NVIDIA CUDA Toolkit Release Notes
The Release Notes for the CUDA Toolkit.
1. CUDA 12.2 Release Notes
The release notes for the NVIDIA® CUDA® Toolkit can be found online at https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html.
Note
The release notes have been reorganized into two major sections: the general CUDA release notes, and the CUDA libraries release notes including historical information for 12.x releases.
1.1. CUDA Toolkit Major Component Versions
- CUDA Components
-
Starting with CUDA 11, the various components in the toolkit are versioned independently.
For CUDA 12.2 Update 2, the table below indicates the versions:
Component Name |
Version Information |
Supported Architectures |
Supported Platforms |
|
---|---|---|---|---|
CUDA C++ Core Compute Libraries |
Thrust |
2.1.0 |
x86_64, POWER, aarch64-jetson |
Linux, Windows |
CUB |
2.1.0 |
|||
libcu++ |
2.1.0 |
|||
Cooperative Groups |
12.0.0 |
|||
CUDA Compatibility |
12.2.34086590 |
x86_64, POWER, aarch64-jetson |
Linux, Windows |
|
CUDA Runtime (cudart) |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
cuobjdump |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows |
|
CUPTI |
12.2.142 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuxxfilt (demangler) |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows |
|
CUDA Demo Suite |
12.2.140 |
x86_64 |
Linux, Windows |
|
CUDA GDB |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, WSL |
|
CUDA Nsight Eclipse Plugin |
12.2.144 |
x86_64, POWER |
Linux |
|
CUDA NVCC |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA nvdisasm |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows |
|
CUDA NVML Headers |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA nvprof |
12.2.142 |
x86_64, POWER |
Linux, Windows |
|
CUDA nvprune |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA NVRTC |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
NVTX |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA NVVP |
12.2.142 |
x86_64, POWER |
Linux, Windows |
|
CUDA OpenCL |
12.2.140 |
x86_64 |
Linux, Windows |
|
CUDA Profiler API |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA Compute Sanitizer API |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuBLAS |
12.2.5.6 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuDLA |
12.2.140 |
aarch64-jetson |
Linux |
|
CUDA cuFFT |
11.0.8.103 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuFile |
1.7.2.10 |
x86_64 |
Linux |
|
CUDA cuRAND |
10.3.3.141 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuSOLVER |
11.5.2.141 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA cuSPARSE |
12.1.2.141 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA NPP |
12.2.1.4 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA nvJitLink |
12.2.140 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
CUDA nvJPEG |
12.2.2.4 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
Nsight Compute |
2023.2.2.3 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL (Windows 11) |
|
Nsight Systems |
2023.2.3.1004 |
x86_64, POWER, aarch64-jetson |
Linux, Windows, WSL |
|
Nsight Visual Studio Edition (VSE) |
2023.2.2.23221 |
x86_64 (Windows) |
Windows |
|
nvidia_fs1 |
2.17.5 |
x86_64, aarch64-jetson |
Linux |
|
Visual Studio Integration |
12.2.140 |
x86_64 (Windows) |
Windows |
|
NVIDIA Linux Driver |
535.104.05 |
x86_64, POWER, aarch64-jetson |
Linux |
|
NVIDIA Windows Driver |
537.13 |
x86_64 (Windows) |
Windows, WSL |
- CUDA Driver
-
Running a CUDA application requires the system with at least one CUDA capable GPU and a driver that is compatible with the CUDA Toolkit. See Table 3. For more information various GPU products that are CUDA capable, visit https://developer.nvidia.com/cuda-gpus.
Each release of the CUDA Toolkit requires a minimum version of the CUDA driver. The CUDA driver is backward compatible, meaning that applications compiled against a particular version of the CUDA will continue to work on subsequent (later) driver releases.
More information on compatibility can be found at https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#cuda-compatibility-and-upgrades.
Note: Starting with CUDA 11.0, the toolkit components are individually versioned, and the toolkit itself is versioned as shown in the table below.
