nvidia

I have been reading the programming guide for CUDA and OpenCL, and I cannot figure out what a bank conflict is. They just sort of dive into how to solve the problem without elaborating on the subject itself. Can anybody help me understand it? I have no preference if the help is in the context of CUDA/OpenCL or just bank conflicts in general in computer science. Grizzly For nvidia (and amd for that matter) gpus the local memory is divided into memorybanks. Each bank can only address one dataset at a time, so if a halfwarp tries to load/store data from/to the same bank the access has to be

首先添加PPA源。 打开终端，输入： sudo add-apt-repository ppa:bumblebee/stable 然后输入： sudo apt-get update ==================================================== 开始安装。 在终端输入： sudo apt-get install bumblebee (该命令自动安**umblebee 3) sudo apt-get install bumblebee-nvidia (该命令安装NVIDIA官方驱动) 安装完成，重启！！！ ==================================================== 重启后验证是否成功。 打开终端，输入： lspci |grep -i vga 注意，下面列出了Intel集显和NVIDIA独显的工作情况，NVIDIA显卡信息后面有个“rev ff”，表示独显已经关闭。 再输入： sudo optirun glxgears 这是会跳出一个名为“glxgears”，显示3D齿轮画面的窗口，不要关闭窗口。 打开另一个终端，输入： lspci |grep -i vga 下面又列出Intel集显和NVIDIA独显的工作情况，NVIDIA显卡信息后面显示“rev+数字”的形式，表示独显已经开启，正在工作。 然后

I've actually solved this, but I'm posting it for posterity. I ran into a very odd issue with the DataGridView on my dual-monitor system. The issue manifests itself as an EXTREMELY slow repaint of the control ( like 30 seconds for a full repaint ), but only when it is on one of my screens. When on the other, the repaint speed is fine. I have an Nvidia 8800 GT with the latest non-beta drivers (175. something). Is it a driver bug? I'll leave that up in the air, since I have to live with this particular configuration. (It does not happen on ATI cards, though...) The paint speed has nothing to do

How are threads organized to be executed by a GPU? cibercitizen1 Hardware If a GPU device has, for example, 4 multiprocessing units, and they can run 768 threads each: then at a given moment no more than 4*768 threads will be really running in parallel (if you planned more threads, they will be waiting their turn). Software threads are organized in blocks. A block is executed by a multiprocessing unit. The threads of a block can be indentified (indexed) using 1Dimension(x), 2Dimensions (x,y) or 3Dim indexes (x,y,z) but in any case x y z <= 768 for our example (other restrictions apply to x,y,z

I have been using CUDA for a few weeks, but I have some doubts about the allocation of blocks/warps/thread. I am studying the architecture from a didactic point of view (university project), so reaching peak performance is not my concern. First of all, I would like to understand if I got these facts straight: The programmer writes a kernel, and organize its execution in a grid of thread blocks. Each block is assigned to a Streaming Multiprocessor (SM). Once assigned it cannot migrate to another SM. Each SM splits its own blocks into Warps (currently with a maximum size of 32 threads). All the

I want to measure time inner kernel of GPU, how how to measure it in NVIDIA CUDA? e.g. __global__ void kernelSample() { some code here get start time some code here get stop time some code here } Try this, it measures time between 2 events in milliseconds. cudaEvent_t start, stop; float elapsedTime; cudaEventCreate(&start); cudaEventRecord(start,0); //Do kernel activity here cudaEventCreate(&stop); cudaEventRecord(stop,0); cudaEventSynchronize(stop); cudaEventElapsedTime(&elapsedTime, start,stop); printf("Elapsed time : %f ms\n" ,elapsedTime); You can do something like this: __global__ void