CUDA Toolkit 13.0 - Release Notes
1. OverviewïWelcome to the release notes for NVIDIA® CUDA® Toolkit 13.0. 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.
Table 1 CUDA 13.0 Component VersionsïNote
Starting with CUDA 11, individual components within the CUDA Toolkit (for example: compiler, libraries, tools) are versioned independently.
For CUDA 13.0 , 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
13.0.48
CUDA Application Compiler (crt)
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA Compilation Optimizer (ctadvisor)
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA Runtime (cudart)
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA culibos
13.0.39
x86_64, arm64-sbsa
Linux
CUDA cuobjdump
13.0.39
x86_64, arm64-sbsa
Linux, Windows
CUPTI
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuxxfilt (demangler)
13.0.39
x86_64, arm64-sbsa
Linux, Windows
CUDA Documentation
13.0.39
x86_64
Linux, Windows
CUDA GDB
13.0.39
x86_64, arm64-sbsa
Linux, WSL
CUDA Nsight Eclipse Plugin
13.0.39
x86_64
Linux
CUDA NVCC
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvdisasm
13.0.39
x86_64, arm64-sbsa
Linux, Windows
CUDA NVML Headers
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvprune
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA NVRTC
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA NVTX
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA OpenCL
13.0.39
x86_64
Linux, Windows
CUDA Profiler API
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA Sandbox dev
13.0.39
x86_64, arm64-sbsa
Linux, WSL
CUDA Compute Sanitizer API
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuBLAS
13.0.0.19
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuFFT
12.0.0.15
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuFile
1.15.0.42
x86_64, arm64-sbsa
Linux
CUDA cuRAND
10.4.0.35
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuSOLVER
12.0.3.29
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA cuSPARSE
12.6.2.49
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA NPP
13.0.0.50
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvFatbin
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvJitLink
13.0.39
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvJPEG
13.0.0.40
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvptxcompiler
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
CUDA nvvm
13.0.48
x86_64, arm64-sbsa
Linux, Windows, WSL
Nsight Compute
2025.3.0.19
x86_64, arm64-sbsa
Linux, Windows, WSL (Windows 11)
Nsight Systems
2025.3.2.367
x86_64, arm64-sbsa
Linux, Windows, WSL
Nsight Visual Studio Edition (VSE)
2025.3.0.25168
x86_64 (Windows)
Windows
nvidia_fs1
2.26.6
x86_64, arm64-sbsa
Linux
Visual Studio Integration
13.0.39
x86_64 (Windows)
Windows
NVIDIA Linux Driver
580.65.06
x86_64, arm64-sbsa
Linux
2.2. CUDA DriverïTable 2 CUDA Toolkit and Minimum Required Driver Version for CUDA Minor Version CompatibilityïRunning 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 â 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.
Table 3 CUDA Toolkit and Corresponding Driver VersionsïCUDA Toolkit
Toolkit Driver Version
Linux x86_64 Driver Version
Windows x86_64 Driver Version
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 âN/Aâ 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:
https://docs.nvidia.com/deploy/cuda-compatibility/index.html
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 CUDAâs 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 CUDAïArm platforms are now unified in CUDA Toolkit, enabling single-install and consistent builds across all Arm architectures. Note that this applies to new architectures only and does not apply to Jetson Orin, which remains as-is.
Updated vector types with 32-byte alignment for increased load/store performance on Blackwell (see additional details below).
Added support for the following new operating system distributions:
Red Hat Enterprise Linux 10.0 and 9.6
Debian 12.10
Fedora 42
Rocky Linux 10.0 and 9.6
SM101 has been renumbered as SM110 from this release.
CUDA 13.0 supports all NVIDIA architectures from Turing through Grace Blackwell.
Added support for cuMemCreate and cudaMallocAsync on the host, with CU_MEM_LOCATION_TYPE_HOST.
Improved error reporting from CUDA batched memcpy APIs, using CUDA rich error reporting introduced in CTK 12.9. As a result, the batched memcpy APIs have been superseded with a new function prototype eliding the now-unnecessary failIdx out parameter.
Added CUDA Runtime API support for rich error reporting.
cuda-checkpoint utility updated to allow GPU migration. Users can now specify how GPUs from the old and new machines should be matched by specifying UUID pairs.
Coherent memory platforms (including Grace Hopper) can now be initialized in non-NUMA mode, where video memory and kernel memory are managed separately as on more traditional platforms (this does not affect coherency or bandwidth, only memory organization).
Fatbin file compression now uses Zstd instead of LZ4, improving compression ratios and resulting in smaller generated binaries.
Added cuGreenCtxGetId function to allow unique identification of green context IDs without needing to convert from a primary context.
Registers can now additionally spill to shared memory, which is approximately 10x lower latency than spilling to L2 cache. This feature is opt-in and controllable via the pragma enable_smem_spilling.
CUDA Runtime now uses contextless loading.
Hostnames are now supported as part of nvidia-imex nodes_config domain definitions. This allows dynamic reassignment of the underlying IP addresses of compute nodes by updating the DNS or /etc/hosts files.
All Windows userspace .exe and .dll files are now signed.
Added support for managed memory discard in UVM, through the following new APIs:
cuMemDiscardBatchAsync
cuMemDiscardAndPrefetchBatchAsync
For changes to PTX, refer to https://docs.nvidia.com/cuda/parallel-thread-execution/#ptx-isa-version-9-0.
