CUDA Toolkit 13.4 Update 1 - Release Notes

1. Overview

Welcome to the release notes for NVIDIA® CUDA® Toolkit 13.4 Update 1. This release includes enhancements and fixes across the CUDA Toolkit and its libraries.

This documentation is organized into two main sections:

  • CUDA Platform

    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. CUDA Platform

2.1. CUDA Toolkit Major Components

For CUDA 13.4 Update 1, the table below indicates the versions:

Table 1 CUDA 13.4 Update 1 Component Versions

Component Name

Version Information

Supported Architectures

Supported Platforms

CUDA C++ Core Compute Libraries

Thrust

3.4.3

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUB

3.4.3

libcu++

3.4.3

Cooperative Groups

13.3.4.3.1

CUDA Compatibility Package (Orin)

13.4.47145772

arm64-sbsa

Linux

CUDA Application Compiler (crt)

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA Compilation Optimizer (ctadvisor)

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA Runtime (cudart)

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA culibos

13.4.92

x86_64, arm64-sbsa

Linux

CUDA cuobjdump

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUPTI

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuxxfilt (demangler)

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA Documentation

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA GDB

13.4.92

x86_64, arm64-sbsa

Linux

CUDA NVCC

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvdisasm

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA NVML Headers

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvprune

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA NVRTC

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA NVTX

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA OpenCL

13.4.92

x86_64, arm64 (Windows)

Linux, Windows

CUDA Profiler API

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA Sandbox dev

13.4.92

x86_64, arm64-sbsa

Linux

CUDA Compute Sanitizer API

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA TILE-IR AS

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuBLAS

13.8.0.4

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuDLA

13.4.92

arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuFFT

12.4.0.43

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuFile

1.19.1.55

x86_64, arm64-sbsa

Linux

CUDA cuobjclient

1.3.1.55

x86_64, arm64-sbsa

Linux

CUDA cuRAND

10.4.4.72

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuSOLVER

12.3.4.7

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA cuSPARSE

12.8.6.72

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA NPP

13.2.0.58

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvFatbin

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvJitLink

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvJPEG

13.2.3.58

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvptxcompiler

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

CUDA nvvm

13.4.92

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

Nsight Compute

2026.3.1.2

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

Nsight Systems

2026.3.2.476

x86_64, arm64-sbsa, arm64 (Windows)

Linux, Windows

Nsight Visual Studio Edition (VSE)

2026.3.0.26187

x86_64, arm64 (Windows)

Windows

Visual Studio Integration

13.4.92

x86_64, arm64 (Windows)

Windows

2.2. CUDA Driver

Note

The NVIDIA driver is no longer bundled with the CUDA Toolkit – on Windows starting with CUDA 13.1, and on Linux starting with CUDA 13.4. Download and install the appropriate driver from the official NVIDIA Driver Downloads page.

Running a CUDA application requires a system with at least one CUDA-capable GPU and a driver that is compatible with the CUDA Toolkit. For more information about various GPU products that are CUDA-capable, visit https://developer.nvidia.com/cuda/gpus.

The NVIDIA driver branch corresponding to each CUDA Toolkit release is shown below. Update releases within a CUDA minor version use the same driver branch.

Table 2 CUDA Toolkit and Corresponding Driver Branch

CUDA Toolkit

Corresponding Driver Branch

CUDA 13.4

R615

CUDA 13.3

R610

CUDA 13.2

R595

CUDA 13.1

R590

CUDA 13.0

R580

Note

Existing CUDA 13.x applications run on drivers >=580 under CUDA minor version compatibility. CUDA 13.4 new features and newly enabled platforms require an R615 or later driver that supports them. The Windows driver for the RTX Spark device is 616.41 or later.

The CUDA driver is backward compatible: applications compiled against a particular CUDA Toolkit version continue to work on subsequent (later) driver releases. In addition, CUDA minor version compatibility allows applications to run on a driver older than the corresponding driver branch, within the ranges shown below. The installed driver must meet or exceed the minimum required version for the CUDA Toolkit. For details, see the CUDA Compatibility Guide and CUDA Compatibility and Upgrades.

Table 3 CUDA Toolkit and Minimum Required Driver Version for CUDA Minor Version Compatibility

CTK Version

Driver Range for Minor Version Compatibility

Min

Max

13.x

>= 580

N/A

12.x

>= 525

< 580

11.x

>= 450

< 525

CUDA 11.0 shipped with earlier driver versions. Minor-version compatibility across the CUDA 11.x family requires driver version 450.80.02 or later on Linux, or 452.39 or later on Windows.

Older CUDA versions (12.9 and earlier)
Table 4 Corresponding Driver Versions (CUDA 12.9 and earlier)

CUDA Toolkit

Corresponding Driver Version

Linux x86_64 Driver Version

Windows x86_64 Driver Version

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

2.3. New Features

2.3.1. CUDA Platform

  • None

2.3.2. CUDA Developer Tools

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

2.3.3. CUDA Compiler

2.3.4. CUDA C++ Core Libraries (CCCL)

  • None

2.3.5. CUDA Python

  • None

2.3.6. CUDA Tile

  • None

2.3.7. CUDA Tile IR

  • None

2.4. Resolved Issues

2.4.1. General CUDA

  • Fixed an issue where applications statically built with, or dynamically linking to, an older CUDA Runtime (down to CUDA 11.2) with the R615 driver could report benign public error messages indicating that IMEX channels were incorrectly set up.

2.4.2. CUDA Compiler

  • Fixed an issue where kernels using the fabric.try_put.tensor, fabric.try_red.tensor, or fabric.try_atom PTX instructions did not set the fabric-write grid attribute, which could leave fabric writes unfenced at grid completion when the kernel was launched in a CUDA graph.

2.4.3. CUDA Tools

  • None

2.5. Known Issues

2.5.1. CUDA Platform

  • None

2.5.2. CUDA Compiler

  • None

2.6. Deprecated or Dropped Features

2.6.1. CUDA Platform

  • Legacy Nsight Eclipse Edition plugins are no longer delivered in CUDA Toolkit packages beginning with CUDA 13.3.

