NVIDIA CUDA Toolkit Release Notes

The Release Notes for the CUDA Toolkit.

1. CUDA 12.2 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 12.2 Update 2, the table below indicates the versions:

CUDA 12.2 Update 2 Component Versions

Component Name

Version Information

Supported Architectures

Supported Platforms

CUDA C++ Core Compute Libraries

Thrust

2.1.0

x86_64, POWER, aarch64-jetson

Linux, Windows

CUB

2.1.0

libcu++

2.1.0

Cooperative Groups

12.0.0

CUDA Compatibility

12.2.34086590

x86_64, POWER, aarch64-jetson

Linux, Windows

CUDA Runtime (cudart)

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

cuobjdump

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows

CUPTI

12.2.142

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuxxfilt (demangler)

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows

CUDA Demo Suite

12.2.140

x86_64

Linux, Windows

CUDA GDB

12.2.140

x86_64, POWER, aarch64-jetson

Linux, WSL

CUDA Nsight Eclipse Plugin

12.2.144

x86_64, POWER

Linux

CUDA NVCC

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvdisasm

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows

CUDA NVML Headers

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvprof

12.2.142

x86_64, POWER

Linux, Windows

CUDA nvprune

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NVRTC

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

NVTX

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NVVP

12.2.142

x86_64, POWER

Linux, Windows

CUDA OpenCL

12.2.140

x86_64

Linux, Windows

CUDA Profiler API

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA Compute Sanitizer API

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuBLAS

12.2.5.6

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuDLA

12.2.140

aarch64-jetson

Linux

CUDA cuFFT

11.0.8.103

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuFile

1.7.2.10

x86_64

Linux

CUDA cuRAND

10.3.3.141

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuSOLVER

11.5.2.141

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA cuSPARSE

12.1.2.141

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA NPP

12.2.1.4

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvJitLink

12.2.140

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

CUDA nvJPEG

12.2.2.4

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

Nsight Compute

2023.2.2.3

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL (Windows 11)

Nsight Systems

2023.2.3.1004

x86_64, POWER, aarch64-jetson

Linux, Windows, WSL

Nsight Visual Studio Edition (VSE)

2023.2.2.23221

x86_64 (Windows)

Windows

nvidia_fs1

2.17.5

x86_64, aarch64-jetson

Linux

Visual Studio Integration

12.2.140

x86_64 (Windows)

Windows

NVIDIA Linux Driver

535.104.05

x86_64, POWER, aarch64-jetson

Linux

NVIDIA Windows Driver

537.13

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

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 12.2.x

>=525.60.13

>=525.41

CUDA 12.1.x

>=525.60.13

>=527.41

CUDA 12.0.x

>=525.60.13

>=527.41

CUDA 11.8.x

>=450.80.02

>=452.39

CUDA 11.7.x

>=450.80.02

>=452.39

CUDA 11.6.x

>=450.80.02

>=452.39

CUDA 11.5.x

>=450.80.02

>=452.39

CUDA 11.4.x

>=450.80.02

>=452.39

CUDA 11.3.x

>=450.80.02

>=452.39

CUDA 11.2.x

>=450.80.02

>=452.39

CUDA 11.1 (11.1.0)

>=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.

CUDA Toolkit and Corresponding Driver Versions

CUDA Toolkit

Toolkit Driver Version

Linux x86_64 Driver Version

Windows x86_64 Driver Version

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

  • This release introduces Heterogeneous Memory Management (HMM), allowing seamless sharing of data between host memory and accelerator devices. HMM is supported on Linux only and requires a recent kernel (6.1.24+ or 6.2.11+).

    HMM requires the use of NVIDIA’s GPU Open Kernel Modules driver.

    As this is the first release of HMM, some limitations exist:

    • GPU atomic operations on file-backed memory are not yet supported.

    • Arm CPUs are not yet supported.

    • HugeTLBfs pages are not yet supported on HMM (this is an uncommon scenario).

    • The fork() system call is not fully supported yet when attempting to share GPU-accessible memory between parent and child processes.

    • HMM is not yet fully optimized, and may perform slower than programs using cudaMalloc(), cudaMallocManaged(), or other existing CUDA memory management APIs. The performance of programs not using HMM will not be affected.