The minimum required driver version for CUDA minor version compatibility is shown below. CUDA minor version compatibility is described in detail in https://docs.nvidia.com/deploy/cuda-compatibility/index.html
CUDA Toolkit |
Minimum Required Driver Version for CUDA Minor Version Compatibility* |
|
---|---|---|
Linux x86_64 Driver Version |
Windows x86_64 Driver Version |
|
CUDA 12.2.x |
>=525.60.13 |
>=525.41 |
CUDA 12.1.x |
>=525.60.13 |
>=527.41 |
CUDA 12.0.x |
>=525.60.13 |
>=527.41 |
CUDA 11.8.x |
>=450.80.02 |
>=452.39 |
CUDA 11.7.x |
>=450.80.02 |
>=452.39 |
CUDA 11.6.x |
>=450.80.02 |
>=452.39 |
CUDA 11.5.x |
>=450.80.02 |
>=452.39 |
CUDA 11.4.x |
>=450.80.02 |
>=452.39 |
CUDA 11.3.x |
>=450.80.02 |
>=452.39 |
CUDA 11.2.x |
>=450.80.02 |
>=452.39 |
CUDA 11.1 (11.1.0) |
>=450.80.02 |
>=452.39 |
CUDA 11.0 (11.0.3) |
>=450.36.06** |
>=451.22** |
* Using a Minimum Required Version that is different from Toolkit Driver Version could be allowed in compatibility mode – please read the CUDA Compatibility Guide for details.
** CUDA 11.0 was released with an earlier driver version, but by upgrading to Tesla Recommended Drivers 450.80.02 (Linux) / 452.39 (Windows), minor version compatibility is possible across the CUDA 11.x family of toolkits.
The version of the development NVIDIA GPU Driver packaged in each CUDA Toolkit release is shown below.
CUDA Toolkit |
Toolkit Driver Version |
|
---|---|---|
Linux x86_64 Driver Version |
Windows x86_64 Driver Version |
|
CUDA 12.2 Update 2 |
>=535.104.05 |
>=537.13 |
CUDA 12.2 Update 1 |
>=535.86.09 |
>=536.67 |
CUDA 12.2 GA |
>=535.54.03 |
>=536.25 |
CUDA 12.1 Update 1 |
>=530.30.02 |
>=531.14 |
CUDA 12.1 GA |
>=530.30.02 |
>=531.14 |
CUDA 12.0 Update 1 |
>=525.85.12 |
>=528.33 |
CUDA 12.0 GA |
>=525.60.13 |
>=527.41 |
CUDA 11.8 GA |
>=520.61.05 |
>=520.06 |
CUDA 11.7 Update 1 |
>=515.48.07 |
>=516.31 |
CUDA 11.7 GA |
>=515.43.04 |
>=516.01 |
CUDA 11.6 Update 2 |
>=510.47.03 |
>=511.65 |
CUDA 11.6 Update 1 |
>=510.47.03 |
>=511.65 |
CUDA 11.6 GA |
>=510.39.01 |
>=511.23 |
CUDA 11.5 Update 2 |
>=495.29.05 |
>=496.13 |
CUDA 11.5 Update 1 |
>=495.29.05 |
>=496.13 |
CUDA 11.5 GA |
>=495.29.05 |
>=496.04 |
CUDA 11.4 Update 4 |
>=470.82.01 |
>=472.50 |
CUDA 11.4 Update 3 |
>=470.82.01 |
>=472.50 |
CUDA 11.4 Update 2 |
>=470.57.02 |
>=471.41 |
CUDA 11.4 Update 1 |
>=470.57.02 |
>=471.41 |
CUDA 11.4.0 GA |
>=470.42.01 |
>=471.11 |
CUDA 11.3.1 Update 1 |
>=465.19.01 |
>=465.89 |
CUDA 11.3.0 GA |
>=465.19.01 |
>=465.89 |
CUDA 11.2.2 Update 2 |
>=460.32.03 |
>=461.33 |
CUDA 11.2.1 Update 1 |
>=460.32.03 |
>=461.09 |
CUDA 11.2.0 GA |
>=460.27.03 |
>=460.82 |
CUDA 11.1.1 Update 1 |
>=455.32 |
>=456.81 |
CUDA 11.1 GA |
>=455.23 |
>=456.38 |
CUDA 11.0.3 Update 1 |
>= 450.51.06 |
>= 451.82 |
CUDA 11.0.2 GA |
>= 450.51.05 |
>= 451.48 |
CUDA 11.0.1 RC |
>= 450.36.06 |
>= 451.22 |
CUDA 10.2.89 |
>= 440.33 |
>= 441.22 |
CUDA 10.1 (10.1.105 general release, and updates) |
>= 418.39 |
>= 418.96 |
CUDA 10.0.130 |
>= 410.48 |
>= 411.31 |
CUDA 9.2 (9.2.148 Update 1) |
>= 396.37 |
>= 398.26 |
CUDA 9.2 (9.2.88) |
>= 396.26 |
>= 397.44 |
CUDA 9.1 (9.1.85) |
>= 390.46 |
>= 391.29 |
CUDA 9.0 (9.0.76) |
>= 384.81 |
>= 385.54 |
CUDA 8.0 (8.0.61 GA2) |
