![]() ![]() Since then roughly 8,000 developers have signed up to use the plugin. We first saw Parallel Nsight last year when Fermi was introduced under the name Nexus, and it finally shipped a few months ago along-side the first Fermi based Tesla cards. In order to accomplish this goal, NVIDIA wrote a plugin for Visual Studio to enable the complete programming and debugging of CUDA and graphics code within Visual Studio – something that wasn’t previously possible – called Parallel Nsight. Reaching these programmers would require extending CUDA programming to the IDE and toolsets they already use, and in the process expand the market by making development more accessible than it was with older toolsets. Furthermore NVIDIA ultimately wants to extend practical GPU programming to the more rank & file programmers, where Microsoft’s Visual Studio is by far and wide the IDE of choice for C and C++. With support for higher level languages comes the need for better programming tools and better debugging tools. However good hardware requires good software, and that’s where our discussion is going today. With the Fermi architecture NVIDIA would have the hardware necessary to take the next step in to GPU computing. The Fermi architecture was a big step towards this goal, providing a GPU much better suited for GPU computing than the previous GT200/G80 thanks in large part to the GPU’s unified address space, ECC support, and support for C++. Specifically, they will be releasing Parallel Nsight 1.5, and version 3.2 of the CUDA Toolkit.Īs we’ve reiterated a number of times now, NVIDIA’s long-term goals require the company to expand their GPU market beyond video cards and in to the High Performance Computing (HPC) space, where the brute force applied by GPUs for gaming purposes can be applied to academic, industrial, and even consumer computing applications. Next week NVIDIA will be releasing the first major update to their GPGPU programming toolchain since the Fermi-based Tesla series launched earlier this year. Support for Windows and Linux with the latest NVIDIA data center and mobile GPUs.Not to be outdone by Intel’s IDF and AMD’s counter-meeting this week, NVIDIA’s GPU Computing group has their own announcement this week ahead of their GPU Technology Conference next week.Support for fusion of memory-limited operations like pointwise and reduction with math-limited operations like convolution and matmul.Support for 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.For access to NVIDIA optimized deep learning framework containers that have cuDNN integrated into frameworks, visit NVIDIA GPU CLOUD to learn more and get started.ĭownload cuDNN Developer Guide Forums Latest Release Notes cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, PyTorch, and TensorFlow. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.ĭeep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. ![]() The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks.
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