CUDA Toolkit 13.1 - Release Notes — Release Notes 13.1 documentation (original) (raw)

CUDA Toolkit 13.1 - Release Notes

1. Overview

Welcome to the release notes for NVIDIA® CUDA® Toolkit 13.1. This release includes enhancements and fixes across the CUDA Toolkit and its libraries.

This documentation is organized into two main sections:

• General CUDA
  Focuses on the core CUDA infrastructure including component versions, driver compatibility, compiler/runtime features, issues, and deprecations.
• CUDA Libraries
  Covers the specialized computational libraries with their feature updates, performance improvements, API changes, and version history across CUDA 13.x releases.

2. General CUDA

2.1. CUDA Toolkit Major Components

Note

Starting with CUDA 11, individual components within the CUDA Toolkit (for example: compiler, libraries, tools) are versioned independently.

For CUDA 13.1 , the table below indicates the versions:

Table 1 CUDA 13.1 Component Versions

2.2. 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

* 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.

** Starting with CUDA 13.1, the Windows display driver is no longer bundled with the CUDA Toolkit package. Users must download and install the appropriate NVIDIA driver separately from the official driver download page.

For more information on supported driver versions, see the CUDA Compatibility Guide for drivers.

*** 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.

1

Only available on select Linux distros

Table 3 CUDA Toolkit and Corresponding Driver Versions

• CUDA Toolkit driver bundling (pre-CUDA 13.1):
  • The CUDA Toolkit previously included an NVIDIA display driver for convenience.
  • This bundled driver was intended only for development purposes.
  • It is not recommended for production use, especially with Tesla GPUs.
• Recommended driver for Tesla GPUs:
  • For production environments using Tesla GPUs, download the latest certified driver from the official NVIDIA Driver Downloads site:
    https://www.nvidia.com/drivers
• Optional driver installation during Toolkit setup:
  • During CUDA Toolkit installation, users may choose to skip driver installation:
    * On Windows: via interactive or silent install options.
    * On Linux: by skipping driver meta packages.
• Change in CUDA 13.1 (Windows-specific):
  • Starting with CUDA 13.1, the Windows display driver is no longer bundled with the CUDA Toolkit.
  • Windows users must manually download and install the appropriate driver from the official NVIDIA site.
• Driver compatibility notes:
  • Some compatibility tables may list “N/A” for Windows driver versions.
  • Users must still ensure the installed driver meets or exceeds the minimum required version for the CUDA Toolkit.
  • For details, refer to the official CUDA Compatibility Guide for Drivers:

2.3. New Features

2.3.1. General CUDA

• Introducing CUDA Tile (CUDA Tile IR and cuTile)
  • CUDA 13.1 introduces CUDA Tile, which comprises CUDA Tile IR, a new virtual instruction set for NVIDIA GPUs that enables high-performance, tile-based code generation at a higher level of abstraction.
  • Building on this, cuTile is a new Python domain-specific language (DSL) that allows developers to author these high-performance, tile-based GPU kernels.
  • CUDA Tile includes updates across the compiler pipeline, FATBIN format, and driver.
  • The initial release targets Blackwell GPUs, with broader architecture support planned across the CUDA 13.x series.
    For a high-level overview, see the CUDA Tile webpage. For IR and compiler details, see the CUDA Tile IR documentation, and for the Python front end, see the cuTile Python documentation.

2.3.2. CUDA Compiler

• For changes to PTX, see the PTX ISA version 9.1 documentation.
• CUDA 13.1 introduces tileiras, a compiler that translates Tile IR bytecode into GPU machine instructions (SASS). For more information, see the Tile IR specification and changelog.

2.3.3. CUDA Documentation

• Introduced the new CUDA Programming Guide, the official, comprehensive resource on the CUDA programming model. The guide has been restructured into five parts that cover a language agnostic overview of CUDA, introductory and advanced CUDA programming in C++ and Python, detailed descriptions of specific CUDA features, and technical appendices for reference. The legacy CUDA C++ Programming Guide remains available in this release but is deprecated and includes notices that direct readers to the new guide.