  • Python 3.10 support is deprecated across the CUDA Python 13.4 packages.

2.6.2. Architectures

  • CUDA 14.0 will move to Armv8.2-A as the minimum supported architecture for ARM64-SBSA.

2.6.3. Operating Systems

  • None

2.6.4. CUDA Toolchains

  • None

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 13.4 Update 1

  • New Features

    • Emulated FP64 matrix multiplications:

      • Improved ZGEMM peak performance by up to 25% on Rubin GPUs.

      • When the per-handle workspace is insufficient, fixed-point FP64 emulation now allocates its temporary workspace from a cuBLAS-managed, per-device CUDA memory pool that retains memory across stream synchronizations. This avoids repeated allocation overhead without requiring users to tune the default memory pool.

  • Known Issues

    • When running NVIDIA Compute Sanitizer (versions up to CUDA Toolkit 13.4) with cuBLAS-linked workloads, the tool may report false-positive errors regarding invalid global reads or out-of-bounds (OOB) memory accesses inside cuBLAS accelerated kernels. This is a known issue caused by a limitation in Compute Sanitizer’s ability to accurately identify the PTX semantics of the asynchronous global-to-shared memory copy instruction cp.async.cg.shared.global [dst], [src], cp-size, src-size when src-size < cp-size. In this specific scenario, where the number of bytes to copy exceeds the source buffer size, the hardware guarantees that the remaining bytes (cp-size - src-size) in the destination shared memory are automatically padded with zeros, ensuring that the memory access is neither invalid nor OOB. Hence, Compute Sanitizer reports for these specific cases can be safely ignored. [6196059]

    • cuBLASLt Grouped GEMM with per-batch tensor-wide scales causes an invalid memory access for groups where m > 0, n > 0, and k = 0. As a workaround, always pass valid scale pointers for each group. This issue was introduced in CUDA Toolkit 13.1 (cuBLAS 13.2.0). [CUB-10481]

  • Resolved Issues

    • Fixed an issue where cuBLASLt Grouped GEMM operations with CUBLAS_POINTER_MODE_HOST could lead to incorrect results on Hopper GPUs when the number of waves was larger than 2. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6681084]

    • Fixed an issue where cuBLASLt Grouped GEMM operations could lead to incorrect results on B300 and Rubin GPUs when the input matrices used the NVFP4 data type, the number of waves was larger than 3, and the algorithm attribute CUBLASLT_ALGO_CONFIG_STAGES_ID was CUBLASLT_MATMUL_STAGES_768xAUTO. This issue was introduced in CUDA Toolkit 13.4 (cuBLAS 13.7.0). [6681084]

    • Fixed an issue where cublasLtMatmul() could return incorrect output when using split-K if the number of K splits (SPLITK_NUM) did not evenly divide the K dimension and the floor division of K / SPLITK_NUM was an exact multiple of the stage size. This affected only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 9.0, 10.x, and 11.x. This issue was introduced in CUDA Toolkit 12.6 Update 2 (cuBLAS 12.6.3). [6580689]

    • Fixed an issue where cublasLtMatmul() could produce incorrect results on B300 and Rubin GPUs when the input matrices used the NVFP4 data type. This affected only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 and the algorithm attribute CUBLASLT_ALGO_CONFIG_STAGES_ID set to CUBLASLT_MATMUL_STAGES_768xAUTO. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1). [CUB-10573]

3.1.2. cuBLAS: Release 13.4

  • New Features

    • Emulated FP64 matrix multiplications:

      • Emulated FP64 matrix multiplications now leverage the Ozaki-II scheme when it provides a performance benefit over the Ozaki-I scheme; the Ozaki-II scheme is supported on NVIDIA Ampere and newer GPUs.

      • On B200 and RTX PRO 6000 Blackwell Server Edition GPUs, this enables up to 175 and 45 TFLOPS of emulated DGEMM performance, respectively, and up to 295 and 70 TFLOPS of emulated ZGEMM performance, respectively, by leveraging the 2M algorithm.

      • Emulated FP64 matrix multiplications now support Rubin GPUs (compute capability 10.7), leveraging the Ozaki-I and Ozaki-II schemes and the new TI16 type via the tcgen05.mma instruction to achieve up to 212 TFLOPS of emulated DGEMM performance. Emulated ZGEMM can reach up to 301 TFLOPS, with further improvements to come.

    • cuBLASLt adds experimental support for an alternative scaling-factor layout for MXFP8 matmuls through the CUBLASLT_MATMUL_MATRIX_SCALE_VEC32_MN_K4_UE8M0 and CUBLASLT_MATMUL_MATRIX_SCALE_VEC128_MN_K4_UE8M0 scaling modes. For more information, see the cuBLAS documentation.

    • Added support for the NVIDIA Rubin (compute capability 10.7) GPU architecture.

    • cuBLASLt Grouped GEMM performance is improved on Blackwell data center GPUs through dynamic scheduling of matrix computations. Improvements of up to 20% can be seen for Grouped GEMM calls with a large number of groups (for example, 32). [5913842] [5992105] [CUB-9925]

  • Known Issues

    • When running NVIDIA Compute Sanitizer (versions up to CUDA Toolkit 13.4) with cuBLAS-linked workloads, the tool may report false-positive errors regarding invalid global reads or out-of-bounds (OOB) memory accesses inside cuBLAS accelerated kernels. This is a known issue caused by a limitation in Compute Sanitizer’s ability to accurately identify the PTX semantics of the asynchronous global-to-shared memory copy instruction cp.async.cg.shared.global [dst], [src], cp-size, src-size when src-size < cp-size. In this specific scenario, where the number of bytes to copy exceeds the source buffer size, the hardware guarantees that the remaining bytes (cp-size - src-size) in the destination shared memory are automatically padded with zeros, ensuring that the memory access is neither invalid nor OOB. Hence, Compute Sanitizer reports for these specific cases can be safely ignored. [6196059]