>= 375.26 |
>= 376.51 |
CUDA 8.0 (8.0.44) |
>= 367.48 |
>= 369.30 |
CUDA 7.5 (7.5.16) |
>= 352.31 |
>= 353.66 |
CUDA 7.0 (7.0.28) |
>= 346.46 |
>= 347.62 |
For convenience, the NVIDIA driver is installed as part of the CUDA Toolkit installation. Note that this driver is for development purposes and is not recommended for use in production with Tesla GPUs.
For running CUDA applications in production with Tesla GPUs, it is recommended to download the latest driver for Tesla GPUs from the NVIDIA driver downloads site at https://www.nvidia.com/drivers.
During the installation of the CUDA Toolkit, the installation of the NVIDIA driver may be skipped on Windows (when using the interactive or silent installation) or on Linux (by using meta packages).
For more information on customizing the install process on Windows, see https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#install-cuda-software.
For meta packages on Linux, see https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#package-manager-metas.
1.2. New Features
This section lists new general CUDA and CUDA compilers features.
1.2.1. General CUDA
-
This release introduces Heterogeneous Memory Management (HMM), allowing seamless sharing of data between host memory and accelerator devices. HMM is supported on Linux only and requires a recent kernel (6.1.24+ or 6.2.11+).
HMM requires the use of NVIDIA’s GPU Open Kernel Modules driver.
As this is the first release of HMM, some limitations exist:
GPU atomic operations on file-backed memory are not yet supported.
Arm CPUs are not yet supported.
HugeTLBfs pages are not yet supported on HMM (this is an uncommon scenario).
The
fork()
system call is not fully supported yet when attempting to share GPU-accessible memory between parent and child processes.HMM is not yet fully optimized, and may perform slower than programs using
cudaMalloc()
,cudaMallocManaged()
, or other existing CUDA memory management APIs. The performance of programs not using HMM will not be affected.
The Lazy Loading feature (introduced in CUDA 11.7) is now enabled by default on Linux with the 535 driver. To disable this feature on Linux, set the environment variable
CUDA_MODULE_LOADING=EAGER
before launch. Default enablement for Windows will happen in a future CUDA driver release. To enable this feature on Windows, set the environment variableCUDA_MODULE_LOADING=LAZY
before launch.Host NUMA memory allocation: Allocate a CPU memory targeting a specific NUMA node using either the CUDA virtual memory management APIs or the CUDA stream-ordered memory allocator. Applications must ensure device accesses to pointer backed by HOST allocations from these APIs are performed only after they have explicitly requested accessibility for the memory on the accessing device. It is undefined behavior to access these host allocations from a device without accessibility for the address range, regardless of whether the device supports pageable memory access or not.
-
Added per-client priority mapping at runtime for CUDA Multi-Process Service (MPS). This allows multiple processes running under MPS to arbitrate priority at a coarse-grained level between multiple processes without changing the application code.
