Information on the EULA for the CUDA drivers is displayed.Please comply with the NVIDIA EULA terms. Notice that a URL for the EULA is included in the Details section. After YaST checks the registration for the system, a list of modules that are installed or available is displayed.Ĭlick on the box to select the NVIDIA Compute Module 15 X86-64.Start Yast and select System Extensions.Note that the NVIDIA Compute Module 15 is currently only available for the SLE HPC 15 product. This module is available for use with all SLE HPC 15 Service Packs. ![]() You can select it at installation time or activate it post installation. To simplify installation of NVIDIA CUDA Toolkit on SUSE Linux Enterprise for High Performance Computing (SLE HPC) 15, we have included a new SUSE Module, NVIDIA Compute Module 15. This Module adds the NVIDIA CUDA network repository to your SLE HPC system. The NVIDIA CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.ĬUDA supports the SUSE Linux operating system distributions (both SUSE Enterprise and OpenSUSE) and NVIDIA provides a repository with the necessary packages to easily install the CUDA Toolkit and NVIDIA drivers on SUSE. To get the full advantage of NVIDIA GPUs, you need to use NVIDIA CUDA, which is a general purpose parallel computing platform and programming model for NVIDIA GPUs. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime. To get the full advantage of NVIDIA GPUs, you need to use the CUDA parallel computing platform and programming toolkit. Heterogeneous Computing, the use of both CPUs and accelerators like graphics processing units (GPUs), has become increasingly more common and GPUs from NVIDIA are the most popular accelerators used today for AI/ML workloads. Speed up fused operations on any CNN architectureĬuDNN is supported on Windows and Linux with Ampere, Turing, Volta, Pascal, Maxwell, and Kepler GPU architectures in data center and mobile GPUs.The High-Performance Computing industry is rapidly embracing the use of AI and ML technology in addition to legacy parallel computing.Arbitrary dimension ordering, striding, and sub-regions for 4d tensors means easy integration into any neural net implementation.Supports FP32, FP16, BF16 and TF32 floating point formats and INT8, and UINT8 integer formats.Optimized kernels for computer vision and speech models including ResNet, ResNext, EfficientNet, EfficientDet, SSD, MaskRCNN, Unet, VNet, BERT, GPT-2, Tacotron2 and WaveGlow.Tensor Core acceleration for all popular convolutions including 2D, 3D, Grouped, Depth-wise separable, and Dilated with NHWC and NCHW inputs and outputs.Read the latest cuDNN release notes for a detailed list of new features and enhancements. Developers can download cuDNN or pull it from framework containers on NGC. ![]() Runtime fusion to compile kernels on the fly with new operators, heuristics and fusionsĬuDNN 8.3 is now available as six smaller libraries, providing granularity when integrating into applications.Optimizations accelerating transformer-based models. ![]() It has been redesigned for ease of use, application integration, and offers greater flexibility to developers. CuDNN 8.3 is optimized for A100 GPUs delivering up to 5x higher performance versus V100 GPUs out of the box and includes new optimizations and APIs for applications such as conversational AI and computer vision.
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