    • cuBLASLt Grouped GEMM with per-batch tensor-wide scales causes an invalid memory access for groups where m > 0, n > 0, and k = 0. As a workaround, always pass valid scale pointers for each group. This issue was introduced in CUDA Toolkit 13.1 (cuBLAS 13.2.0). [CUB-10481]

    • cublasLtMatmul() can return incorrect output when using split-K if the number of K splits (SPLITK_NUM) does not evenly divide the K dimension and the floor division of K / SPLITK_NUM is an exact multiple of the stage size. This affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 9.0, 10.x, and 11.x. As a workaround, use cublasLtMatmulAlgoConfigGetAttribute() to query the number of K splits (CUBLASLT_ALGO_CONFIG_SPLITK_NUM) and the stage size (derived from CUBLASLT_ALGO_CONFIG_STAGES_ID), then use cublasLtMatmulAlgoConfigSetAttribute() to set a value that evenly divides the K dimension, or for which K / SPLITK_NUM is not an exact multiple of the stage size. This issue was first introduced in CUDA Toolkit 12.6 Update 2 (cuBLAS 12.6.3). [6580689]

    • cuBLASLt Grouped GEMM operations with CUBLAS_POINTER_MODE_HOST can lead to incorrect results on Hopper GPUs when the number of waves is larger than 2. As a workaround, pass device alpha and beta using CUBLAS_POINTER_MODE_DEVICE. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6681084]

    • cuBLASLt Grouped GEMM operations can lead to incorrect results on B300 and Rubin GPUs when the input matrices use the NVFP4 data type, the number of waves is larger than 3, and the algorithm attribute CUBLASLT_ALGO_CONFIG_STAGES_ID is CUBLASLT_MATMUL_STAGES_768xAUTO. As a workaround, skip such candidates when using cublasLtMatmulAlgoGetHeuristic(). [6681084]

    • cublasLtMatmul() can produce incorrect results on B300 and Rubin GPUs when the input matrices use the NVFP4 data type. This affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 and the algorithm attribute CUBLASLT_ALGO_CONFIG_STAGES_ID is CUBLASLT_MATMUL_STAGES_768xAUTO. [CUB-10573]

  • Resolved Issues

    • Between CUDA Toolkit 13.3 Update 1 and 13.4, cuBLAS released an independent patch release (cuBLAS 13.6.1) that resolves several issues. Refer to the associated cuBLAS patch release notes for details.

    • Fixed an issue where cublasLtMatmulAlgoGetHeuristic() could return no algorithms, and cublasLtMatmul() could return CUBLAS_STATUS_NOT_SUPPORTED, for some NVFP4 matmuls with NVFP4 output and the CUBLASLT_EPILOGUE_BIAS epilogue on GPUs with compute capability 12.x. [CUB-10222]

    • Fixed an issue where cublasLtMatmulAlgoGetHeuristic() returned CUBLAS_STATUS_INTERNAL_ERROR and invalidated an in-flight CUDA graph capture when called while a non-default blocking stream was being captured. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1). [6288786]

    • Fixed an issue where cublas<t>gemv() with trans equal to CUBLAS_OP_T or CUBLAS_OP_C could perform an illegal memory access when the number of elements addressed by the output vector (n * incy) exceeded the 32-bit index range. cublasZgemv() could additionally return CUBLAS_STATUS_NOT_SUPPORTED when n * lda exceeded that range. [6207926]

    • Fixed an issue where cublasZgemv() with trans equal to CUBLAS_OP_C and incy greater than 1 produced incorrect results when FP64 fixed-point emulation was enabled (CUBLAS_FP64_EMULATED_FIXEDPOINT_MATH with CUBLAS_EMULATION_STRATEGY_EAGER). [6207926]

3.1.3. cuBLAS: Release 13.3 Update 1

  • New Features

    • The TMA-based kernel (Hopper and newer) now accelerates DSYMV in addition to the already-enabled SSYMV. The 16-byte alignment requirement for the A pointer was dropped for this kernel, and support for atomics was added through cublasSetAtomicsMode(). The geomean speedup across architectures and datatypes is 1.3x, and up to 5.9x.

  • Known Issues

    • Non-default epilogues are unintentionally allowed for cublasLtMatmul() with int8 inputs using regular data ordering and scale type CUDA_R_32F. This is an undocumented and lightly tested feature that users are discouraged from using, and it is planned for removal in the next major release. [CUB-10067]

    • In cuBLASLt, the heuristics for the Grouped GEMM API return sub-optimal algorithms when the C and D matrices use CUBLASLT_ORDER_ROW ordering. As a workaround, swap CUBLASLT_MATMUL_PREF_GROUPED_DESC_D_AVERAGE_ROWS and CUBLASLT_MATMUL_PREF_GROUPED_DESC_D_AVERAGE_COLS in the preferences before calling cublasLtMatmulAlgoGetHeuristic(). [6335555]

    • Multiple cuBLASLt Grouped GEMM operations reusing the same workspace on Hopper GPUs may lead to hangs or unspecified launch failures. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6456362]

    • Calling cublasLtMatmulAlgoGetHeuristic() while a non-default blocking stream is being captured returns CUBLAS_STATUS_INTERNAL_ERROR. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1). [6288786]

    • GEMV-like operations (e.g., cublas<t>gemv(), or cublasLtMatmul() with M or N equal to 1) may return CUBLAS_STATUS_NOT_SUPPORTED for certain shapes and workspace configurations. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1).