We introduce a new environment variable
CUDA_MPS_CLIENT_PRIORITY
, which accepts two values: NORMAL priority, 0, and BELOW_NORMAL priority, 1.For example, given two clients, a potential configuration is as follows:
// Client 1’s Environment
export CUDA_MPS_CLIENT_PRIORITY=0
// NORMAL
// Client 2’s Environment
export CUDA_MPS_CLIENT_PRIORITY=1
// BELOW NORMAL
1.2.2. CUDA Compilers
libNVVM samples have been moved out of the toolkit and made publicly available on GitHub as part of the NVIDIA/cuda-samples project. Similarly, the nvvmir-samples have been moved from the nvidia-compiler-sdk project on GitHub to the new location of the libNVVM samples in the NVIDIA/cuda-samples project.
For changes to PTX, refer to https://docs.nvidia.com/cuda/parallel-thread-execution/#ptx-isa-version-8-2.
1.2.3. CUDA Developer Tools
For changes to nvprof and Visual Profiler, see the changelog.
For new features, improvements, and bug fixes in CUPTI, see the changelog.
For new features, improvements, and bug fixes in Nsight Compute, see the changelog.
For new features, improvements, and bug fixes in Compute Sanitizer, see the changelog.
For new features, improvements, and bug fixes in CUDA-GDB, see the changelog.
1.3. Resolved Issues
1.3.1. General CUDA
Resolved potential soft lock-ups around
rm_run_nano_timer_callback()
. A Linux kernel device driver API used for timer management in the Linux kernel interface of the NVIDIA GPU driver was susceptible to a race condition under multi-GPU configurations.Fixed potential GSP-RM hang in
kernel_resolve_address()
.Removed potential GPUDirect RDMA driver crash in
nvidia_p2p_put_pages()
. The legacy non-persistent memory APIs allow third party driver to invokenvidia_p2p_put_pages
with a stalepage_table
pointer, which has already been freed by the RM callback as part of the process shutdown sequence. This behavior was broken when persistent memory support was added to the legacynvidia_p2p
APIs. We resolved the issue by providing new APIs:nvidia_p2p_get/put_pages_persistent
for persistent memory. Thus, the original behavior of the legacy APIs for non-persistent memory is restored. This is essentially a change in the API, so although the nvidia-peermem is updated accordingly, external consumers of persistent memory mapping will need to be changed to use the new dedicated APIs.Resolved an issue in
watchcat
syscall.-
Fixed potential incorrect results in optimized code under high register pressure. NVIDIA has found that under certain rare conditions, a register spilling optimization in PTXAS could result in incorrect compilation results. This issue is fixed for offline compilation (non-JIT) in the CUDA 12.2 release and will be fixed for JIT compilation in the next enterprise driver update.
NVIDIA believes this issue to be extremely rare, and applications relying on JIT that are working successfully should not be affected.
1.4. Deprecated or Dropped Features
Features deprecated in the current release of the CUDA software still work in the current release, but their documentation may have been removed, and they will become officially unsupported in a future release. We recommend that developers employ alternative solutions to these features in their software.
- General CUDA
-
Ubuntu 18.04 support has reached EOL.
- CUDA Tools
-
None.
- CUDA Compiler
-
None.
2. CUDA Libraries
This section covers CUDA Libraries release notes for 12.x releases.
CUDA Math Libraries toolchain uses C++11 features, and a C++11-compatible standard library (libstdc++ >= 20150422) is required on the host.
-
Support for the following compute capabilities is removed for all libraries:
sm_35 (Kepler)
sm_37 (Kepler)
2.1. cuBLAS Library
2.1.1. cuBLAS: Release 12.2 Update 2
-
New Features
cuBLASLt will now attempt to decompose problems that cannot be run by a single gemm kernel. It does this by partitioning the problem into smaller chunks and executing the gemm kernel multiple times. This improves functional coverage for very large m, n, or batch size cases and makes the transition from the cuBLAS API to the cuBLASLt API more reliable.
-
Known Issues
cuBLASLt matmul operations may compute the output incorrectly under the following conditions: the data type of matrices A and B is FP8, the data type of matrices C and D is FP32, FP16, or BF16, the beta value is 1.0, the C and D matrices are the same, the epilogue contains GELU activation function.