CUDA Runtime API:
The prototype for the CUDA Runtime API function cudaGraphKernelNodeCopyAttributes has been updated. It now takes hDst followed by hSrc to align with the calling convention used by most copy APIs.
nvJitLinkCreate:
The options parameter of nvJitLinkCreate is now of type const char * const * instead of const char **.
General Compiler (NVCC, nvFatbin, fatbinary):
The default compression mode is now equivalent to using --compress-mode=balance. As a result, newly generated binaries will benefit from the higher compression ratio provided by the Zstandard algorithm.
NVCC:
A new flag,
-jobserver, has been added to NVCC to control thread usage by-split-compileand--threadswhen invoked under GNU Make with-jN(whereN > 1). This option is supported on Linux only.
Requires GNU Make 4.4+ to work out of the box.
Can also work with GNU Make 4.3+ with appropriate Makefile modifications.
For more details, see the NVCC option listing.
Fatbinary:
The -image option, which was previously deprecated, has been removed in CUDA 13.0. Users should use -image3 or -image2 instead, depending on their requirements.
New host compiler support in CUDA 13.0:
Clang 20
GCC 15
libNVVM
libNVVM has been updated to align with the LLVM 20 release.
Split from the NVCC component
libNVPTXCompiler
Split from the NVCC component
CUDA CRT
Split from the NVCC component
A new developer tool, Compile Time Advisor (ctadvisor), has been introduced in CUDA 13.0. This tool helps users analyze the compilation time of their CUDA C++ code and provides actionable suggestions to reduce build time.
For details on new features, improvements, and bug fixes, see the changelogs for:
CUDA cuLibOS
Split from cudart
The behavior of cuInit on Windows has been unified with Linux for systems using NVSwitch. If the Fabric Manager service is not running and the system is expected to use NVSwitch or NVLink for P2P connectivity, cuInit will return CUDA_ERROR_NOT_READY until the configuration issue is resolved.
Under certain scenarios involving an over-aligned type and the MSVC #pragma pack directive, NVCC incorrectly computed the size, alignment, and other layout characteristics of the type.
This issue can occur when using an over-aligned type as the embedded object of std::optional<T> from the MSVC STL. It only affects Windows when NVCC is used with an MSVC host compiler.
For example, NVCC may incorrectly determine the layout of variable z in the following code:
struct alignas(16) complex {
double x;
double y;
};
std::optional<complex> z;
This may lead to a crash or out-of-memory error during later compilation stages. The issue was introduced in CUDA 12.9. It is fixed in CUDA 13.0.
When the [[no_unique_address]] attribute is applied to an empty member variable within an otherwise empty object, NVCC may crash during the device compilation stage.
For example, the following code could trigger a crash:
struct Foo { };
struct Bar {
[[no_unique_address]] Foo x{};
};
This issue is resolved in CUDA 13.0.
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 -34Fixes 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
nokaslrtoGRUB_CMDLINE_LINUX_DEFAULT:GRUB_CMDLINE_LINUX_DEFAULT="quiet splash nokaslr"Then, update GRUB and reboot:
sudo update-grub sudo rebootOption 2: Disable HMM for UVM
Create or edit /etc/modprobe.d/uvm.conf.
Add or update the following line:
options nvidia_uvm uvm_disable_hmm=1Unload and reload the
nvidia_uvmkernel module or reboot the system:sudo modprobe -r nvidia_uvm sudo modprobe nvidia_uvm
On Mediatek Dimensity Auto Cockpit platforms, setting the NvSciBufGeneralAttrKey_EnableGpuCompression attribute causes NvSciBuf import into CUDA to fail with the following error:
cudaErrorInvalidValue / CUDA_ERROR_INVALID_VALUE
Compression is not supported in CUDA for NvSciBuf interoperability on this platform.
Starting with CUDA 13.0, on Windows Server 2022 systems with NVSwitch deployments in TCC mode, cuInit() may return CUDA_ERROR_NOT_READY if the Fabric Manager (FM) is not running or the GPU fabric is misconfigured. [5328746]
This change aligns Windows behavior with Linux, where the driver now fails initialization instead of silently falling back to reduced performance.
To avoid this issue:
Ensure the Fabric Manager service is correctly installed and running.
Alternatively, configure the RmGpuFabricProbe registry key if the system is intended to run without NVSwitch and Fabric Manager.
This change may introduce regressions in legacy workflows that relied on the previous fallback behavior. It is recommended to validate affected systems using NVSwitch on Windows.
Calling cuDLA APIs on the Tegra THOR platform is not supported, as the THOR SoC does not include a DLA engine. Attempting to do so may result in unexpected behavior.
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
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.
Support for Ubuntu 20.04 has been dropped starting with this release. Users are advised to migrate to Ubuntu 22.04 LTS or later.
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)
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 Libraryï 3.1.1. cuBLAS: Release 13.0ïNew 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_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.
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.
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.
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 16Ã 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]
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.
Deprecations
Removed the nvjpegEncoderParamsCopyHuffmanTables API.
Resolved Issues
3.7. NPP Libraryï 3.7.1. NPP: Release 13.0ï
nvJPEG is now more robust and no longer crashes or exhibits undefined behavior when decoding malformed or truncated bitstreams. [5168024, 5133845, 5143450]
nvjpegEncodeYUVnow 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.
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 â 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]
This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (âNVIDIAâ) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.
NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.
Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.
NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (âTerms of Saleâ). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.
NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customerâs own risk.
NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customerâs sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customerâs product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs.
No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.
Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices.
THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, âMATERIALSâ) ARE BEING PROVIDED âAS IS.â NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAâs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product.
4.2. OpenCLïOpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.
4.3. TrademarksïNVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4