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

    • GEMV-like operations (e.g., cublas<t>gemv(), or cublasLtMatmul() with M or N equal to 1) may produce incorrect results on GPUs with compute capability 12.0 or 12.1 when the matrix is transposed, its non-accumulation dimension is greater than 3145680, and beta is not equal to 0. This issue was introduced in CUDA Toolkit 13.3 Update 1 (cuBLAS 13.6.0). [CUB-10445]

    • cuBLASLt Grouped GEMM operations with CUBLAS_POINTER_MODE_HOST can lead to incorrect results on Hopper GPUs when the number of waves is larger than 2. As a workaround, pass device alpha and beta using CUBLAS_POINTER_MODE_DEVICE. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6681084]

  • Resolved Issues

    • Fixed an issue where cublasXt<t>spmm() could produce incorrect results with m greater than 46340. [6155165]

    • Fixed an issue where cublasLtMatmul() could run an unsupported combination of data types: an FP32-like compute type with FP32 C and D and non-FP32 A and B, in which case A and B are incorrectly interpreted as FP32 matrices. [CUB-9942]

    • Fixed an issue where cublasLtMatmul() returned CUBLAS_STATUS_NOT_SUPPORTED for FP8 Grouped GEMM problems with scale modes CUBLASLT_MATMUL_MATRIX_SCALE_VEC128_32F and CUBLASLT_MATMUL_MATRIX_SCALE_BLK128x128_32F on Hopper GPUs. [CUB-10031]

    • Fixed an issue where cublasLtMatmul() with int8 inputs and scale type CUDA_R_32I would allow non-default epilogues on Blackwell GPUs with compute capability 10.x and return incorrect results. The correct behavior is to disallow all but the default epilogue, as documented. [CUB-10066]

3.1.4. cuBLAS: Release 13.3

  • New Features

    • Enabled memory-parsimonious tiling for FP64 emulated matrix multiplications. This improvement ensures that the workspace memory budget no longer exceeds 8 GB.

    • Added support for CUDA Green contexts.

    • Improved FP4 matrix multiplication performance on Blackwell Ultra GPUs by a geometric mean of 5% across a wide range of problems, with up to 7% speedup for some small problems.

    • Improved TF32 matrix multiplication performance on Blackwell and Blackwell Ultra GPUs by a geometric mean of 27% across a wide range of problems and layouts, with up to 3.5x speedup for some small problems.

    • Improved TF32 TN matrix multiplication performance on Hopper GPUs by a geometric mean of 11% across a wide range of problems, with up to 40% speedup for some small problems.

    • Improved SYMV performance with TMA-based acceleration for Hopper, Blackwell, and Blackwell Ultra kernels with up to 27% geomean speedup.

  • Known Issues

    • Multiple cuBLASLt Grouped GEMM operations reusing the same workspace on Hopper GPUs may lead to hangs or unspecified launch failures. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6456362]

    • cuBLASLt Grouped GEMM operations with CUBLAS_POINTER_MODE_HOST can lead to incorrect results on Hopper GPUs when the number of waves is larger than 2. As a workaround, pass device alpha and beta using CUBLAS_POINTER_MODE_DEVICE. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6681084]

    • Calling cublasLtMatmulAlgoGetHeuristic() while a non-default blocking stream is being captured returns CUBLAS_STATUS_INTERNAL_ERROR. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1). [6288786]

    • GEMV-like operations (e.g., cublas<t>gemv(), or cublasLtMatmul() with M or N equal to 1) may return CUBLAS_STATUS_NOT_SUPPORTED for certain shapes and workspace configurations. This issue was introduced in CUDA Toolkit 13.3 (cuBLAS 13.5.1).

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

3.1.5. cuBLAS: Release 13.2 Update 2

  • Known Issues

    • Multiple cuBLASLt Grouped GEMM operations reusing the same workspace on Hopper GPUs may lead to hangs or unspecified launch failures. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6456362]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

  • Resolved Issues

    • Fixed an issue where cublasLtMatmul() ignored the tensor-wide scaling value specified by CUBLASLT_MATMUL_DESC_D_SCALE_POINTER for NVFP4 matrix multiplications with NVFP4 output, resulting in incorrect results. This affected algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6059292]

3.1.6. cuBLAS: Release 13.2 Update 1

Note

CUDA Toolkit 13.2 Update 1 contains a critical cuBLAS bug for an issue where cublasLtMatmul() could ignore tensor-wide scaling for NVFP4 matrix multiplications, resulting in incorrect results. Please see the cuBLAS patch release notes for an available cuBLAS patch (13.4.1) to resolve this issue.

  • New Features

    • Extended the experimental Grouped GEMM API in cuBLASLt to support NVFP4 inputs and bias epilogues on Blackwell GPUs with Compute Capability 10.x and 11.0. Grouped GEMM NVFP4 support currently uses only MMA tile sizes with K equal to 64.

    • Extended the experimental Grouped GEMM API in cuBLASLt to support BF16, FP16, and FP8 input data types with BF16, FP16, and FP32 output data types on Hopper GPUs. For FP8 inputs, tensorwide scaling and block scaling (VEC128 and BLK128x128) are supported.

    • Improved Grouped GEMM performance on Blackwell GPUs, providing up to 20% higher performance for large problem sizes where the matrices exceed the L2 cache size.

  • Known Issues

    • cublasLtMatmul() ignores the tensor-wide scaling value provided by CUBLASLT_MATMUL_DESC_D_SCALE_POINTER for NVFP4 matrix multiplications with NVFP4 output, leading to incorrect results. This affects algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x. This issue was introduced in CUDA Toolkit 13.2 Update 1. [6059292]

    • Multiple cuBLASLt Grouped GEMM operations reusing the same workspace on Hopper GPUs may lead to hangs or unspecified launch failures. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6456362]

    • cuBLASLt Grouped GEMM operations with CUBLAS_POINTER_MODE_HOST can lead to incorrect results on Hopper GPUs when the number of waves is larger than 2. As a workaround, pass device alpha and beta using CUBLAS_POINTER_MODE_DEVICE. This issue was introduced in CUDA Toolkit 13.2 Update 1 (cuBLAS 13.4.0). [6681084]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

  • Resolved Issues

    • Fixed an issue in cublasLtMatmulAlgoGetHeuristic() that could result in no algorithm candidates being returned for Grouped GEMM on Blackwell GPUs. [CUB-9657]

3.1.7. cuBLAS: Release 13.2

  • New Features

    • Extended the experimental Grouped GEMM API in cuBLASLt to support MXFP8 inputs on GPUs with Compute Capability 10.x and 11.0.

