• For EfficientNet when run using NHWC FP16 Tenor Core configurations on V100 and A100 GPU architectures.
• For PilotNet, AH-Net, MobileNet V3 on V100 and A100 GPU architectures.
• For various 3-D convolution cases on RTX 8000.

When calling cudnnConvolutionBiasActivationForward() with INT8x4 or INT8x32 IO tensors, it could result in CUDNN_STATUS_BAD_PARAM in 8.0.4. This issue has been fixed in this release.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN 8.0.4 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN 8.0.3 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

Occasionally, inaccurate results were observed in outputs of the cudnnRNNBackwardWeights() and cudnnRNNBackwardWeightsEx() functions when the RNN cell type was GRU and the NVIDIA Ampere GPU architecture was used with FP32 I/O and mathType of CUDNN_DEFAULT_MATH or CUDNN_TENSOR_OP_MATH. Users may switch to CUDNN_FMA_MATH as a temporary workaround. This issue is being investigated.

cudnnRNN*() with LSTM mode may produce inaccurate results on the cy outputs when clipping is enabled on all GPUs. This issue exists in previous cuDNN releases as well.

On Volta and Pascal architectures, performance regressions may be present for various TRUE_HALF convolutions.

Some ResNet-50 and SSD mixed precision inference use-cases may have performance regressions compared to cuDNN 7.6 on V100. V-Net 3D models might have performance regressions on Turing based architectures.

When using cudnnRNN*Ex() APIs, if the user used CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED or CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED as the layout of the RNN data descriptors, and if the batch size is larger than 6144 on Volta or NVIDIA Ampere A100 GPUs, or larger than 4096 on Turing GPUs, CUDNN_STATUS_EXECUTION_FAILED would be returned.

Documentation of the Backend API is not complete. The CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR and CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR descriptor types will be documented in a future release.

The conv_sample_v8.0 sample is not included in the Debian and RPM packages. This will be fixed in a future release.

The libfreeimage.a library in the RHEL 8 ppc64le RPM is for the wrong architecture. This will be fixed in a future release.

When the user is upgrading from cuDNN 8.0.2 to 8.0.3 through the Debian or RPM package, before installing libcudnn8-samples_*.deb/rpm, users should manually uninstall the old libcudnn8-doc package, otherwise a file conflict may happen.

Samples must be installed in a writable location, otherwise the samples can crash.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN 8.0.2 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

The tensor pointers and the filter pointers require at a minimum 4-byte alignment, including INT8 data in the cuDNN library.

Some computational options in cuDNN 8.0.2 now require increased alignment on tensors in order to run performantly. As always, cuDNN recommends users to align tensors to 128-bit boundaries which will be sufficiently aligned for any computational option in cuDNN. Doing otherwise may cause performance regressions in cuDNN 8.0.2 compared to cuDNN v7.6.

For certain algorithms when the computation is in float (32-bit float) and the output is in FP16 (half float), there are cases where the numerical accuracy between the different algorithms might differ. cuDNN 8.0.2 users can target the backend API to query the numerical notes of the algorithms to get the information programmatically. There are cases where algo0 and algo1 will have a reduced precision accumulation when users target the legacy API. In all cases, these numerical differences are not known to affect training accuracy even though they might show up in unit tests.

For the _ALGO_0 algorithm of convolution backward data and backward filter, grouped convolution with groups larger than 1 and with odd product of dimensions C, D (if 3D convolution), H, and W is not supported on devices older than Volta. To prevent a potential illegal memory access by an instruction that only has a 16-bit version in Volta and above, pad at least one of the dimensions to an even value.

On K80 GPUs when cudnnConvolutionForward() is used with CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algorithm and half input/output data types a silent error might occur when the output width Q is 1 and both height and width padding are zero.

In INT8x32 Tensor Core cases, the parameters supported by cuDNN v7.6 are limited to W >= (R-1) * dilationW && H >= (S-1) * dilationH, whereas, in cuDNN v8.0.x, W == (R-1) * dilationW || H == (S-1) * dilationH cases are no longer supported.

In prior versions of cuDNN, some convolution algorithms can use texture-based load instructure for performance improvements particularly in older hardware architectures. Users can opt-out of using texture using the environmental variable CUDNN_TEXOFF_DBG. In cuDNN 8.x, this variable is removed. Texture loading is turned off by default. Users who wish to continue to use texture-based load, can adapt the new backend API and toggle the engine knob CUDNN_KNOB_TYPE_USE_TEX to 1 for engines that support texture-based load instructions.