2.3.4. CUDA Developer Tools

For details on new features, improvements, and bug fixes, see the changelogs for:

• Nsight Systems.
• Nsight Visual Studio Edition.
• CUPTI.
• Nsight Compute.
• Compute Sanitizer.
• CUDA-C++-Programming-Guide.

2.4. Known Issues

2.4.1. General CUDA

• Certain Linux kernels with KASLR enabled have a known issue in HMM initialization, causing CUDA initialization to fail. This issue is indicated by the following debug message:

[64689.125237] nvidia-uvm: uvm_pmm_gpu.c:3176 devmem_alloc_pagemap[pid:92821] request_free_mem_region() err -34

Fixes to this issue are being handled in upstream kernels. In the meantime, you can use one of the following workarounds:

• Option 1: Disable KASLR (Preferred option)
  If using GRUB, edit /etc/default/grub and add nokaslr to GRUB_CMDLINE_LINUX_DEFAULT:

  GRUB_CMDLINE_LINUX_DEFAULT="quiet splash nokaslr"
  Then, update GRUB and reboot:
  sudo update-grub
  sudo reboot

• Option 2: Disable HMM for UVM
  1. Create or edit /etc/modprobe.d/uvm.conf.
  2. Add or update the following line:
     options nvidia_uvm uvm_disable_hmm=1
  3. Unload and reload the nvidia_uvm kernel module or reboot the system:

     sudo modprobe -r nvidia_uvm
     sudo modprobe nvidia_uvm

2.4.2. CUDA Compiler

• The Tile-IR AS compiler currently supports only Blackwell-class devices and has limited low-precision support. These limitations will be removed in a future release.

2.5. Deprecated or Dropped Features

2.5.1. General CUDA

• CUDA 13.0 deprecates the following legacy vector types:
  • double4
  • long4
  • ulong4
  • longlong4
  • ulonglong4
    These types are being replaced by new aligned variants:
  • *_16a and *_32a (e.g., double4_16a, double4_32a)
    Deprecation warnings can be managed as follows:
  • Globally silenced by defining __NV_NO_VECTOR_DEPRECATION_DIAG
  • Locally suppressed using the macro pair __NV_SILENCE_HOST_DEPRECATION_BEGIN / __NV_SILENCE_HOST_DEPRECATION_END
    These legacy types are planned for removal in CUDA 14.0.
• The CUDA installer for Windows no longer bundles the display driver. Users must install the display driver separately, either before or after installing the CUDA Toolkit.
• Multi-device launch APIs and related references for Cooperative Groups have been removed. These APIs were previously marked as deprecated in CUDA 12.x:
  • cudaLaunchCooperativeKernelMultiDevice has been removed from cuda_runtime_api.h.
  • The accompanying parameter struct cudaLaunchParam has been removed from driver_types.h.
  • this_multi_grid and multi_grid_group have been removed from cooperative_groups.h.
• Changes to cudaDeviceProperties structure:
  In CUDA 13.0, several deprecated fields have been removed from the cudaDeviceProperties structure. To ensure forward compatibility, use the recommended replacement APIs listed below:
  Removed Fields and Their Replacements

  Removed Field	Replacement API
  clockRate	cudaDeviceGetAttribute(cudaDevAttrClockRate)
  deviceOverlap	Use the asyncEngineCount field
  kernelExecTimeoutEnabled	cudaDeviceGetAttribute(cudaDevAttrKernelExecTimeout)
  computeMode	cudaDeviceGetAttribute(cudaDevAttrComputeMode)
  maxTexture1DLinear	cudaDeviceGetTexture1DLinearMaxWidth()
  memoryClockRate	cudaDeviceGetAttribute(cudaDevAttrMemoryClockRate)
  singleToDoublePrecisionPerfRatio	cudaDeviceGetAttribute(cudaDevAttrSingleToDoublePrecisionPerfRatio)
  cooperativeMultiDeviceLaunch	No replacement available
  Removed cudaDeviceAttr Types (No Replacement Available)

