CUDA Toolkit 13.0 Update 1 - Release Notes
1. Overview
Welcome to the release notes for NVIDIA® CUDA® Toolkit 13.0 Update 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 13.0 Update 1, the table below indicates the versions:
Component Name |
Version Information |
Supported Architectures |
Supported Platforms |
|
---|---|---|---|---|
CUDA C++ Core Compute Libraries |
Thrust |
3.0.1 |
x86_64, arm64-sbsa |
Linux, Windows |
CUB |
3.0.1 |
|||
libcu++ |
3.0.1 |
|||
Cooperative Groups |
13.0.85 |
|||
CUDA Application Compiler (crt) |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA Compilation Optimizer (ctadvisor) |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA Runtime (cudart) |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA culibos |
13.0.85 |
x86_64, arm64-sbsa |
Linux |
|
CUDA cuobjdump |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows |
|
CUPTI |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuxxfilt (demangler) |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows |
|
CUDA Documentation |
13.0.85 |
x86_64 |
Linux, Windows |
|
CUDA GDB |
13.0.85 |
x86_64, arm64-sbsa |
Linux, WSL |
|
CUDA Nsight Eclipse Plugin |
13.0.85 |
x86_64 |
Linux |
|
CUDA NVCC |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvdisasm |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows |
|
CUDA NVML Headers |
13.0.87 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvprune |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA NVRTC |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA NVTX |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA OpenCL |
13.0.85 |
x86_64 |
Linux, Windows |
|
CUDA Profiler API |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA Sandbox dev |
13.0.85 |
x86_64, arm64-sbsa |
Linux, WSL |
|
CUDA Compute Sanitizer API |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuBLAS |
13.0.2.14 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuFFT |
12.0.0.61 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuFile |
1.15.1.6 |
x86_64, arm64-sbsa |
Linux |
|
CUDA cuRAND |
10.4.0.35 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuSOLVER |
12.0.4.66 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA cuSPARSE |
12.6.3.3 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA NPP |
13.0.1.2 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvFatbin |
13.0.85 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvJitLink |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvJPEG |
13.0.1.86 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvptxcompiler |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvvm |
13.0.88 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
CUDA nvsdm |
580.82.07 |
x86_64 |
Linux, Windows, WSL |
|
Nsight Compute |
2025.3.1.4 |
x86_64, arm64-sbsa |
Windows, WSL (Windows 11) |
|
Nsight Systems |
2025.3.2.474 |
x86_64, arm64-sbsa |
Linux, Windows, WSL |
|
Nsight Visual Studio Edition (VSE) |
2025.3.1.25227 |
x86_64 (Windows) |
Windows |
|
nvidia_fs1 |
2.26.6 |
x86_64, arm64-sbsa |
Linux |
|
Visual Studio Integration |
13.0.85 |
x86_64 (Windows) |
Windows |
|
NVIDIA Linux Driver |
580.82.07 |
x86_64, arm64-sbsa |
Linux |
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
CTK Version |
Driver Range for Minor Version Compatibility |
|
---|---|---|
Min |
Max |
|
13.x |
>= 580 |
N/A |
12.x |
>= 525 |
< 580 |
11.x |
>= 450 |
< 525 |
* 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 13.0, 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
CUDA Toolkit |
Toolkit Driver Version |
|
---|---|---|
Linux x86_64 Driver Version |
Windows x86_64 Driver Version |
|
CUDA 13.0 Update 1 |
>=580.82.07 |
N/A |
CUDA 13.0 GA |
>=580.65.06 |
N/A |
CUDA 12.9 Update 1 |
>=575.57.08 |
>=576.57 |
CUDA 12.9 GA |
>=575.51.03 |
>=576.02 |
CUDA 12.8 Update 1 |
>=570.124.06 |
>=572.61 |
CUDA 12.8 GA |
>=570.26 |
>=570.65 |
CUDA 12.6 Update 3 |
>=560.35.05 |
>=561.17 |
CUDA 12.6 Update 2 |
>=560.35.03 |
>=560.94 |
CUDA 12.6 Update 1 |
>=560.35.03 |
>=560.94 |
CUDA 12.6 GA |
>=560.28.03 |
>=560.76 |
CUDA 12.5 Update 1 |
>=555.42.06 |
>=555.85 |
CUDA 12.5 GA |
>=555.42.02 |
>=555.85 |
CUDA 12.4 Update 1 |
>=550.54.15 |
>=551.78 |
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 |
CUDA Toolkit driver bundling (pre-CUDA 13.0):
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:
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 13.0 (Windows-specific):
Starting with CUDA 13.0, 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
CUDA 13.0 is a new major version. CUDA follows semantic versioning, and guarantees ABI stability within the major version series. CUDA Toolkit releases in the 13.x series are ABI-compatible with drivers corresponding to the same series (r580 and newer). Note that CUDA’s API can evolve over the course of a major release to include new functionality, and not all new functionality may be supported on older drivers.