The implementation of cuDNNLRNCrossChannelBackward() for even-sized normalization windows was incorrect in all previous releases. This issue has been fixed in this release.

There isn’t a dedicated API to query the supported or the most performant algo for cudnnConvolutionBiasActivationForward() in cuDNN. It is not recommended to query w via cudnnGetConvolutionForwardAlgorithm_v7. Instead, we recommend using the cuDNN version 8 backend API. The number of supported engines can be queried using enum CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT from an operation graph descriptor via cudnnBackendGetAttribute().

A memcheck error may have occurred on cuDNN version 7.x builds when calling cudnnConvolutionBackwardFilter () on Volta or Turing GPUs. This issue has been fixed in this release.

Various convolutions which exhibited sub-optimal performance on GA100 GPU are now achieving ideal performance. (not applicable for Jetson platforms)

cudnnCnnTrainVersionCheck() and cudnnCnnInferVersionCheck() were missing in past releases. This issue has been fixed in this release.

Documentation of RNN new APIs and deprecations is not complete. The cudnnRNNBackwardData_v8() and cudnnRNNBackwardWeights_v8() have been added to this release.

cuDNN 8.0.1 built with Windows and CUDA 11.0 RC had reduced performance on 2D, 3D, and grouped convolutions compared to Linux. This issue has been fixed in this release. (not applicable for Jetson platforms)

There was a known issue in cuDNN 8.0.1 when linking statically to cuDNN and using the library's 3D algo1 backward filter convolutions. Users would see the library emit an internal error or incorrectly state that a shared library was missing. This issue has been fixed in this release.

When using an RPM file on RedHat for installation, upgrading from cuDNN v7 to cuDNN v8 directly or indirectly via TensorRT 7.1.3 would cause installation errors. This issue has been fixed in this release.

The implementation of cuDNNLRNCrossChannelBackward was inconsistent with the implementation of cuDNNLRNCrossChannelForward and returned incorrect results when the normalization window was even. This issue has been fixed in this release.

RNN APIs in cuDNN v8.0.1, compiled with CUDA 11.0, used an incorrect default down-conversion on GPUs with CUDA SM version SM80 (NVIDIA Ampere GPU family) when supplied input data and weights have the CUDNN_DATA_FLOAT type and cudnnMathType_t set via cudnnSetRNNMatrixMathType() is CUDNN_DEFAULT_MATH or CUDNN_TENSOR_OP_MATH. Instead of using the default TF32 computation when Tensor Cores are used, a down-conversion to FP16 (half-precision) was performed; same as in the CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION mode. This introduced a lower dynamic range of intermediate data but possibly faster execution. To disable the automatic down-conversion of CUDNN_DATA_FLOAT weights and data in RNN APIs, the user needed to set the environmental variable NVIDIA_TF32_OVERRIDE to 0 (notice this would have disabled the use of TF32 in the entire library, which might have a performance impact on CNNs that are not affected by this issue). Another workaround was to assign the CUDNN_FMA_MATH mode to the cudnnMathType_t argument in cudnnSetRNNMatrixMathType(). Due to this, the A100 GPU TF32 feature was not accessible for RNNs in cuDNN v8.0.1. This issue has been fixed in this release. (not applicable for Jetson platforms)

cuDNN convolution APIs may return CUDNN_STATUS_EXECUTION_FAILED when the number of input or output channels equals to or exceeds 2097152. This issue exists for all cuDNN 8.0.x releases. This issue has been fixed in this release.

Since version 8.0.0 Preview, cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter() erroneously returned CUDNN_STATUS_INTERNAL_ERROR when the workspace size argument value was less than the required workspace size as returned by their respective cudnnGetWorkspace() API. This issue has been fixed and CUDNN_STATUS_BAD_PARAMS is returned as documented.

In this release, the performance of cudnnConvolutionBiasActivationForward() for true-half use cases on Pascal, INT8x4 use cases on Volta, and Turing, compared to version 7.6 is still lower. In addition, FP32 and pseudo-FP16 performance on Volta, Turing and the NVIDIA Ampere GPU architecture is still not fully optimized.

The new RNN APIs: cudnnRNNForward(), cudnnRNNBackwardData_v8(), and cudnnRNNBackwardWeights_v8() are available as a preview in the cuDNN 8.0.2 release.