    • Added control over special-case handling in FP32 emulation via the environment variable CUBLAS_EMULATION_SPECIAL_VALUES_SUPPORT_MASK. Setting CUBLAS_EMULATION_SPECIAL_VALUES_SUPPORT_MASK=0 can improve performance for applications that do not require preservation of infinity and NaN values, without requiring code changes. For more information, see the cudaEmulationSpecialValuesSupport_t documentation.

    • Added FP64 fixed-point emulation support to the cublas[D|Z]syrk, cublas[D|Z]syr2k, cublasZherk, and cublasZher2k routines. When the math mode is set to CUBLAS_FP64_EMULATED_FIXEDPOINT_MATH, cuBLAS will automatically use FP64 emulation for sufficiently large SYRK and HERK problems. Current support is limited to GPUs with Compute Capability 10.0.

    • Improved performance on RTX PRO 6000 GPUs, delivering up to 20% speedup for FP8, FP16/BF16, TF32, and INT8 precisions.

    • Improved GEMM performance on DGX Spark systems for MXFP8 and NVFP4 data types in large M and N problem sizes, with up to 3× performance improvement for selected matrix shapes.

  • Known Issues

    • On Blackwell GPUs, FP64 fixed-point emulation kernels may produce incorrect results or experience data corruption when executed concurrently with third-party kernels that allocate tensor memory. [CUB-9633]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

  • Resolved Issues

    • Fixed an issue in cublasLtMatmul that could lead to incorrect results when it ran concurrently with another kernel that uses Tensor Memory. This issue only affected algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with Compute Capability 10.x and 11.x, and existed since cuBLAS 12.8. [5807900]

    • Fixed an issue in cublasLtMatmul that could lead to incorrect results or invalid memory access errors for large leading dimensions, specifically when the product of the data type size and the leading dimension of a matrix exceeded the bounds of a signed 32 bit integer. This issue affected GPUs with Compute Capability 9.0, 10.x, or 11.0, existed since cuBLAS 12.6 Update 2, and only affected algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66. [CUB-9572]

    • Fixed an issue in the cuBLASLt Matmul API that could cause FP8 kernels to hang on GPUs with Compute Capability 9.0 when beta != 0 and scale_C = 0. This issue only affected algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66. [CUB-9627]

    • Fixed an issue in the cuBLASLt Grouped GEMM API that ignored groups with k = 0, leading to incorrect results. This issue existed since CUDA 13.1. [CUB-9529]

    • Fixed an issue in the cuBLASLt Matmul API that could cause incorrect results when C broadcasting was used (LDC = 0). [5845724]

    • Added missing checks for matrix pointer alignment in the cublasLtMatmul API. [CUB-9577] [CUB-9599] [CUB-9585]

    • Fixed an issue in cublasLtMatmul that could lead to incorrect results for NVFP4 precision on B300 and GB300 GPUs when the m dimension was not a multiple of 64. [CUB-9577]

    • Fixed an issue in cublasLtMatmul that could lead to incorrect results for NVFP4 precision on future GPUs, impacting future hardware compatibility. [CUB-9570]

    • Fixed an issue in GEMM and Matmul APIs with BF16 and FP16 inputs on DGX Spark and FP8 inputs on GeForce that could potentially cause illegal memory accesses. [5846563]

    • Fixed an issue in cuBLASLt to enable CUBLASLT_EPILOGUE_BGRADA and CUBLASLT_EPILOGUE_BGRADB epilogues when the C matrix CUBLASLT_MATRIX_LAYOUT_ORDER was set to CUBLASLT_ORDER_ROW. [4617436]

    • Fixed an integer overflow bug in complex, emulated FP64 matrix multiplication. The affected routines include cublasZgemm, cublasZtrsm, cublasGemmEx, and cublasLtMatmul. The overflow occurred when 2*m*n + m exceeded UINT_MAX, where m is the number of rows of op(A) and C, and n is the number of columns of op(B) and C. [5720478]

    • Improved GB200 and B200 performance for MXFP8 and NVFP4 precisions when M and N were less than or equal to 32. [CUB-9646]

3.1.8. cuBLAS: Release 13.1 Update 1

  • Known Issues

    • The cuBLASLt Grouped GEMM API ignores groups with k = 0, which can lead to incorrect results. As a workaround, initialize output matrices D with beta*C for all groups, and then compute Grouped GEMM as D += A*B so the result for groups with k = 0 is computed properly. This issue applies to the experimental cuBLASLt Grouped GEMM API introduced in CUDA 13.1. [CUB-9529]

    • Complex FP64 GEMM routines using fixed-point emulation can produce incorrect results when matrix dimensions are large enough that m*n > 2^31 due to integer overflow in an address calculation. [5720478]

    • cublasLtMatmul() may produce incorrect results when run concurrently with another kernel that uses Tensor Memory. This issue affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x, and has existed since cuBLAS 12.8. [5807900]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • cuBLAS GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

  • Resolved Issues

    • Fixed an issue where fixed point emulation with 7 mantissa bits or less could trigger unspecified launch failures. [5692684]

    • Fixed an issue where cublasLtMatmul with FP8 arguments and CUBLASLT_MATMUL_MATRIX_SCALE_SCALAR_32F scaling mode (default) incorrectly required scaling factor addresses to be 16-byte aligned. This issue existed since cuBLAS 12.9. [5728938]

3.1.9. 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 sm_121 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 using CUBLASLT_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]

    • Added validation for batched matmul to reject invalid configurations where the batch counts differ (Adesc batch count != Bdesc batch count). [5645772]