2.3.1. General CUDA
Increased the MPS server client limit from 48 to 60 on NVIDIA Ampere Architecture GPUs and later architectures. The limit remains 48 on NVIDIA Turing Architecture GPUs and 36 on NVIDIA Tegra products. These limits are subject to change in future architectures.
2.3.2. CUDA Compiler
For changes to PTX, refer to https://docs.nvidia.com/cuda/parallel-thread-execution/#ptx-isa-version-9-0.
2.3.3. CUDA Developer Tools
For details on new features, improvements, and bug fixes, see the changelogs for:
2.4. Deprecated or Dropped Features
2.4.1. 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 fromcuda_runtime_api.h
.The accompanying parameter struct
cudaLaunchParam
has been removed fromdriver_types.h
.this_multi_grid
andmulti_grid_group
have been removed fromcooperative_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
fieldkernelExecTimeoutEnabled
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
2.4.2. 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.
2.4.3. 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.
2.4.4. Deprecated or Dropped CUDA Toolchains
CUDA Tools
The profiling tools NVIDIA Visual Profiler and nvprof have been removed in CUDA 13.0. Users are encouraged to migrate to the next-generation tools:
NVIDIA Nsight Systems for GPU and CPU sampling and tracing
NVIDIA Nsight Compute for detailed GPU kernel profiling
The pre-built CUDA Demo Suite applications have been removed in CUDA 13.0. Users are encouraged to build CUDA Samples from source, available on GitHub.
The
nv-p2p
APIs are deprecated starting with CUDA 13.0 and will be removed in CUDA 14.0. These APIs remain functional in CUDA 13.0 but will no longer receive enhancements or bug fixes.The following CUPTI APIs and features have been removed in CUDA 13.0:
CUPTI Event API (
cupti_events.h
)CUPTI Metric API (
cupti_metrics.h
)PC Sampling Activity API (
cupti_activity.h
)Source/SASS-level metrics (
cupti_activity.h
)
The following APIs have been deprecated in CUDA 13.0:
CUPTI Profiling API (
cupti_profiler_target.h
)Perfworks Metric API (
nvperf_host.h
)
Users are encouraged to transition to the following replacements:
CUPTI Range Profiling API (
cupti_range_profiler.h
)CUPTI Profiler Host API (
cupti_profiler_host.h
)
CUDA Compiler
The following host compilers are no longer supported as of CUDA 13.0:
Intel ICC 2021.7
Microsoft Visual Studio 2017 (MSVC 2017)
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 13.0 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 whenbeta = 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 ofalpha
/beta
, which must be 0 or 1. (CUB-8873)
3.1.2. cuBLAS: Release 13.0
New Features
The
cublasGemmEx
,cublasGemmBatchedEx
, andcublasGemmStridedBatchedEx
functions now acceptCUBLAS_GEMM_AUTOTUNE
as a valid value for thealgo
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 currentcublasHandle_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_SUPPORTED
if 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 13.0 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.
3.2.2. 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 acufftHandle
to indicate a null handle. It is safe to callcufftDestroy
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 againstlibcufft_static.a
instead, as both are functionally equivalent.
3.3. cuSOLVER Library
3.3.1. 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.
3.3.2. 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 sizen <= 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
andcuSOLVERRf
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
tocuDSS
.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
, andcusolverDn<t>syevdx
is no longer limited ton <= 32768
.This update also applies to routines that share the same internal implementation:
cusolverDnXgesvdr
,cusolverDnXgesvdp
,cusolverDn<t>sygvd
,cusolverDn<t>sygvdx
, andcusolverDn<t>gesvdaStridedBatched
.
3.4. cuSPARSE Library
3.4.1. 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).
3.4.2. 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 whenm = 0
orn = 0
. [CUSPARSE-2319]Fixed incorrect results from CSR SpMV when any of the input matrix dimensions are zero, such as when
m = 0
orn = 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 13.0 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).
3.6.2. 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 13.0
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 theNppStreamContext
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
OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.
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