Occasionally, inaccurate results were observed in outputs of the cudnnRNNBackwardWeights() and cudnnRNNBackwardWeightsEx() functions when the RNN cell type was GRU and the NVIDIA Ampere GPU architecture was used with FP32 I/O and mathType of CUDNN_DEFAULT_MATH or CUDNN_TENSOR_OP_MATH. Users may switch to CUDNN_FMA_MATH as a temporary workaround. This issue is being investigated.

cudnnRNN*() with LSTM mode may produce inaccurate results on the cy outputs when clipping is enabled on all GPUs. This issue exists in previous cuDNN releases as well.

On Volta and Pascal architectures, performance regressions may be present for TRUE_HALF convolution backward filter.

When using cudnnRNN*Ex() APIs, if the user uses CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED or CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED as the layout of the RNN data descriptors, and if the batch size is larger than 6144 on Volta or NVIDIA Ampere A100 GPUs, or larger than 4096 on Turing GPUs, CUDNN_STATUS_EXECUTION_FAILED may be returned.

Currently, there are libcudnn_ops/cnn/adv_infer/train_static.a binaries in the cuDNN Debian and tgz packages. Users are advised not to link against those and link against libcudnn_static.a instead. Those binaries will be removed from the release packages in the next release.

When using cudnnRNN*Ex() APIs, if the user plans to use CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED or CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED as the layout of the RNN data descriptors, the user should call cudnnSetRNNPaddingMode() to set the mode to CUDNN_RNN_PADDED_IO_ENABLED after initializing an RNNDescriptor but before calling cudnnGetRNNWorkspaceSize(). Not doing this may result in CUDNN_STATUS_EXECUTION_FAILED.

Fused convolution-scale-bias-activation with per-channel α1 and α2 scaling gives incorrect results when the reorder type in the convolution descriptor is set to CUDNN_NO_REORDER.

When the user is using cudnnRNN* APIs with the problem sizes (input size, hidden size) being not multiples of 16 for FP16 tensors or multiples of 8 for FP32 tensors, users may encounter a return status of CUDNN_STATUS_EXECUTION_FAILED.

For some 3D spatial non-Tensor-Core convolutions on Maxwell, Pascal, Volta, and Turing architectures, cudnnBackwardFilter() can return incorrect results when the convolution width padding exceeds the value (filterWidth - 1)/2. Likewise, users of cudnnBackendExecute() can experience the same issue when using the engine with CUDNN_ATTR_ENGINE_GLOBAL_INDEX 32 for backward filter. The issue affecting cudnnBackwardFilter() has been fixed in this release. With cudnnBackendFinalize(), an engine descriptor with CUDNN_ATTR_ENGINE_GLOBAL_INDEX 32 and a backward filter operation that satisfies the above condition will return CUDNN_STATUS_NOT_SUPPORTED.

Samples must be installed in a writable location, otherwise the samples can crash.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN 8.0.1 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

Some data types are not widely supported by all cuDNN API. For example, CUDNN_DATA_INT8x4 is not supported by many functions. In such cases, support is available by using cudnnTransformTensor() to transform the tensors from the desired type to a type supported by the API. For example, a user is able to transform input tensors from CUDNN_DATA_INT8x4 to CUDNN_DATA_INT8, run the desired API and then transform output tensors from CUDNN_DATA_INT8 to CUDNN_DATA_INT8x4. Note that this transformation will incur an extra round trip to memory.

The tensor pointers and the filter pointers require at a minimum 4-byte alignment, including INT8 data in the cuDNN library.

Some computational options in cuDNN 8.0.1 now require increased alignment on tensors in order to run performantly. As always, cuDNN recommends users to align tensors to 128-bit boundaries which will be sufficiently aligned for any computational option in cuDNN. Doing otherwise may cause performance regressions in cuDNN 8.0.1 compared to cuDNN v7.6.

For certain algorithms when the computation is in float (32-bit float) and the output is in FP16 (half float), there are cases where the numerical accuracy between the different algorithms might differ. cuDNN 8.0.1 users can target the backend API to query the numerical notes of the algorithms to get the information programmatically. There are cases where algo0 and algo1 will have a reduced precision accumulation when users target the legacy API. In all cases, these numerical differences are not known to affect training accuracy even though they might show up in unit tests.