  • Known Issues

    • The Grouped GEMM cuBLASLt API ignores groups with k = 0, which can lead to incorrect results. As a workaround, initialize each output matrix D with beta * C for all groups before the call, then compute Grouped GEMM as D += A * B so that the result for groups with k = 0 is preserved. This issue applies to the experimental Grouped GEMM cuBLASLt API released in CUDA 13.1. [CUB-9529]

    • cublasLtMatmul() may produce incorrect results when run concurrently with another kernel that uses Tensor Memory. This issue affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x, and has existed since cuBLAS 12.8. [5807900]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • cuBLAS GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

3.1.10. 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]

    • FP8 matmuls may fail to launch on multi-device Blackwell GeForce systems. As a workaround, run a separate process per device. [CUB-9487]

    • cublasLtMatmul() may produce incorrect results when run concurrently with another kernel that uses Tensor Memory. This issue affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x, and has existed since cuBLAS 12.8. [5807900]

    • Strided batched GEMM operations using broadcast operands may perform out-of-bounds memory reads on GPUs with compute capability 12.0 or 12.1 when an input matrix shared across batches (via zero or overlapping strides) is smaller than a few hundred KB. Numerical accuracy is unaffected, but these invalid accesses can trigger compute-sanitizer warnings or, in rare instances, illegal memory access errors. This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [6040940] [5996751]

    • cuBLAS GEMM kernels on GPUs with compute capability 10.x or 12.x may access device alpha and beta pointers before calling cudaGridDependencySynchronize(). This can result in a WAR hazard if the preceding PDL kernel produces alpha and beta values on device after calling cudaTriggerProgrammaticLaunchCompletion(). This issue was introduced in CUDA Toolkit 13.0 Update 2 (cuBLAS 13.1.0). [CUB-10409]

3.1.11. 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]

  • Known Issues

    • cublasLtMatmul() may produce incorrect results when run concurrently with another kernel that uses Tensor Memory. This issue affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x, and has existed since cuBLAS 12.8. [5807900]

3.1.12. 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 for sm_110 GPUs for arm64-sbsa on Linux.

  • Resolved Issues

    • Fixed an issue where some cublasZtrmm() kernels produced incorrect results when M is equal to 1 and side is CUBLAS_SIDE_RIGHT on NVIDIA Ada and Blackwell GeForce-class GPUs. [5452663]

  • 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]

    • Some cublasSsyrkx() kernels produce incorrect results when beta is equal to 0 on Blackwell GPUs. [CUB-8846]

    • cublasLtMatmul() may produce incorrect results when run concurrently with another kernel that uses Tensor Memory. This issue affects only algorithms with CUBLASLT_ALGO_CONFIG_ID equal to 66 on GPUs with compute capability 10.x and 11.x, and has existed since cuBLAS 12.8. [5807900]

  • 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 13.4

  • New Features

    • Added new plan property NVFFT_PLAN_PROPERTY_INT64_BLUESTEIN_ONLY to enforce Bluestein-only kernels.

    • Added new plan property NVFFT_PLAN_PROPERTY_INT64_DISABLE_FMA to disable fused multiply-add contraction when using LTO kernels.

  • Resolved Issues

    • Added alignment checks that prevent unaligned memory access when using user-provided work areas.

    • Fixed a correctness issue in real-side LTO callback kernels for R2C and C2R transforms. This issue was first identified in CUDA Toolkit 13.3 Update 1.

3.2.2. cuFFT: Release 13.3 Update 1

  • Known Issues

    • An issue identified in CUDA 13.3 affects the correctness of real-side LTO callback kernels for R2C and C2R transforms. It affects only even sizes above certain large thresholds (8192 or greater in single precision, or 4096 or greater in double precision) whose length has a largest prime factor of at least 127.

3.2.3. cuFFT: Release 13.3

  • New Features

    • Expanded LTO support to include transform sizes divisible by primes larger than 127, along with increased callback support.

  • Resolved Issues

    • Fixed an issue where cufftXtQueryPlan could result in floating-point exceptions when querying multi-GPU plans that are not single-batch one-dimensional FFTs. [5923044]

3.2.4. cuFFT: Release 13.2

  • New Features

    • Using cuFFT link-time optimized (LTO) kernels now requires NVRTC.

  • Deprecations

    • cufftDebug is deprecated and will be removed in a future release.

3.2.5. 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.

3.2.6. 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.

3.2.7. 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 13.4 Update 1

  • New Features

    • cusolverDnXgesvd now computes singular vectors with a hybrid blocked update by default, in which the Givens rotations of the bidiagonal SVD are computed on the host and applied on the device. The previous classical LASR update remains available as CUSOLVER_ALG_1 via cusolverDnSetAdvOptions. Best performance of the default path is attained with pinned host memory.

    • A two-stage symmetric eigenvalue decomposition is now available for cusolverDnXsyevd, reducing the dense symmetric matrix to tridiagonal form through an intermediate banded form (sytrd_sy2sb + sytrd_sb2st). The default algorithm (CUSOLVER_ALG_0) now selects automatically between the one-stage and two-stage paths based on the problem size, architecture, and math mode; CUSOLVER_ALG_2 forces the two-stage path and CUSOLVER_ALG_1 forces the one-stage path.

  • Resolved Issues

    • Fixed a hang when cuSOLVERDn routines were used inside a CUDA Green Context. The handle cached the physical device SM count at cusolverDnCreate time, so persistent kernels launched more CTAs than the green context could schedule and the spin-wait barriers never completed. [4256378]

    • Fixed an integer overflow in the internal pivot handling of cusolverDnXgetrf and cusolverDnXgetrf64 when the column offset times the leading dimension exceeded the INT32_MAX range, which could cause incorrect results or invalid memory accesses for matrices with large leading dimensions. [5411488]

3.3.2. cuSOLVER: Release 13.4

  • New Features

    • Further reduced device workspace for cusolverDnXgesvd when singular vectors are requested and the input matrix is tall-and-skinny (m > n).