2.1.2. cuBLAS: Release 12.2
-
Known Issues
cuBLAS initialization fails on Hopper architecture GPUs when MPS is in use with
CUDA_MPS_ACTIVE_THREAD_PERCENTAGE
set to a value less than 100%. There is currently no workaround for this issue.Some Hopper kernels produce incorrect results for batched matmuls with
CUBLASLT_EPILOGUE_RELU_BIAS
orCUBLASLT_EPILOGUE_GELU_BIAS
and a non-zeroCUBLASLT_MATMUL_DESC_BIAS_BATCH_STRIDE
. The kernels apply the first batch’s bias vector to all batches. This will be fixed in a future release.
2.1.3. cuBLAS: Release 12.1 Update 1
-
New Features
Support for FP8 on NVIDIA Ada GPUs.
Improved performance on NVIDIA L4 Ada GPUs.
Introduced an API that instructs the cuBLASLt library to not use some CPU instructions. This is useful in some rare cases where certain CPU instructions used by cuBLASLt heuristics negatively impact CPU performance. Refer to https://docs.nvidia.com/cuda/cublas/index.html#disabling-cpu-instructions.
-
Known Issues
When creating a matrix layout using the
cublasLtMatrixLayoutCreate()
function, the object pointed at bycublasLtMatrixLayout_t
is smaller thancublasLtMatrixLayoutOpaque_t
(but enough to hold the internal structure). As a result, the object should not be dereferenced or copied explicitly, as this might lead to out of bound accesses. If one needs to serialize the layout or copy it, it is recommended to manually allocate an object of sizesizeof(cublasLtMatrixLayoutOpaque_t)
bytes, and initialize it usingcublasLtMatrixLayoutInit()
function. The same applies tocublasLtMatmulDesc_t
andcublasLtMatrixTransformDesc_t
. The issue will be fixed in future releases by ensuring thatcublasLtMatrixLayoutCreate()
allocates at leastsizeof(cublasLtMatrixLayoutOpaque_t)
bytes.
2.1.4. cuBLAS: Release 12.0 Update 1
-
New Features
Improved performance on NVIDIA H100 SXM and NVIDIA H100 PCIe GPUs.
-
Known Issues
For optimal performance on NVIDIA Hopper architecture, cuBLAS needs to allocate a bigger internal workspace (64 MiB) than on the previous architectures (8 MiB). In the current and previous releases, cuBLAS allocates 256 MiB. This will be addressed in a future release. A possible workaround is to set the
CUBLAS_WORKSPACE_CONFIG
environment variable to :32768:2 when running cuBLAS on NVIDIA Hopper architecture.
-
Resolved Issues
Reduced cuBLAS host-side overheads caused by not using the cublasLt heuristics cache. This began in the CUDA Toolkit 12.0 release.
Added forward compatible single precision complex GEMM that does not require workspace.
2.1.5. cuBLAS: Release 12.0
-
New Features
cublasLtMatmul
now supports FP8 with a non-zero beta.Added
int64
APIs to enable larger problem sizes; refer to 64-bit integer interface.-
Added more Hopper-specific kernels for
cublasLtMatmul
with epilogues:CUBLASLT_EPILOGUE_BGRAD{A,B}
CUBLASLT_EPILOGUE_{RELU,GELU}_AUX
CUBLASLT_EPILOGUE_D{RELU,GELU}
Improved Hopper performance on arm64-sbsa by adding Hopper kernels that were previously supported only on the x86_64 architecture for Windows and Linux.
-
Known Issues
There are no forward compatible kernels for single precision complex gemms that do not require workspace. Support will be added in a later release.
-
Resolved Issues
Fixed an issue on NVIDIA Ampere architecture and newer GPUs where
cublasLtMatmul
with epilogueCUBLASLT_EPILOGUE_BGRAD{A,B}
and a nontrivial reduction scheme (that is, notCUBLASLT_REDUCTION_SCHEME_NONE
) could return incorrect results for the bias gradient.cublasLtMatmul
for gemv-like cases (that is, m or n equals 1) might ignore bias with theCUBLASLT_EPILOGUE_RELU_BIAS
andCUBLASLT_EPILOGUE_BIAS
epilogues.