  • cudaDevAttrCooperativeMultiDeviceLaunch
  • cudaDevAttrMaxTimelineSemaphoreInteropSupported
• The following legacy header files related to deprecated texture and surface references have been removed from the CUDA 13.0 runtime:
  • cuda_surface_types.h
  • cuda_texture_types.h
  • surface_functions.h
  • texture_fetch_functions.h

2.5.2. Deprecated Architectures

• Architecture support for Maxwell, Pascal, and Volta is considered feature-complete. Offline compilation and library support for these architectures have been removed in CUDA Toolkit 13.0 major version release. The use of CUDA Toolkits through the 12.x series to build applications for these architectures will continue to be supported, but newer toolkits will be unable to target these architectures.

2.5.3. Deprecated or Dropped Operating Systems

• Support for Ubuntu 20.04 has been dropped starting with this release. Users are advised to migrate to Ubuntu 22.04 LTS or later.

2.5.4. Deprecated or Dropped CUDA Toolchains

CUDA Tools

• As of CUDA 13.1, support for Nsight Eclipse Edition plugins is deprecated, and will be dropped in a future CUDA release.

3. CUDA Libraries

This section covers CUDA Libraries release notes for 13.x releases.

Note

Documentation will be updated to accurately reflect supported C++ standard libraries for CUDA Math Libraries.

3.1. cuBLAS Library

3.1.1. cuBLAS: Release 13.1

• New Features
  • Introduced experimental support for grouped GEMM in cuBLASLt. Users can create a matrix with grouped layout using cublasLtGroupedMatrixLayoutCreate or cublasLtGroupedMatrixLayoutInit, where matrix shapes are passed as device arrays. cublasLtMatmul now accepts matrices with grouped layout, in which case matrices are passed as a device array of pointers, where each pointer is a separate matrix that represents a group with its own shapes. Initial support covers A/B types FP8 (E4M3/E5M2), FP16, and BF16, with C/D types FP16, BF16, and FP32; column-major only, default epilogue, 16-byte alignment; requires GPUs with compute capability 10.x or 11.0.
    In addition, the following experimental features were added as part of grouped GEMM:
    * Per-batch tensor-wide scaling for FP8 inputs, enabled by the new cublasLtMatmulDescAttributes_t entry CUBLASLT_MATMUL_MATRIX_SCALE_PER_BATCH_SCALAR_32F.
    * Per-batch device-side alpha and beta, enabled by the new cublasLtMatmulDescAttributes_t entries CUBLASLT_MATMUL_DESC_ALPHA_BATCH_STRIDE and CUBLASLT_MATMUL_DESC_BETA_BATCH_STRIDE.
  • Improved performance on NVIDIA DGX Spark for CFP32 GEMMs. [_5514146_]
  • Added SM121 DriveOS support.
  • Improved performance on Blackwell (sm_100 and sm_103) via heuristics tuning for FP32 GEMMs whose shapes satisfy M, N >> K. [_CUB-8572_]
  • Improved performance of FP16, FP32, and CFP32 GEMMs on Blackwell Thor.
• Resolved Issues
  • Fixed missing memory initialization in cublasCreate() that could result in emulation environment variables being ignored. [_CUB-9302_]
  • Removed unnecessary overhead related to loading kernels on GPUs with compute capability 10.3. [_5547886_]
  • Fixed FP8 matmuls potentially failing to launch on multi-device Blackwell GeForce systems. [_CUB-9487_]
  • Added stricter checks for in-place matmul to prevent invalid use cases (C == D is allowed if and only if Cdesc == Ddesc). As a side effect, users are no longer able to use D as a dummy pointer for C when usingCUBLASLT_POINTER_MODE_DEVICE with beta = 0. However, a distinct dummy pointer may still be passed. The stricter checking was added in CUDA Toolkit 13.0 Update 2. [_5471880_]
  • Fixed cublasLtMatmul with INT8 inputs, INT32 accumulation, and INT32 outputs potentially returning CUBLAS_STATUS_NOT_SUPPORTED when dimension N is larger than 65,536 or when batch count is larger than 1. [_5541380_]