    • Reduced device workspace for cusolverDnXgeev by optimizing the Householder reflector accumulation (ORGHR) stage, enabling computation of larger eigenvalue problems within available GPU memory.

    • Added cusolverDnXsytrf and cusolverDnXsytrf_bufferSize, which provide a 64-bit generic API for computing LDLT and UDUT factorizations in symmetric indefinite linear systems.

    • Added cusolverDnXhetrf and cusolverDnXhetrf_bufferSize, which provide a 64-bit generic API for computing LDLH and UDUH factorizations in Hermitian indefinite linear systems, with support for the CUDA_C_32F and CUDA_C_64F data types.

    • Added cusolverDnXhetrs and cusolverDnXhetrs_bufferSize, which provide a 64-bit generic API for solving linear systems of the form AX = B using the factorization obtained via cusolverDnXhetrf, supporting the CUDA_C_32F and CUDA_C_64F data types.

  • Resolved Issues

    • Fixed an issue where cusolverDnXgetrf silently discarded a NaN value in the input matrix instead of propagating it to the factored output. [5849138]

3.3.3. cuSOLVER: Release 13.3 Update 1

  • New Features

    • Improved cusolverDnXgetrf performance with pivoting for sm_90, sm_100, sm_103, and sm_120.

  • Resolved Issues

    • Fixed an issue where cusolverDn{C/Z}sytrf() and cusolverDn{C/Z}sytrs() with devIpiv == nullptr could treat the complex symmetric input as Hermitian instead of symmetric, which could lead to incorrect results for complex symmetric problems. The documented symmetric factorization and solve behaviors have been restored.

    • Fixed accuracy issues on ill-conditioned and rank-deficient matrices for cusolverDnXgesvdp and cusolverDnXpolar.

3.3.4. cuSOLVER: Release 13.3

  • New Features

    • Improved cusolverDnXgeev performance when computing eigenvectors by moving eigenvector post-processing from the host to the device.

  • Known Issues

    • The cusolverDn{C,Z}sytrf and cusolverDnXsytrs APIs assume that the complex input matrix A is Hermitian instead of symmetric when devIpiv is set to NULL. This issue exists starting with CUDA Toolkit 13.1. [5797471]

3.3.5. cuSOLVER: Release 13.2 Update 1

  • New Features

    • Improved performance of cusolverDnXgeqrf() and cusolverDn<S,D,C,Z>geqrf() on sm_90, sm_100, sm_103, and sm_120 for matrices with m <= 65536.

    • Added the new public 64-bit interface cusolverDnXpolar(), which exposes the QDWH algorithm implementation for polar decomposition in cuSOLVERDn.

    • Added the new public 64-bit interface cusolverDnXstedc(), which computes the eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the divide-and-conquer method.

3.3.6. cuSOLVER: Release 13.2

  • New Features

    • Added FP64 fixed-point emulation support to cuSOLVERDn. The following new APIs are available:

    • cusolverDnSetFixedPointEmulationMantissaControl()

    • cusolverDnGetFixedPointEmulationMantissaControl()

    • cusolverDnSetFixedPointEmulationMaxMantissaBitCount()

    • cusolverDnGetFixedPointEmulationMaxMantissaBitCount()

    • cusolverDnSetFixedPointEmulationMantissaBitOffset()

    • cusolverDnGetFixedPointEmulationMantissaBitOffset()

    • cusolverDnSetEmulationSpecialValuesSupport()

    • cusolverDnGetEmulationSpecialValuesSupport()

    • Added the cusolverDnXsygvd API to support larger problem sizes.

  • Known Issues

    • Starting with CUDA Toolkit 13.1, cusolverDn{C,Z}sytrf and cusolverDnXsytrs assume the complex input matrix A is Hermitian (instead of symmetric) when devIpiv == NULL. [5797471]

3.3.7. cuSOLVER: Release 13.1

  • Resolved Issues

    • Fixed a bug that prevented users from changing the algorithm for cusolverDnXsyevBatched by using cusolverDnSetAdvOptions. [5539844]

3.3.8. 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.

3.3.9. 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 13.4

  • Known Issues

    • Mixed-precision CSR/COO SpMM is not supported in some cases. Refer to the cuSPARSE documentation for details.

  • Resolved Issues

    • Fixed a bug with cached parameters in SpMVOp.

3.4.2. cuSPARSE: Release 13.3 Update 1

  • New Features

    • Improved SpGEAM performance by an average of 40%. [CUSPARSE-3361]

    • Reduced preprocessing time for SpMM ALG3.

  • Known Issues

    • Incorrect result when running BSR SDDMM on a very large matrix.

  • Resolved Issues

    • Fixed a rarely occurring issue in CSC and transposed CSR SpMV. [5975307]

    • Fixed an accuracy issue in mixed-precision SELL SpMV.

    • Fixed an issue with the algorithm configuration cache in SpMVOp.

3.4.3. cuSPARSE: Release 13.3

  • New Features

    • Added support for the CSC format in SpSV and SpSM.

    • Improved CSR SpMV ALG2 performance by an average of 11%.

    • Added the Generic API SpGEAM for sparse matrix-matrix addition.

    • Added SpMVOp ALG1 with reduced preprocessing overhead.

    • Added support for mixed index types in SpMVOp computation for CSR matrices with 64-bit offsets and 32-bit indices.

    • Added support for the FP32 data type in SpMVOp.

    • Avoided recompilation for the same epilogue in SpMVOp.

    • Added mixed-precision support in SpMV for 32-bit input matrices and 64-bit input vectors.

    • Added support for updating matrix values after preprocessing in SpMVOp ALG1.

  • Resolved Issues

    • Fixed a memory leak in SpMVOp when destroy_lrb() was called. [5974043]

3.4.4. cuSPARSE: Release 13.2 Update 1

  • New Features

    • Improved cusparseSpMVOp_createDescr() performance by up to 2.5x.