For the _ALGO_0 algorithm of convolution backward data and backward filter, grouped convolution with groups larger than 1 and with odd product of dimensions C, D (if 3D convolution), H, and W is not supported on devices older than Volta. To prevent a potential illegal memory access by an instruction that only has a 16-bit version in Volta and above, pad at least one of the dimensions to an even value.

On K80 GPUs when cudnnConvolutionForward() is used with CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algorithm and half input/output data types a silent error might occur when the output width Q is 1 and both height and width padding are zero.

On pre-Volta, there are significant performance issues on convolution layers when the compute type is half.

Sub-optimal performance is present in this release for all INT8 convolutions for all GPUs.

The performance of cudnnConvolutionBiasActivationForward() is slower than v7.6 in most cases. This is being actively worked on and performance optimizations will be available in the upcoming releases.

There are some peer-to-peer documentation links that are broken within the cuDNN API Reference. These links will be fixed in the next release.

cudnnCnnTrainVersionCheck() and cudnnCnnInferVersionCheck() are missing in this release and will be added in the GA release.

Documentation of RNN new APIs and deprecations is not complete. The cudnnRNNBackwardData_v8() and cudnnRNNBackwardWeights_v8() functions will be implemented in the next release.

cuDNN 8.0.1 Preview build with Windows and CUDA 11.0 RC has reduced performance on 2D, 3D, and grouped convolutions compared to Linux.

There is a known issue in cuDNN 8.0.1 when linking statically to cuDNN and using the library's 3D algo1 backward filter convolutions. Users will see the library emit an internal error or incorrectly state that a shared library is missing. This is a bug that will be fixed in a future release.

Steps 1 and 2 are required for the user to be able to switch between v7 and v8 installations. After steps 1 and 2 are performed once, step 3 can be used repeatedly and the user can choose the appropriate cuDNN version to work with. For more information, refer to the Installing From An RPM File and Upgrading From v7 To v8 sections in the cuDNN Installation Guide.

When FFT Tiled aglo (i.e., CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING in forward convolution or CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING for backward data) is used for 3D convolution, an intermittent silent failure might happen due to an incorrect stream used for kernel execution. In some cases, this might be manifested as undefined values seen in the output.

The implementation of cuDNNLRNCrossChannelBackward is inconsistent with the implementation of cuDNNLRNCrossChannelForward and returns incorrect results when the normalization window is even. This will be fixed in a future release.

RNN APIs in cuDNN v8.0.1, compiled with CUDA 11.0, use an incorrect default down-conversion on GPUs with CUDA SM version SM80 (NVIDIA Ampere GPU family) when supplied input data and weights have the CUDNN_DATA_FLOAT type and cudnnMathType_t set via cudnnSetRNNMatrixMathType() is CUDNN_DEFAULT_MATH or CUDNN_TENSOR_OP_MATH. Instead of using the default TF32 computation when Tensor Cores are used, a down-conversion to FP16 (half-precision) is performed; same as in the CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION mode. This introduces a lower dynamic range of intermediate data but possibly faster execution. To disable the automatic down-conversion of CUDNN_DATA_FLOAT weights and data in RNN APIs, set the environmental variable NVIDIA_TF32_OVERRIDE to 0 (notice this will disable the use of TF32 in the entire library, which might have a performance impact on CNNs that are not affected by this issue). Another workaround is to assign the CUDNN_FMA_MATH mode to the cudnnMathType_t argument in cudnnSetRNNMatrixMathType(). Due to this, the A100 TF32 feature is not accessible for RNNs in cuDNN v8.0.1.

cuDNN convolution APIs may return CUDNN_STATUS_EXECUTION_FAILED when the number of input or output channels equals to or exceeds 2097152.

When the user is using cudnnRNN* APIs with the problem sizes (input size, hidden size) being not multiples of 16 for FP16 tensors or multiples of 8 for FP32 tensors, users may encounter a return status of CUDNN_STATUS_EXECUTION_FAILED.

Samples must be installed in a writable location, otherwise the samples can crash.

RNN and multi-head attention API calls may exhibit non-deterministic behavior when the cuDNN 8.0.0 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.

When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the non-deterministic behavior of cuDNN RNN and multi-head attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.

The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 non-adjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multi-stream setups.