Deprecations
Disallow including
cublas.h
andcublas_v2.h
in the same translation unit.-
Removed:
CUBLAS_MATMUL_STAGES_16x80
andCUBLAS_MATMUL_STAGES_64x80
fromcublasLtMatmulStages_t
. No kernels utilize these stages anymore.cublasLt3mMode_t
,CUBLASLT_MATMUL_PREF_MATH_MODE_MASK
, andCUBLASLT_MATMUL_PREF_GAUSSIAN_MODE_MASK
fromcublasLtMatmulPreferenceAttributes_t
. Instead, use the corresponding flags fromcublasLtNumericalImplFlags_t
.CUBLASLT_MATMUL_PREF_POINTER_MODE_MASK
,CUBLASLT_MATMUL_PREF_EPILOGUE_MASK
, andCUBLASLT_MATMUL_PREF_SM_COUNT_TARGET
fromcublasLtMatmulPreferenceAttributes_t
. The corresponding parameters are taken directly fromcublasLtMatmulDesc_t
.CUBLASLT_POINTER_MODE_MASK_NO_FILTERING
fromcublasLtPointerModeMask_t
. This mask was only applicable toCUBLASLT_MATMUL_PREF_MATH_MODE_MASK
which was removed.
2.2. cuFFT Library
2.2.1. cuFFT: Release 12.2
-
New Features
cufftSetStream
can be used in multi-GPU plans with a stream from any GPU context, instead of from the primary context of the first GPU listed incufftXtSetGPUs
.Improved performance of 1000+ of FFTs of sizes ranging from 62 to 16380. The improved performance spans hundreds of single precision and double precision cases for FFTs with contiguous data layout, across multiple GPU architectures (from Maxwell to Hopper GPUs) via PTX JIT.
Reduced the size of the static libraries when compared to cuFFT in the 12.1 release.
-
Resolved Issues
cuFFT no longer exhibits a race condition when threads simultaneously create and access plans with more than 1023 plans alive.
cuFFT no longer exhibits a race condition when multiple threads call
cufftXtSetGPUs
concurrently.
2.2.2. cuFFT: Release 12.1 Update 1
-
Known Issues
cuFFT exhibits a race condition when one thread calls
cufftCreate
(orcufftDestroy
) and another thread calls any API (exceptcufftCreate
orcufftDestroy
), and when the total number of plans alive exceeds 1023.cuFFT exhibits a race condition when multiple threads call
cufftXtSetGPUs
concurrently on different plans.
2.2.3. cuFFT: Release 12.1
-
New Features
Improved performance on Hopper GPUs for hundreds of FFTs of sizes ranging from 14 to 28800. The improved performance spans over 542 cases across single and double precision for FFTs with contiguous data layout.
-
Known Issues
Starting from CUDA 11.8, CUDA Graphs are no longer supported for callback routines that load data in out-of-place mode transforms. An upcoming release will update the cuFFT callback implementation, removing this limitation. cuFFT deprecated callback functionality based on separate compiled device code in cuFFT 11.4.
-
Resolved Issues
cuFFT no longer produces errors with compute-sanitizer at program exit if the CUDA context used at plan creation was destroyed prior to program exit.
2.2.4. cuFFT: Release 12.0 Update 1
-
Resolved Issues
Scratch space requirements for multi-GPU, single-batch, 1D FFTs were reduced.
2.2.5. cuFFT: Release 12.0
-
New Features
PTX JIT kernel compilation allowed the addition of many new accelerated cases for Maxwell, Pascal, Volta and Turing architectures.
-
Known Issues
cuFFT plan generation time increases due to PTX JIT compiling. Refer to Plan Initialization TIme.
-
Resolved Issues
cuFFT plans had an unintentional small memory overhead (of a few kB) per plan. This is resolved.