    • Reduced cusparseSpMVOp_createPlan() planning latency for default epilogues through ahead-of-time compilation, avoiding JIT compilation in this case.

  • Resolved Issues

    • Fixed an issue that caused performance regressions in BSR SpMM for certain block sizes. [5860241]

  • Deprecation

    • Deprecated the SpMMOp and SpGEMMreuse APIs.

3.4.5. cuSPARSE: Release 13.2

  • New Features

    • Improved the runtime of the SpMVOp::buffer_size_estimate API.

3.4.6. cuSPARSE: Release 13.1 Update 1

  • New Features

    • Added a new cusparseSpMVOp_bufferSize API that returns the size of the workspace buffer required for SpMVOp computations. Users provide this buffer when creating cusparseSpMVOpDescr_t, removing internal memory allocations.

    • Improved SpMVOp performance on B200. [CUSPARSE-2931] [CUSPARSE-2932] [CUSPARSE-2933]

  • Resolved Issues

    • Fixed an accuracy issue in mixed-precision CSR/COO SpMM computations. [CUSPARSE-2349]

    • Fixed an issue in CSR SpMM computations when the input dense matrix has a high number of columns. [CUSPARSE-2301]

3.4.7. 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]

3.4.8. 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).

3.4.9. 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 13.4

  • New Features

    • Added new FP8 scaling factor type __nv_fp8_ue5m3 and corresponding conversion operations in cuda_fp8.h. [5482730]

  • Resolved Issues

    • Fixed fp128 nearest integral functions returning off-by-one results in pathological cases. This fix also delivers performance improvements of 2x–8x for fp128 operations on GB200 and up to approximately 200x on RTX 5080, covering conversion, nearest integral, frexp, ilogb, modf, and multiplication. Fixed an underflow in fp128 hypot.

    • Fixed __hmin_nan and __hmax_nan bfloat16 functions ignoring the sign of zero in emulation code paths for devices with compute capability below 8.0 and host CPUs; -0.0 now correctly compares as less than +0.0. [6182113]

    • Fixed __nv_fp8_ue5m3 conversions from long integer types that could produce a result off by one. [6182039]

3.5.2. CUDA Math: Release 13.3

  • Resolved Issues

    • Fixed an issue where silent data corruption could occur when the CUDA Math API __mul24() intrinsic was called with compile-time constant inputs due to undefined behavior from compiler optimizations applied to overflowing signed integer multiplication. This issue was introduced in CUDA Toolkit 11.1 and resolved in CUDA Toolkit 13.3. [5807344]

3.5.3. CUDA Math: Release 13.2 Update 1

  • Known Issues

    • Silent data corruption can occur when the CUDA Math API __mul24() intrinsic is called with compile-time constant inputs. Compiler optimizations applied to overflowing signed integer multiplication can expose the program to undefined behavior. This issue was introduced in CUDA Toolkit 11.1 and will be fixed in a future release. [5807344]

3.5.4. CUDA Math: Release 13.2

  • New Features

    • Accuracy and performance improvements were made to the following libdevice single-precision math functions:

      • expm1f(): up to 20% faster, with minor accuracy improvements.

      • erff(): 5% to 10% faster, with minor accuracy improvements.

    These gains come from algorithmic simplifications, reduced branching, and tighter approximations. [5480287]

  • Resolved Issues

    • ACLE extension support for GCC: ARM64 users, notice the behavior change for __clz and __clzll CUDA Math integer intrinsics: the signatures were updated to match the host compiler’s declarations, relevant for GCC 11.4 onwards. The (un-)signedness of the return type of the intrinsic affects the caller code relying on integer types promotions. [6258270]

3.5.5. 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 13.4 Update 1

  • Resolved Issues

    • Fixed parsing issues that could cause crashes or hangs when decoding malformed JPEG bitstreams.

3.6.2. nvJPEG: Release 13.3 Update 1

  • Resolved Issues

    • Fixed an issue that would cause nvjpegCreate* calls to error out on Orin. [6176492]

3.6.3. nvJPEG: Release 13.3

  • New Features

    • Added support for region-of-interest decoding with nvjpegDecodeBatchedEx when using the NVJPEG_BACKEND_LOSSLESS_JPEG backend.

  • Resolved Issues

    • Fixed an issue with boundary handling when decoding a region of interest with NVJPEG_FLAGS_UPSAMPLING_WITH_INTERPOLATION enabled.

3.6.4. nvJPEG: Release 13.2 Update 2

  • Known Issues

    • nvjpegCreate* calls may error out on Orin due to compatibility issues with the libnvcuvid.so driver library. This issue is fixed in CUDA Toolkit 13.3 Update 1. [6176492]

3.6.5. nvJPEG: Release 13.2 Update 1

  • New Features

    • Added the NVJPEG_OUTPUT_UNCHANGEDI enum value to nvjpegOutputFormat_t for unchanged interleaved output. For chroma subsampling formats other than 4:4:4, chroma values are duplicated so that the chroma and luma dimensions match.

3.6.6. nvJPEG: Release 13.1 Update 1

  • Resolved Issues

    • Reduced nvJPEG encoder initialization time on Thor. [5533951]

3.6.7. 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]

3.6.8. 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].

3.6.9. 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 13.4

  • Resolved Issues

    • Fixed a regression in nppiNV12ToRGB_8u_ColorTwist32f_P2C3R_Ctx introduced in CUDA 12.9 that could render black NV12 frames as blue. [6229084]

3.7.2. NPP: Release 13.1

  • Resolved Issues

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

3.7.3. 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.

  • 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]

4. Notices

4.1. Notice

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 (“NVIDIA”) 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 (“Terms of Sale”). 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 customer’s 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 customer’s 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 customer’s 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, “MATERIALS”) ARE BEING PROVIDED “AS IS.” 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, NVIDIA’s 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. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

4.3. Trademarks

NVIDIA 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.