gpu

问题 Suppose there is a lot of vector shapes (Bezier curves which determine the boundary of a shape). For example a page full of tiny letters. What is the fastest way to create a bitmap out of it? I once saw a demo several years ago (can't find it now) where some guys used GPU to rasterize the vector art - they were able to zoom in/out of the page in real-time. What is the current state of GPU rendering of Bezier shapes? Is it really fast? Faster than CPU? What are the common and not-so-common

问题 i have the following information: Maximum number of threads per block: 512 Maximum sizes of each dimension of a block: 512 x 512 x 64 does this mean that the maximum number of threads in a 2d thread block is 512x512 which gives me a 262144 threads in every block? if yes, then is it a good practice to have this number of threads in a a kernel of minimum 256 blocks? 回答1: No, it means that the maximum threads per block is 512, You can decide how to lay that out over [1 ... 512] x [1 ... 512] x

问题 When developing an AIR app for mobile application can someone explain to me the differences between using renderMode = GPU vs renderMODE = direct and when you would use either? 回答1: Please feel free to correct me but this is my understanding. There are 2 components in the display pipeline Rendering Compositing There are 3 renderModes possible with Flash/AIR CPU GPU DIRECT With CPU, BOTH Compositing and Rendering are handled by the CPU/software. With GPU, Compositing is handled by the GPU

问题 This question already has answers here : Closed 8 years ago . Possible Duplicate: How to exlude all the devices which have low GPU in Android Manifest? Yesterday, I created a live wallpaper for Android and tried it in my Droid X, Galaxy S, and Fascinate. And I noticed that it runs smootly in Galaxy S and Fasciante but not in Droid X which has lower GPU. So far I can only eliminate a specific devices which has normal-xlarge screen using the code below. But it does not specifically tell whether

问题 A short description of my problem is as follows: I developed a function that calls a CUDA kernel. My function receives a pointer to the host data buffers (input and output of kernel), and has no control over the allocation of these buffers. --> It is possible that the host data was allocated with either of malloc or cudaHostAlloc. My function is not specifically told which allocation method was used. The question is: what is a feasible way for my function to figure out whether the host

问题 Is it possible to decode video using ffmpeg capabilities directly into texture asynchroniously? I need to output the video onto a geometry. There is mpv video player, which can output video directly into framebuffer and uses other close to metal features, but is there minimalistic example, which suitable for embedded devices (OpenGL ES 2.0 or 3.0)? It would be nice if texture won't leave GPU memory during whole the frame time. 回答1: I currently use sws_scale to trim the edges off mpegts

问题 I am a little bit confused of the overall situation when it comes to OpenCL development so I'll just state my current understanding and questions as a list. Please correct me if I'm wrong. I know there are SDKs ("Platforms") by Intel, AMD (and I guess there is also OpenCL support in the Nvidia SDK?) Are there SDKs by other vendors? Will the SDK of one vendor support the devices of another? e.g. Nvidia devices with AMD sdk? I am able to run programs on my Intel CPU using AMD SDK. Is it the way

问题 I am trying to copy the data in a cv::cuda::GpuMat to a uint8_t* variable which is to be used in a kernel. The GpuMat contains an image data of resolution 752x480 and of type CV_8UC1. Below is the sample code: uint8_t *imgPtr; cv::Mat left, downloadedLeft; cv::cuda::GpuMat gpuLeft; left = imread("leftview.jpg", cv::IMREAD_GRAYSCALE); gpuLeft.upload(left); cudaMalloc((void **)&imgPtr, sizeof(uint8_t)*gpuLeft.rows*gpuLeft.cols); cudaMemcpyAsync(imgPtr, gpuLeft.ptr<uint8_t>(), sizeof(uint8_t)

问题 I have trained a network (using GPU) and now I want to run it (for inference) on a CPU. To do so, I use the following code which loads the meta graph and then the parameters of the network. config = tf.ConfigProto( device_count = {'GPU': 0} ) sess = tf.Session(config=config) meta_graph=".../graph-0207-190023.meta" model=".../model.data-00000-of-00001" new_saver = tf.train.import_meta_graph(meta_graph) new_saver.restore(sess, model) Problem is that since the graph has been defined for training

问题 I want to embed a webbrowser in two stages of my javafx program. Using javafx webview does not work for the intended content (360° view generated with Panotour). So I have to search for alternatives. I failed using JCEF; it does not work with javafx (neither with using SwingNode and GLJPanel). JxBrowser looks promising, but is very expensive and closed source. Can someone recommend a opensource(I have to extend the webbrowser)and html5 browser to embed in a javafx view? Nice to have with gpu