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You want a cheap high performance GPU for deep learning? In this blog post I will guide through the choices, so you can find the GPU which is best for you.
gpu.js is a single-file JavaScript library for GPGPU in the browser. gpu.js will automatically compile specially written JavaScript functions into shader language and run them on the GPU using the WebGL API. In the case where WebGL is not available, the functions will still run in regular JavaScript.
Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
Kubernetes-GPU-Guide - This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.
With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed.
provides a software development platform that allows developers to take advantage of a new generation of high performance processors. These new processors, including GPUs, the IBM Cell, and other multi-core processors
contains over 40 chapters and nearly 1000 pages full of the latest GPU programming techniques, and includes hundreds of full-color diagrams and pictures. GPU Gems 3 won the Game Developer Magazine's 2007 Front Line Award. Aailable for free online on the NVIDIA Developer Site
CUDA lets you work with familiar programming concepts while developing software that can run on a GP This is the first of a series of articles to introduce you to the power of CUDA -- through working code -- and to the thought process to help you map applications onto multi-threaded hardware (such as GPUs) to get big performance increases. Of course, not all problems can be mapped efficiently onto multi-threaded hardware, so part of my thought process will be to distinguish what will and what won't work, plus provide a common-sense idea of what might work "well-enough". "CUDA programming" and "GPGPU programming" are not the same (although CUDA runs on GPUs). CUDA permits working with familiar programming concepts while developing software that can run on a GPU. It also avoids the performance overhead of graphics layer APIs by compiling your software directly to the hardware (GPU assembly language, for instance), thereby providing great performance.