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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 ...
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
The computations are offloaded to the GPUs through either the low-level or the high-level API introduced with CUDA. CUDA is only available for Nvidia's graphics products. Nvidia OptiX is part of Nvidia GameWorks. OptiX is a high-level, or "to-the-algorithm" API, meaning that it is designed to encapsulate the entire algorithm of which ray ...
4.2 Linux ES 3.0 Linux: 1.2 1.0 Linux: 25.6 1720 No Mobile: Celeron 10x0M Celeron 10x7U Pentium 2117U Pentium 20x0M: 350–1100 HD Graphics 2500 [37] Workstation: Xeon E3-1265LV2: 0152: 650–1150: Yes Desktop: Core i3-32x0 Core i3-32x0T Core i5-3xx0 Core i5-3xx0S Core i5-3xx0T HD Graphics 4000 [29] Core i3-3225 Core i3-3245 Core i5-3475S Core ...
This article documents the version history of the Linux kernel. Each major version – identified by the first two numbers of a release version – is designated one of the following levels of support: Supported until next stable version; Long-term support (LTS); maintained for a few years [1]
[21] glibc 2.0 also had better internationalisation and more in-depth translation, IPv6 capability, 64-bit data access, facilities for multithreaded applications, future version compatibility, and the code was more portable. [22] The last-used version of Linux libc used the internal name libc.so.5.
Monte Carlo tree search (MCTS) is a heuristic search algorithm which expands the search tree based on random sampling of the search space. A version of Monte Carlo tree search commonly used in computer chess is PUCT, Predictor and Upper Confidence bounds applied to Trees. DeepMind's AlphaZero and Leela Chess Zero uses MCTS instead of minimax.