3.1.2. cuBLAS: Release 13.0 Update 2

• New Features
  • Enabled opt-in fixed-point emulation for FP64 matmuls (D/ZGEMM) which improves performance and power-efficiency. The implementation follows the Ozaki-1 Scheme and leverages an automatic dynamic precision framework to ensure FP64-level accuracy. See here for more details on fixed-point emulation along with the table of supported compute-capabilities and the CUDA library samples for example usages.
  • Improved performance on NVIDIA DGX Spark for FP16/BF16 and FP8 GEMMs.
  • Added support for BF16x9 FP32 emulation to cublas[SC]syr[2]k and cublasCher[2]k routines. With the math mode set to CUBLAS_FP32_EMULATED_BF16X9_MATH, for large enough problems, cuBLAS will automatically dispatch SYRK and HERK to BF16x9-accelerated algorithms.
• Resolved Issues
  • Fixed undefined behavior caused by dereferencing a nullptr when passing an uninitialized matrix layout descriptor for Cdesc in cublasLtMatmul. [_CUB-8911_]
  • Improved performance of cublas[SCDZ]syr[2]k and cublas[CZ]her[2]k on Hopper GPUs when dimension N is large. [CUB-8293, _5384826_]
• Known Issues
  • cublasLtMatmul with INT8 inputs, INT32 accumulation, and INT32 outputs might return CUBLAS_STATUS_NOT_SUPPORTED when dimension N is larger than 65,536 or when the batch count is larger than 1. The issue has existed since CUDA Toolkit 13.0 Update 1 and will be fixed in a later release. [_5541380_]

3.1.3. cuBLAS: Release 13.0 Update 1

• New Features
  • Improved performance:
    * Block-scaled FP4 GEMMs on NVIDIA Blackwell and Blackwell Ultra GPUs
    * SYMV on NVIDIA Blackwell GPUs [_5171345_]
    * cublasLtMatmul for small cases when run concurrently with other CUDA kernels [_5238629_]
    * TF32 GEMMs on Thor GPUs [_5313616_]
    * Programmatic Dependent Launch (PDL)is now supported in some cuBLAS kernels for architectures sm_90 and above, decreasing kernel launch latencies when executed alongside other PDL kernels.
• Resolved Issues
  • Fixed an issue where some cublasSsyrkx kernels produced incorrect results when beta = 0 on NVIDIA Blackwell GPUs. [_CUB-8846_]
  • Resolved issues in cublasLtMatmul with INT8 inputs, INT32 accumulation, and INT32 outputs where:
    * cublasLtMatmul could have produced incorrect results when A and B matrices used regular ordering (CUBLASLT_ORDER_COL or CUBLASLT_ORDER_ROW). [_CUB-8874_]
    * cublasLtMatmul could have been run with unsupported configurations of alpha/ beta, which must be 0 or 1. [_CUB-8873_]

3.1.4. cuBLAS: Release 13.0

• New Features
  • The cublasGemmEx, cublasGemmBatchedEx, and cublasGemmStridedBatchedEx functions now accept CUBLAS_GEMM_AUTOTUNE as a valid value for the algo parameter. When this option is used, the library benchmarks a selection of available algorithms internally and chooses the optimal one based on the given problem configuration. The selected algorithm is cached within the current cublasHandle_t, so subsequent calls with the same problem descriptor will reuse the cached configuration for improved performance.
    This is an experimental feature. Users are encouraged to transition to the cuBLASLt API, which provides fine-grained control over algorithm selection through the heuristics API and includes support for additional data types such as FP8 and block-scaled formats, as well as kernel fusion. (see autotuning example in cuBLASLt).
  • Improved performance of BLAS Level 3 non-GEMM kernels (SYRK, HERK, TRMM, SYMM, HEMM) for FP32 and CF32 precisions on NVIDIA Blackwell GPUs.
  • This release adds support of SM110 GPUs for arm64-sbsa on Linux.
• Known Issues
  • cublasLtMatmul previously ignored user-specified auxiliary (Aux) data types for ReLU epilogues and defaulted to using a bitmask. The correct behavior is now enforced: an error is returned if an invalid Aux data type is specified for ReLU epilogues. [CUB-7984]
• Deprecations
  • The experimental feature for atomic synchronization along the rows (CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_ROWS) and columns (CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_COLS) of the output matrix which was deprecated in 12.8 has now been removed.
  • Starting with this release, cuBLAS will return CUBLAS_STATUS_NOT_SUPPORTEDif any of the following descriptor attributes are set but the corresponding scale is not supported:
    * CUBLASLT_MATMUL_DESC_A_SCALE_POINTER
    * CUBLASLT_MATMUL_DESC_B_SCALE_POINTER
    * CUBLASLT_MATMUL_DESC_D_SCALE_POINTER
    * CUBLASLT_MATMUL_DESC_D_OUT_SCALE_POINTER
    * CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_SCALE_POINTER
  • Previously, this restriction applied only to non-narrow precision matmuls. It now also applies to narrow precision matmuls when a scale is set for a non-narrow precision tensor.

