gpu

问题 CPU usage too high while running Ruta Script.So I plan to use GPU. Whether I need to do any additional process to run the script in GPU machine. Orelse is there any alternative solution to reduce the CPU usage Sample Script: PACKAGE uima.ruta.example; ENGINE utils.PlainTextAnnotator; TYPESYSTEM utils.PlainTextTypeSystem; WORDLIST EditorMarkerList = 'EditorMarker.txt'; WORDLIST EnglishStopWordList = 'EnglishStopWords.txt'; WORDLIST FirstNameList = 'FirstNames.txt'; WORDLIST

问题 follow up to: keras with tensorflow on GPU machine - some parts are very slow Running mnist_cnn.py (slightly modified - mainly adding logging) from tensorflow 1.4 running was done using a prebuilt docker image: tensorflow/tensorflow:1.4.0-gpu-py3 on a p2.xlarge aws machine (that has a Tesla K80 GPU) performance is good, the 1st batch (which is dominated by the call to _make_train_function) takes about 2 seconds: (see time stamp for begin batch and end batch) 2017-11-19 08:26:26,172 : INFO :

问题 Is there a way to resize an image using GPU (graphic card) that is consumable through a .NET application? I am looking for an extremely performant way to resize images and have heard that the GPU could do it much quicker than CPU (GDI+ using C#). Are there known implementations or sample code using the GPU to resize images that I could consume in .NET? 回答1: Have you thought about using XNA to resize your images? Here you can find out how to use XNA to save image as a png/jpeg to a

问题 I have been trying to set up an Ubuntu environment on my laptop for some time now for CUDA programming. I am currently dual booting Windows 8 and Ubuntu 12.04 and want to install CUDA 5 on Ubuntu. The laptop has a GeForce GT 640M graphics card (See below for full specs). It is an Optimus card. Originally I was dual booting Ubuntu 11.10 and have tried tutorials on both 11.10 and 12.04. I have tried many tutorials of all shapes and sizes, including this tutorial. The installation process shows

问题 I would like to be able to use a feature in PTX 1.3 which is not yet implemented it the C interface. Is there a way to write my own function in PTX and inject into an existing binary? The feature I'm looking for is getting the value of %smid 回答1: The answer: __noinline__ __device__ uint get_smid(void) { uint ret; asm("mov.u32 %0, %smid;" : "=r"(ret) ); return ret; } 来源： https://stackoverflow.com/questions/2983553/cuda-injecting-my-own-ptx-function

问题 I'm using nsight 2.2 , Toolkit 4.2 , latest nvidia driver , I'm using couple gpu's in my computer. Build customize 4.2. I have set "generate GPU ouput" on CUDA's project properties, nsight monitor is on (everything looks great). I set several break points on my global - kernel function . nsight stops at the declaration of the function , but skips over several break points. it's just like nsight decide whether to hit a break point or skip over a break point. The funny thing is that nsight

问题 Does the GeForce 6xx Series GPUS use RISC, CISC or VLIW style instructions? In one source, at http://www.motherboardpoint.com/risc-cisc-t241234.html someone said "GPUs are probably closer to VLIW than to RISC or CISC" . In another source, at http://en.wikipedia.org/wiki/Very_long_instruction_word#implementations it says "both Nvidia and AMD have since moved to RISC architectures in order to improve performance on non-graphics workload" 回答1: AFAIK, Nvidia does not publicly document it's

问题 Does the GeForce 6xx Series GPUS use RISC, CISC or VLIW style instructions? In one source, at http://www.motherboardpoint.com/risc-cisc-t241234.html someone said "GPUs are probably closer to VLIW than to RISC or CISC" . In another source, at http://en.wikipedia.org/wiki/Very_long_instruction_word#implementations it says "both Nvidia and AMD have since moved to RISC architectures in order to improve performance on non-graphics workload" 回答1: AFAIK, Nvidia does not publicly document it's

问题 I am interested in getting TensorFlow to run on Windows, however at present I realize that this is not possible due to some of the dependencies not being usable with Windows, e.g. Bazel. The need arises because as I currently understand it the only way to access the GPU from TensorFlow is via a non-virtual install of Linux. I do realize I can dual boot into a Linux install, but would prefer to avoid that route. To resolve the problem I am in need of the entire dependency chain to build

问题 I am a desktop GL developer, and I am starting to explore the mobile world. To avoid misunderstandings, or welcome-but-trivial replies, I can humbly say that I am pretty well aware of the GL and GL|ES machinery. The short question is: if we are using GL|ES 2.0 in a shared memory architecture, what's the point behind using VBOs against client-side arrays? More in detail: Vertex buffers are raw chunks of memory, the driver cannot in any way optimize anything because the access pattern depends