LTO functionalities (cusparseSpMMOp()) switched from driver to nvJitLto library. Starting from CUDA 12.0 the user needs to link to libnvJitLto.so, see cuSPARSE documentation. JIT LTO performance has also been improved for cusparseSpMMOpPlan().

    • Introduced const descriptors for the Generic APIs, for example, cusparseConstSpVecGet(). Now the Generic APIs interface clearly declares when a descriptor and its data are modified by the cuSPARSE functions.

    • Added two new algorithms to cusparseSpGEMM() with lower memory utilization. The first algorithm computes a strict bound on the number of intermediate product, while the second one allows partitioning the computation in chunks.

    • Added int8_t support to cusparseGather(), cusparseScatter(), and cusparseCsr2cscEx2().

    • Improved cusparseSpSV() performance for both the analysis and the solving phases.

    • Improved cusparseSpSM() performance for both the analysis and the solving phases.

    • Improved cusparseSDDMM() performance and added support for batch computation.

    • Improved cusparseCsr2cscEx2() performance.

  • Resolved Issues

    • cusparseSpSV() and cusparseSpSM() could produce wrong results.

    • cusparseDnMatGetStridedBatch() did not accept batchStride == 0.

  • Deprecations

    • Removed deprecated CUDA 11.x APIs, enumerators, and descriptors.

2.5. Math Library

2.5.1. CUDA Math: Release 12.4

  • Resolved Issues

    • Host-specific code in cuda_fp16/bf16 headers is now free from type-punning and shall work correctly in the presence of optimizations based on strict-aliasing rules.

2.5.2. CUDA Math: Release 12.3

  • New Features

    • Performance of SIMD Integer CUDA Math APIs was improved.

  • Resolved Issues

    • The __hisinf() Math APIs from cuda_fp16.h and cuda_bf16.h headers were silently producing wrong results if compiled with the -std=c++20 compiler option because of an underlying nvcc compiler issue, resolved in version 12.3.

  • Known Issues

    • Users of cuda_fp16.h and cuda_bf16.h headers are advised to disable host compilers strict aliasing rules based optimizations (e.g. pass -fno-strict-aliasing to host GCC compiler) as these may interfere with the type-punning idioms used in the __half, __half2, __nv_bfloat16, __nv_bfloat162 types implementations and expose the user program to undefined behavior. Note, the headers suppress GCC diagnostics through: #pragma GCC diagnostic ignored -Wstrict-aliasing. This behavior may improve in future versions of the headers.

2.5.3. CUDA Math: Release 12.2

  • New Features

    • CUDA Math APIs for __half and __nv_bfloat16 types received usability improvements, including host side <emulated> support for many of the arithmetic operations and conversions.

    • __half and __nv_bfloat16 types have implicit conversions to/from integral types, which are now available with host compilers by default. These may cause build issues due to ambiguous overloads resolution. Users are advised to update their code to select proper overloads. To opt-out user may want to define the following macros (these macros will be removed in the future CUDA release):

      • __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__

      • __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__

  • Resolved Issues

    • During ongoing testing, NVIDIA identified that due to an algorithm error the results of 64-bit floating-point division in default round-to-nearest-even mode could produce spurious overflow to infinity. NVIDIA recommends that all developers requiring strict IEEE754 compliance update to CUDA Toolkit 12.2 or newer. The affected algorithm was present in both offline compilation as well as just-in-time (JIT) compilation. As JIT compilation is handled by the driver, NVIDIA recommends updating to driver version greater than or equal to R535 (R536 on Windows) when IEEE754 compliance is required and when using JIT. This is a software algorithm fix and is not tied to specific hardware.

    • Updated the observed worst case error bounds for single precision intrinsic functions __expf(), __exp10f() and double precision functions asinh(), acosh().

2.5.4. CUDA Math: Release 12.1

  • New Features

    • Performance and accuracy improvements in atanf, acosf, asinf, sinpif, cospif, powf, erff, and tgammaf.

2.5.5. CUDA Math: Release 12.0

  • New Features

  • Known Issues

    • Double precision inputs that cause the double precision division algorithm in the default ‘round to nearest even mode’ produce spurious overflow: an infinite result is delivered where DBL_MAX 0x7FEF_FFFF_FFFF_FFFF is expected. Affected CUDA Math APIs: __ddiv_rn(). Affected CUDA language operation: double precision / operation in the device code.

  • Deprecations

    • All previously deprecated undocumented APIs are removed from CUDA 12.0.

2.6. NVIDIA Performance Primitives (NPP)

2.6.1. NPP: Release 12.4

  • New Features

    • Enhanced large file support with size_t.

2.6.2. NPP: Release 12.0

  • Deprecations

    • Deprecating non-CTX API support from next release.

  • Resolved Issues

    • A performance issue with the NPP ResizeSqrPixel API is now fixed and shows improved performance.

2.7. nvJPEG Library

2.7.1. nvJPEG: Release 12.4

  • New Features

    • IDCT performance optimizations for single image CUDA decode.

    • Zero Copy behavior has been changed: Setting NVJPEG_FLAGS_REDUCED_MEMORY_DECODE_ZERO_COPY flag will no longer enable NVJPEG_FLAGS_REDUCED_MEMORY_DECODE.

2.7.2. nvJPEG: Release 12.3 Update 1

  • New Features

    • New APIs: nvjpegBufferPinnedResize and nvjpegBufferDeviceResize which can be used to resize pinned and device buffers before using them.

2.7.3. nvJPEG: Release 12.2

  • New Features

    • Added support for JPEG Lossless decode (process 14, FO prediction).

    • nvJPEG is now supported on L4T.

2.7.4. nvJPEG: Release 12.0

  • New Features

    • Immproved the GPU Memory optimisation for the nvJPEG codec.

  • Resolved Issues

    • An issue that causes runtime failures when nvJPEGDecMultipleInstances was tested with a large number of threads is resolved.

    • An issue with CMYK four component color conversion is now resolved.

  • Known Issues

    • Backend NVJPEG_BACKEND_GPU_HYBRID - Unable to handle bistreams with extra scans lengths.

  • Deprecations

    • The reuse of Huffman table in Encoder (nvjpegEncoderParamsCopyHuffmanTables).

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