3.2. cuFFT Library

3.2.1. cuFFT: Release 13.1

• New Features
  • Improved performance for transforms whose sizes are powers of 2, 3, 5, and 7 on Blackwell GPUs, in both single and double precision.
  • Improved performance for selected power-of-two sizes in 2D and 3D transforms, in both single and double precision.
  • Introduced an experimental cuFFT device API that provides host functions to query or generate device function code and exposes database metadata through a C++ header for use with the cuFFTDx library.
• Resolved Issues
  • Fixed a correctness issue, identified in CUDA 13.0, that affected a very specific subset of kernels: half- and bfloat16-precision strided R2C and C2R FFTs of size 1.

3.2.2. cuFFT: Release 13.0 Update 1

• Known Issues
  • In CUDA 13.0, a correctness issue affects a specific subset of kernels, namely half and bfloat precision size 1 strided R2C and C2R kernels. A fix will be included in a future CUDA release.

3.2.3. cuFFT: Release 13.0

• New Features
  • Added new error codes:
    * CUFFT_MISSING_DEPENDENCY
    * CUFFT_NVRTC_FAILURE
    * CUFFT_NVJITLINK_FAILURE
    * CUFFT_NVSHMEM_FAILURE
  • Introduced CUFFT_PLAN_NULL, a value that can be assigned to a cufftHandle to indicate a null handle. It is safe to call cufftDestroy on a null handle.
  • Improved performance for single-precision C2C multi-dimensional FFTs and large power-of-2 FFTs.
• Known Issues
  • An issue identified in CUDA 13.0 affects the correctness of a specific subset of cuFFT kernels, specifically half-precision and bfloat16 size-1 strided R2C and C2R transforms. A fix will be included in a future CUDA release.
• Deprecations
  • Removed support for Maxwell, Pascal, and Volta GPUs, corresponding to compute capabilities earlier than Turing.
  • Removed legacy cuFFT error codes:
    * CUFFT_INCOMPLETE_PARAMETER_LIST
    * CUFFT_PARSE_ERROR
    * CUFFT_LICENSE_ERROR
  • Removed the libcufft../_static_nocallback.a static library. Users should link against libcufft../_static.a instead, as both are functionally equivalent.

3.3. cuSOLVER Library

3.3.1. cuSOLVER: Release 13.1

• Resolved Issues
  • Fixed a bug that prevented users from changing the algorithm for cusolverDnXsyevBatched by using cusolverDnSetAdvOptions.[_5539844_]

3.3.2. cuSOLVER: Release 13.0 Update 1

• Resolved Issues
  • Fixed a race condition in cusolverDnXgeev that could occur when using multiple host threads with either separate handles per thread or a shared handle, which caused execution to abort and returned CUSOLVER_STATUS_INTERNAL_ERROR.