  • The Lazy Loading feature (introduced in CUDA 11.7) is now enabled by default on Linux with the 535 driver. To disable this feature on Linux, set the environment variable CUDA_MODULE_LOADING=EAGER before launch. Default enablement for Windows will happen in a future CUDA driver release. To enable this feature on Windows, set the environment variable CUDA_MODULE_LOADING=LAZY before launch.

  • Host NUMA memory allocation: Allocate a CPU memory targeting a specific NUMA node using either the CUDA virtual memory management APIs or the CUDA stream-ordered memory allocator. Applications must ensure device accesses to pointer backed by HOST allocations from these APIs are performed only after they have explicitly requested accessibility for the memory on the accessing device. It is undefined behavior to access these host allocations from a device without accessibility for the address range, regardless of whether the device supports pageable memory access or not.

  • Added per-client priority mapping at runtime for CUDA Multi-Process Service (MPS). This allows multiple processes running under MPS to arbitrate priority at a coarse-grained level between multiple processes without changing the application code.

    We introduce a new environment variable CUDA_MPS_CLIENT_PRIORITY, which accepts two values: NORMAL priority, 0, and BELOW_NORMAL priority, 1.

    For example, given two clients, a potential configuration is as follows:

    // Client 1’s Environment

    export CUDA_MPS_CLIENT_PRIORITY=0

    // NORMAL

    // Client 2’s Environment

    export CUDA_MPS_CLIENT_PRIORITY=1

    // BELOW NORMAL

1.2.2. CUDA Compilers

  • libNVVM samples have been moved out of the toolkit and made publicly available on GitHub as part of the NVIDIA/cuda-samples project. Similarly, the nvvmir-samples have been moved from the nvidia-compiler-sdk project on GitHub to the new location of the libNVVM samples in the NVIDIA/cuda-samples project.

  • For changes to PTX, refer to https://docs.nvidia.com/cuda/parallel-thread-execution/#ptx-isa-version-8-2.

1.2.3. CUDA Developer Tools

  • For changes to nvprof and Visual Profiler, 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

  • Resolved potential soft lock-ups around rm_run_nano_timer_callback(). A Linux kernel device driver API used for timer management in the Linux kernel interface of the NVIDIA GPU driver was susceptible to a race condition under multi-GPU configurations.

  • Fixed potential GSP-RM hang in kernel_resolve_address().

  • Removed potential GPUDirect RDMA driver crash in nvidia_p2p_put_pages(). The legacy non-persistent memory APIs allow third party driver to invoke nvidia_p2p_put_pages with a stale page_table pointer, which has already been freed by the RM callback as part of the process shutdown sequence. This behavior was broken when persistent memory support was added to the legacy nvidia_p2p APIs. We resolved the issue by providing new APIs: nvidia_p2p_get/put_pages_persistent for persistent memory. Thus, the original behavior of the legacy APIs for non-persistent memory is restored. This is essentially a change in the API, so although the nvidia-peermem is updated accordingly, external consumers of persistent memory mapping will need to be changed to use the new dedicated APIs.

  • Resolved an issue in watchcat syscall.

  • Fixed potential incorrect results in optimized code under high register pressure. NVIDIA has found that under certain rare conditions, a register spilling optimization in PTXAS could result in incorrect compilation results. This issue is fixed for offline compilation (non-JIT) in the CUDA 12.2 release and will be fixed for JIT compilation in the next enterprise driver update.

    NVIDIA believes this issue to be extremely rare, and applications relying on JIT that are working successfully should not be affected.

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.

General CUDA

  • Ubuntu 18.04 support has reached EOL.

CUDA Tools

  • None.

CUDA Compiler

  • None.

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 12.2 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.

2.1.2. 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.

2.1.3. 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.

2.1.4. 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.

2.1.5. 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 12.2

  • 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.

2.2.2. 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.

2.2.3. 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.

2.2.4. cuFFT: Release 12.0 Update 1

  • Resolved Issues

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

2.2.5. 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 12.2 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’.

2.3.2. 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 12.2 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.

2.4.2. 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.

2.4.3. 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.

2.4.4. 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 12.2

  • 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().

2.5.2. CUDA Math: Release 12.1

  • New Features

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

2.5.3. 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 12.0

  • 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 12.2

  • New Features

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

    • nvJPEG is now supported on L4T.

2.7.2. 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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