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The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, [18] which supersedes the beta released February 14, 2008. [19] CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most ...
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).
Supported API version TDP (Watts) Comments Core Shader Memory Pixel (GP/s) Texture (GT/s) Size Bandwidth Bus type Bus width Single precision Direct3D OpenGL OpenCL CUDA; GeForce 8100 mGPU [44] 2008 MCP78 TSMC 80 nm Unknown Unknown PCIe 2.0 x16 500 1200 400 (system memory) 8:8:4 2 4 Up to 512 from system memory 6.4 12.8
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
ROCm is free, libre and open-source software (except the GPU firmware blobs [4]), and it is distributed under various licenses. ROCm initially stood for Radeon Open Compute platfor m ; however, due to Open Compute being a registered trademark, ROCm is no longer an acronym — it is simply AMD's open-source stack designed for GPU compute.
Julia is a high-level, general-purpose [17] dynamic programming language, designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling and simulation, most commonly used for numerical analysis and computational science.
The transistor count is the number of transistors in an electronic device (typically on a single substrate or silicon die).It is the most common measure of integrated circuit complexity (although the majority of transistors in modern microprocessors are contained in cache memories, which consist mostly of the same memory cell circuits replicated many times).