3.3.3. cuSOLVER: Release 13.0

• New Features
  • cuSOLVER offers a new math mode to leverage improved performance of emulated FP32 arithmetic on Nvidia Blackwell GPUs.
    To enable and control this feature, the following new APIs have been added:
    * cusolverDnSetMathMode()
    * cusolverDnGetMathMode()
    * cusolverDnSetEmulationStrategy()
    * cusolverDnGetEmulationStrategy()
  • Performance improvements for cusolverDnXsyevBatched() have been made by introducing an internal algorithm switch on Blackwell GPUs for matrices of size n <= 32.
    To revert to the previous algorithm for all problem sizes, use cusolverDnSetAdvOptions().
    For more details, refer to the cusolverDnXsyevBatched() documentation.
• Deprecations
  • cuSOLVERMg is deprecated and may be removed in an upcoming major release. Users are encouraged to use cuSOLVERMp for multi-GPU functionality across both single and multi-node environments. To disable the deprecation warning, add the compiler flag -DDISABLE_CUSOLVERMG_DEPRECATED.
  • cuSOLVERSp and cuSOLVERRf are fully deprecated and may be removed in an upcoming major release. Users are encouraged to use the cuDSS library for better performance and ongoing support.
    For help with the transition, refer to the cuDSS samples or CUDA samples for migrating from cuSOLVERSp to cuDSS.
    To disable the deprecation warning, add the compiler flag: -DDISABLE_CUSOLVER_DEPRECATED.
• Resolved Issues
  • The supported input matrix size for cusolverDnXsyevd, cusolverDnXsyevdx, cusolverDnXsyevBatched, cusolverDn<t>syevd, and cusolverDn<t>syevdx is no longer limited to n <= 32768.
    This update also applies to routines that share the same internal implementation: cusolverDnXgesvdr, cusolverDnXgesvdp, cusolverDn<t>sygvd, cusolverDn<t>sygvdx, and cusolverDn<t>gesvdaStridedBatched.

3.4. cuSPARSE Library

3.4.1. cuSPARSE: Release 13.1

• New Features
  • Introduced an experimental Sparse Matrix-Vector Multiplication (SpMVOp) API that provides improved performance compared with the existing generic CsrMV API. This API supports CSR format with 32-bit indices, double precision, and user-defined epilogues.
  • The nvJitLink shared library is now loaded dynamically at runtime.
  • Improved cusparseXcsrsort with reduced memory usage and higher performance. [_CUSPARSE-2630_]
• Known Issues
  • When using 32-bit indexing, cusparseSpSV and cusparseSpSM may crash if the number of nonzero elements (nnz) approaches 2^31 - 1.[_CUSPARSE-2211_]
• Resolved Issues
  • Fixed potential issues when input and output pointers are not 16-byte aligned in cusparseCsr2cscEx2, cusparseSparseToDense, and CSR/COO cusparseSpMM. [CUSPARSE-2380]
  • Fixed a determinism issue in CSR cusparseSpMM ALG3. [_CUSPARSE-2612_]
  • All routines now support matrices with up to 2^31 - 1 nonzero elements (nnz) when using 32-bit indexing, with the exception of cusparseSpSV and cusparseSpSM. [_CUSPARSE-2153_]
  • Fixed a potential race condition that could occur when dynamically loading driver APIs. [_CUSPARSE-2764_]

3.4.2. cuSPARSE: Release 13.0 Update 1

• New Features
  • Added support for the BSR format in the generic SpMV API (CUSPARSE-2518).
• Deprecation
  • Deprecated the legacy BSR SpMV API (replaced by the generic SpMV API).
• Resolved Issues
  • Enabled all generic APIs to support zero-dimension matrices/vectors (m, n, k = 0) (CUSPARSE-2378).
  • Enabled all generic APIs to support small-dimension matrices/vectors (small m, n, or k) (CUSPARSE-2379).
  • Fixed incorrect results in mixed-precision CSR/COO SpMV computations (CUSPARSE-2349).