2.3. cuSOLVER Library
2.3.1. cuSOLVER: Release 12.2 Update 2
-
Resolved Issues
Fixed an issue with
cusolverDn<t>gesvd()
,cusolverDnGesvd()
, andcusolverDnXgesvd()
, which could cause wrong results for matrices larger than 18918 ifjobu
orjobvt
was unequal to ‘N
’.
2.3.2. cuSOLVER: Release 12.2
-
New Features
A new API to ensure deterministic results or allow non-deterministic results for improved performance. See
cusolverDnSetDeterministicMode()
andcusolverDnGetDeterministicMode()
. Affected functions are:cusolverDn<t>geqrf()
,cusolverDn<t>syevd()
,cusolverDn<t>syevdx()
,cusolverDn<t>gesvdj()
,cusolverDnXgeqrf()
,cusolverDnXsyevd()
,cusolverDnXsyevdx()
,cusolverDnXgesvdr()
, andcusolverDnXgesvdp()
.
-
Known Issues
Concurrent executions of
cusolverDn<t>getrf()
orcusolverDnXgetrf()
in different non-blocking CUDA streams on the same device might result in a deadlock.
2.4. cuSPARSE Library
2.4.1. cuSPARSE: Release 12.2 Update 1
-
New Features
The library now provides the opportunity to dump sparse matrices to files during the creation of the descriptor for debugging purposes. See logging API https://docs.nvidia.com/cuda/cusparse/index.html#cusparse-logging-api.
-
Resolved Issues
Removed
CUSPARSE_SPMM_CSR_ALG3
fallback to avoid confusion in the algorithm selection process.Clarified the supported operations for
cusparseSDDMM()
.cusparseCreateConstSlicedEll()
now usesconst
pointers.Fixed wrong results in rare edge cases of
cusparseCsr2CscEx2()
with base 1 indexing.cusparseSpSM_bufferSize()
could ask slightly less memory than needed.cusparseSpMV()
now checks the validity of the buffer pointer only when it is strictly needed.
-
Deprecations
Several legacy APIs have been officially deprecated. A compile-time warning has been added to all of them.
2.4.2. cuSPARSE: Release 12.1 Update 1
-
New Features
Introduced Block Sparse Row (BSR) sparse matrix storage for the Generic APIs with support for SDDMM routine (
cusparseSDDMM
).Introduced Sliced Ellpack (SELL) sparse matrix storage format for the Generic APIs with support for sparse matrix-vector multiplication (
cusparseSpMV
) and triangular solver with a single right-hand side (cusparseSpSV
).Added a new API call (
cusparseSpSV_updateMatrix
) to update matrix values and/or the matrix diagonal in the sparse triangular solver with a single right-hand side after the analysis step.
2.4.3. cuSPARSE: Release 12.0 Update 1
-
New Features
cusparseSDDMM()
now supports mixed precision computation.Improved
cusparseSpMM()
alg2 mixed-precision performance on some matrices on NVIDIA Ampere architecture GPUs.Improved
cusparseSpMV()
performance with a new load balancing algorithm.cusparseSpSV()
andcusparseSpSM()
now support in-place computation, namely the output and input vectors/matrices have the same memory address.
-
Resolved Issues
cusparseSpSM()
could produce wrong results if the leading dimension (ld) of the RHS matrix is greater than the number of columns/rows.
2.4.4. cuSPARSE: Release 12.0
-
New Features
JIT LTO functionalities (
cusparseSpMMOp()
) switched from driver to nvJitLto library. Starting from CUDA 12.0 the user needs to link tolibnvJitLto.so
, see cuSPARSE documentation. JIT LTO performance has also been improved forcusparseSpMMOpPlan()
.Introduced const descriptors for the Generic APIs, for example,
cusparseConstSpVecGet()
. Now the Generic APIs interface clearly declares when a descriptor and its data are modified by the cuSPARSE functions.Added two new algorithms to
cusparseSpGEMM()
with lower memory utilization. The first algorithm computes a strict bound on the number of intermediate product, while the second one allows partitioning the computation in chunks.Added
int8_t
support tocusparseGather()
,cusparseScatter()
, andcusparseCsr2cscEx2()
.Improved
cusparseSpSV()
performance for both the analysis and the solving phases.Improved
cusparseSpSM()
performance for both the analysis and the solving phases.Improved
cusparseSDDMM()
performance and added support for batch computation.Improved
cusparseCsr2cscEx2()
performance.