Some data types are not widely supported by all cuDNN API. For example, CUDNN_DATA_INT8x4 is not supported by many functions. In such cases, support is available by using cudnnTransformTensor() to transform the tensors from the desired type to a type supported by the API. For example, a user is able to transform input tensors from CUDNN_DATA_INT8x4 to CUDNN_DATA_INT8, run the desired API and then transform output tensors from CUDNN_DATA_INT8 to CUDNN_DATA_INT8x4. Note that this transformation will incur an extra round trip to memory.

The tensor pointers and the filter pointers require at a minimum 4-byte alignment, including INT8 data in the cuDNN library.

Some computational options in cuDNN 8.0.0 now require increased alignment on tensors in order to run performantly. As always, cuDNN recommends users to align tensors to 128-bit boundaries which will be sufficiently aligned for any computational option in cuDNN. Doing otherwise may cause performance regressions in cuDNN 8.0.0 compared to cuDNN v7.6.

For certain algorithms when the computation is in float (32-bit float) and the output is in FP16 (half float), there are cases where the numerical accuracy between the different algorithms might differ. cuDNN 8.0.0 users can target the backend API to query the numerical notes of the algorithms to get the information programmatically. There are cases where algo0 and algo1 will have a reduced precision accumulation when users target the legacy API. In all cases, these numerical differences are not known to affect training accuracy even though they might show up in unit tests.

Support for Ubuntu 14.04 has been deprecated in this release. Upgrade to 16.04 or 18.04 for continued support.

Support for Mac OS X has been deprecated in this release. Operating systems that are currently supported are Linux and Windows.

cuDNN version 8 introduces a new API deprecation policy to enable a faster pace of innovation. A streamlined, two-step, deprecation policy will be used for all API changes starting with cuDNN version 8. For details about this new deprecation policy, see Backward Compatibility And Deprecation Policy in the cuDNN Developer Guide.

Removed and deprecated API changes. For a list of removed and deprecated APIs, see API Changes For cuDNN 8.0.0.

Performance regressions on V100 are observed in this release on SSD inference use cases if not using TensorRT.

There are significant performance regressions on pre-Volta GPUs and some Turing GPUs based on the TU102 architecture. This performance regression is not applicable to T4, JetPack, and Tegra.

Sub-optimal performance is present in this release for all INT8 convolutions for all GPUs.

The performance of cudnnConvolutionBiasActivationForward() is slower than v7.6 in most cases. This is being actively worked on and performance optimizations will be available in the upcoming releases.

On K80 GPUs when cudnnConvolutionForward() is used with CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algorithm and half input/output data types a silent error might occur.

There are some peer-to-peer documentation links that are broken within the cuDNN API Reference. These links will be fixed in the next release.

cudnnCnnTrainVersionCheck() and cudnnCnnInferVersionCheck() are missing in this release and will be added in the GA release.

Documentation of RNN new APIs and deprecations is not complete. The cudnnRNNBackwardData_v8() and cudnnRNNBackwardWeights_v8() functions will be implemented in the next release.

cuDNN 8.0.0 Preview will not work with GA10x NVIDIA Ampere GPU architectures. This will be fixed in the next release.

cuDNN 8.0.0 Preview build with Windows and CUDA 11.0 RC has reduced performance on 2D, 3D, and grouped convolutions compared to Linux.

There is a known issue in cuDNN 8.0.0 when linking statically to cuDNN and using the library's 3D algo1 backward filter convolutions. Users will see the library emit an internal error or incorrectly state that a shared library is missing. This is a bug that will be fixed in a future release.

There is a known issue in cuDNN 8.0.0 when linking statically to cuDNN and using the library's 3D algo1 backward filter convolutions. Users will see the library emit an internal error or incorrectly state that a shared library is missing. This is a bug that will be fixed in a future release.

Steps 1 and 2 are required for the user to be able to switch between v7 and v8 installations. After steps 1 and 2 are performed once, step 3 can be used repeatedly and the user can choose the appropriate cuDNN version to work with. For more information, refer to the Installing From An RPM File and Upgrading From v7 To v8 sections in the cuDNN Installation Guide.

cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter() erroneously returns CUDNN_STATUS_INTERNAL_ERROR when the workspace size argument value is less than the required workspace size as returned by their respective cudnnGetWorkspace() API.

cuDNN convolution APIs may return CUDNN_STATUS_EXECUTION_FAILED when the number of input or output channels equals to or exceeds 2097152.