3.4.3. cuSPARSE: Release 13.0

• New Features
  • Added support for 64-bit index matrices in SpGEMM computation. (CUSPARSE-2365)
• Known Issues
  • cuSPARSE logging APIs can crash on Windows.
  • CUSPARSE_SPMM_CSR_ALG3 does not return deterministic results as stated in the documentation.
• Deprecation
  • Dropped support for pre-Turing architectures (Maxwell, Volta, and Pascal).
• Resolved Issues
  • Fixed a bug in cusparseSparseToDense_bufferSize that caused it to request up to 16× more memory than required. [CUSPARSE-2352]
  • Fixed unwanted 16-byte alignment requirements on the external buffer. Most routines will now work with any alignment. In the generic API, only cusparseSpGEMM routines are still affected. [CUSPARSE-2352]
  • Fixed incorrect results from cusparseCsr2cscEx2 when any of the input matrix dimensions are zero, such as when m = 0 or n = 0. [CUSPARSE-2319]
  • Fixed incorrect results from CSR SpMV when any of the input matrix dimensions are zero, such as when m = 0 or n = 0. [CUSPARSE-1800]

3.5. Math Library

3.5.1. CUDA Math: Release 13.0

• New Features
  • Single and double precision math functions received targeted performance and accuracy improvements through algorithmic simplifications, reduced branching, and tighter approximations.
    * atan2f, atan2: Up to 10% faster with minor improvements in accuracy.
    * sinhf, coshf, acoshf, asinhf, asinh: Up to 50% speedups with minor improvements in accuracy.
    * cbrtf, rcbrtf: 15% faster with minor improvements in accuracy.
    * erfinvf, erfcinvf, normcdfinvf: Minor accuracy improvements, performance neutral.
    * ldexpf, ldexp: Up to 3x faster in single precision and 30% faster in double precision, with no accuracy loss.
    * modff, modf: Up to 50% faster in single precision and 10% faster in double precision, with no accuracy loss.

3.6. nvJPEG Library

3.6.1. nvJPEG: Release 13.1

Resolved Issues

• nvJPEG’s lossless JPEG 92 (lj92) implementation can now correctly handle lj92 files that contain a comment marker in the header. [5484797]

3.6.2. nvJPEG: Release 13.0 Update 1

Resolved Issues

• Fixed a race condition in certain cases during progressive encoding (5307748).
• Fixed an uninitialized read when encoding images as 4:1:0 JPEG bitstreams (5308008).

3.6.3. nvJPEG: Release 13.0

• Deprecations
  • Removed the nvjpegEncoderParamsCopyHuffmanTables API.

Resolved Issues

• nvJPEG is now more robust and no longer crashes or exhibits undefined behavior when decoding malformed or truncated bitstreams. [5168024, 5133845, 5143450]
• nvjpegEncodeYUV now avoids reading outside of allocated device memory in certain cases. [5133826]
• Optimized memory usage when encoding RGB inputs using the hardware encoder.
• Fixed issues related to rounding in various transform, sampling, and conversion steps, improving image quality for both encoder and decoder. [5064901, 3976092]
• Various bug fixes for improved security.

3.7. NPP Library

3.7.1. NPP: Release 13.1

• Resolved Issues
  • Fixed an issue in nppiCFAToRGB_8u_C1C3R() affecting SSIM validation for NPPI_BAYER_GBRG patterns. [5192648]

3.7.2. NPP: Release 13.0

• Deprecations
  • Removal of Legacy Non-Context APIs
    All legacy NPP APIs without the _Ctx suffix have been deprecated and are now removed starting with this release. Developers should transition to the context-aware (_Ctx) versions to ensure continued support and compatibility with the latest CUDA releases.
  • Deprecation of ``nppGetStreamContext()``
    The nppGetStreamContext() API has been deprecated and removed. Developers are strongly encouraged to adopt application-managed stream contexts by explicitly managing the NppStreamContext structure. For guidance, refer to the NPP Documentation – General Conventions and the usage demonstrated in the StreamContexts example.
• Resolved Issues
  • Fixed an issue in nppiFloodFillRange_8u_C1IR_Ctx where the flood fill operation did not correctly fill the full target area. [5141474]
  • Resolved a bug in the nppiDebayer() API that affected proper reconstruction of color data during Bayer pattern conversion. [5138782]

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4.2. OpenCL

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