-
Resolved Issues
cusparseSpSV()
andcusparseSpSM()
could produce wrong results.cusparseDnMatGetStridedBatch()
did not acceptbatchStride == 0
.
-
Deprecations
Removed deprecated CUDA 11.x APIs, enumerators, and descriptors.
2.5. Math Library
2.5.1. CUDA Math: Release 12.2
-
New Features
CUDA Math APIs for
__half
and__nv_bfloat16
types received usability improvements, including host side <emulated> support for many of the arithmetic operations and conversions.-
__half
and__nv_bfloat16
types have implicit conversions to/from integral types, which are now available with host compilers by default. These may cause build issues due to ambiguous overloads resolution. Users are advised to update their code to select proper overloads. To opt-out user may want to define the following macros (these macros will be removed in the future CUDA release):__CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__
__CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__
Resolved Issues
During ongoing testing, NVIDIA identified that due to an algorithm error the results of 64-bit floating-point division in default round-to-nearest-even mode could produce spurious overflow to infinity. NVIDIA recommends that all developers requiring strict IEEE754 compliance update to CUDA Toolkit 12.2 or newer. The affected algorithm was present in both offline compilation as well as just-in-time (JIT) compilation. As JIT compilation is handled by the driver, NVIDIA recommends updating to driver version greater than or equal to R535 (R536 on Windows) when IEEE754 compliance is required and when using JIT. This is a software algorithm fix and is not tied to specific hardware.
Updated the observed worst case error bounds for single precision intrinsic functions
__expf()
,__exp10f()
and double precision functionsasinh()
,acosh()
.
2.5.2. CUDA Math: Release 12.1
-
New Features
Performance and accuracy improvements in
atanf
,acosf
,asinf
,sinpif
,cospif
,powf
,erff
, andtgammaf
.
2.5.3. CUDA Math: Release 12.0
-
New Features
Introduced new integer/fp16/bf16 CUDA Math APIs to help expose performance benefits of new DPX instructions. Refer to https://docs.nvidia.com/cuda/cuda-math-api/index.html.
Known Issues
Double precision inputs that cause the double precision division algorithm in the default ‘round to nearest even mode’ produce spurious overflow: an infinite result is delivered where
DBL_MAX 0x7FEF_FFFF_FFFF_FFFF
is expected. Affected CUDA Math APIs:__ddiv_rn()
. Affected CUDA language operation: double precision / operation in the device code.
-
Deprecations
All previously deprecated undocumented APIs are removed from CUDA 12.0.
2.6. NVIDIA Performance Primitives (NPP)
2.6.1. NPP: Release 12.0
-
Deprecations
Deprecating non-CTX API support from next release.
-
Resolved Issues
A performance issue with the NPP
ResizeSqrPixel
API is now fixed and shows improved performance.
2.7. nvJPEG Library
2.7.1. nvJPEG: Release 12.2
-
New Features
Added support for JPEG Lossless decode (process 14, FO prediction).
nvJPEG is now supported on L4T.
2.7.2. nvJPEG: Release 12.0
-
New Features
Immproved the GPU Memory optimisation for the nvJPEG codec.
-
Resolved Issues
An issue that causes runtime failures when
nvJPEGDecMultipleInstances
was tested with a large number of threads is resolved.An issue with CMYK four component color conversion is now resolved.
-
Known Issues
Backend
NVJPEG_BACKEND_GPU_HYBRID
- Unable to handle bistreams with extra scans lengths.
-
Deprecations
The reuse of Huffman table in Encoder (
nvjpegEncoderParamsCopyHuffmanTables
).
- 1
-
Only available on select Linux distros
3. Notices
3.1. Notice
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3.2. OpenCL
OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.
3.3. Trademarks
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