Forward Compatibility. Figure 4. Forward Compatibility Upgrade Path. Install the package on the system using the package installer. The cuda-compat package consists of the following files: libcuda. Note: This package only provides the files, and does not configure the system. CUDA Compatibility is installed and the application can now run successfully as shown below. Reading database Preparing to unpack Unpacking cuda-compat Setting up cuda-compat Processing triggers for libc-bin 2.
Copy the three CUDA compatibility upgrade files, listed at the start of this section, into a user- or root-created directory. Use the Right Compat Package CUDA forward compat packages should be used only in the following situations when forward compatibility is required across major releases.
Table 3. Feature Exceptions There are specific features in the CUDA driver that require kernel-mode support and will only work with a newer kernel mode driver. Table 4. Check for Compatibility Support In addition to the CUDA driver and certain compiler components, there are other drivers in the system installation stack for example, OpenCL that remain on the old version.
This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver. This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.
Conclusion The CUDA driver maintains backward compatibility to continue support of applications built on older toolkits. Not having to update the driver for newer CUDA releases can mean that new versions of the software can be made available faster to users without any delays.
This is possible as these libraries and frameworks do not have a direct dependency on the CUDA runtime, compiler or driver. When should users use these features? Across minor release versions of CUDA only. Between kernel driver and user mode CUDA driver. Between libraries or runtimes that link to the CUDA driver. If you want to support newer applications on older drivers within the same major release family. All existing CUDA features from older minor releases work. Users may have to incorporate checks in their application when using new features in the minor release that require a new driver to ensure graceful errors.
Users should use the new PTX static library to rebuild binaries. Refer to the workflow section for more details. Requires administrator involvement Depends on the deployment. Not required. Hardware Generation Compute Capability Driver For example, async copy APIs introduced in To use other CUDA APIs introduced in a minor release that require a new driver , one would have to implement fallbacks or fail gracefully.
This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. Notices 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.
It can improve the overall graphics experience and performance in either games or various engineering software applications, include support for newly developed technologies, add compatibility with newer GPU chipsets, or resolve different problems that might have been encountered. When it comes to applying this release, the installation steps should be a breeze, as each manufacturer tries to make them as easy as possible so that each user can update the GPU on their own and with minimum risks however, check to see if this download supports your graphics chipset.
Therefore, get the package extract it if necessary , run the setup, follow the on-screen instructions for a complete and successful installation, and make sure you reboot the system so that the changes take effect.
Users are encouraged to use the cublasLt APIs for algorithm selection functionality. This issue will be fixed in an upcoming release. Plans for FFTs of certain sizes in single precision including some multiples of sizes, and some large prime numbers could fail on future devices with less than 64 kB of shared memory.
Some T4 FFTs are slower than expected. Issue will be fixed in the next update. Deprecated Features Support for callback functionality using separately compiled device code is deprecated on all GPU architectures.
Callback functionality will continue to be supported for all GPU architectures. Improved performance of certain sizes multiples of large powers of 3, powers of 11 in SM Plans with strides, primes larger than in FFT size decomposition and total size of transform including strides bigger than 32GB produce incorrect results. Large prime factors in size decomposition and real to complex or complex to real FFT type no longer cause cuFFT plan functions to fail. Performance improvements for multi-GPU systems.
The regression was introduced in cuFFT After successfully creating a plan, cuFFT now enforces a lock on the cufftHandle. Subsequent calls to any planning function with the same cufftHandle will fail. Improved performance on multi-gpu cuFFT for certain sizes 1k cube.
Resolved an issue that caused cuFFT to crash when reusing a handle after clearing a callback. Resolved bug introduced in There is a known issue with certain cuFFT plans that causes an assertion in the execution phase of certain plans.
This applies to plans with all of the following characteristics: real input to complex output R2C , in-place, native compatibility mode, certain even transform sizes, and more than one batch. Starting with CUDA The workaround is to pad the matrix A with a diagonal matrix D such that the dimension of [A 0 ; 0 D] is bigger than After the syevj, W 0:n-1 contains the eigenvalues and A 0:n-1,0:n-1 contains the eigenvectors.
This reduces the binary size of libcusolver. However, it breaks backward compatibility. The user has to link libcusolver.
This issue has been fixed in CUDA GETRF returned early without finishing the whole factorization when the matrix was singular. The issue has been fixed in release The hidden memory allocation inside cusolverMG handle is about 30 MB per device.
All routines support unsorted column indices, except where strictly indicated Clarified cusparseSpSV and cusparseSpSM memory management. The new routine supports CSR storage format and mixed-precision computation.
Sparse triangular solver adds support for COO format. For example, one can encode a video at blazing fast speed, without any regard to quality and claim extremely high performance, doubling the performance on GPUs with multiple NVENC engines. But such usage may not be of much use in practical situations.
Therefore, it is important to think of encoding performance at a specific quality. NVIDIA encoding benchmarks use the bitrate savings compared with open source encoders x and x's medium preset output, as a measure of the encoding quality. The performance vs. This requires the application to choose appropriate encoding settings, depending upon the GPU in use. In short, despite the reduction of number of NVENCs from Pascal to Turing, one should be able to achieve equivalent encoding performance per GPU, in most practical use cases by adjusting the encoding settings to normalize the encoding quality.
Depending on the maximum API version supported by driver, the application can launch code at runtime compiled with the appropriate API. For encoder, the answer depends on many factors, some of which include: GPU in use and its clock speed, settings used for encoding i.
It is especially important to note that GPU encoding performance is always tied to the encoding quality, and the performance can vary greatly depending upon the chosen settings.
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