opencl

I'm probably misusing OpenCV by using it as wrapper to the official OpenCL C++ bindings so that I can launch my own kernels. However, OpenCV does have classes like Program, ProgramSource, Kernel, Queue, etc. that seem to tell me that I can launch my own (even non-image-based) kernels with OpenCV. I am having trouble finding documentation out there for these classes, let alone examples. So, I took a stab at it so far: #include <fstream> #include <iostream> #include "opencv2/opencv.hpp" #include "opencv2/core/ocl.hpp" #define ARRAY_SIZE 128 using namespace std; using namespace cv; int main(int,

一、简介 OpenCL是Khronos集团开发和维护的一个开放的、免版税的异构系统跨平台并行编程标准。它的设计有助于开发人员充分利用现代异构系统中的巨大计算能力，极大地促进跨平台的应用程序开发。 Snapdragon平台上的Qualcomm®Adrenotm GPU系列是最早完全支持OpenCL的移动GPU之一。下图为Heterogeneous系统使用OpenCL框架图： 二、OpenCL On Snapdragon Snapdragon是当今Android操作系统和物联网（IOT）市场中功能最强大、应用最广泛的移动平台之一。Snapdragon移动平台将一流的移动组件集中在一块芯片上，确保基于Snapdragon的设备以极为节能的集成解决方案提供最新的移动用户体验。 Snapdragon是一个多处理器系统，包括多模式调制解调器、CPU、GPU、DSP、定位/GPS、多媒体、电源管理、射频、软件和操作系统优化、内存、连接（Wi-Fi、蓝牙）等组件。 １、 OpenCL在Adreno A3x、A4x和A5x GPUs上完全受支持，并且完全符合OpenCL标准。OpenCL有不同的版本和配置文件，不同的Adreno GPU可能支持不同的OpenCL版本，如下图所示： Adreno GPUs with OpenCL support 除了OpenCL版本和配置文件的不同之外，Adreno

I'm a novice in OpenCL. I have an algorithm which uses templates. It worked well with OpenMP parallelization but now the amount of data has grown and the only way to process it is to rewrite it to use OpenCL. I can easily use MPI to build it for cluster but Tesla-like GPU is much cheaper than cluster :) Is there any way to use C++ templates in OpenCL kernel? Is it possible to somehow expand templates by C++ compiler or some tool and after that use so changed kernel function? EDIT. The idea of a workaround is to somehow generate C99-compatible code from C++ code from the template. I found a

On Nvidia GPUs, when I call clEnqueueNDRange , the program waits for it to finish before continuing. More precisely, I'm calling its equivalent C++ binding, CommandQueue::enqueueNDRange , but this shouldn't make a difference. This only happens on Nvidia hardware (3 Tesla M2090s) remotely; on our office workstations with AMD GPUs, the call is nonblocking and returns immediately. I don't have local Nvidia hardware to test on - we used to, and I remember similar behavior then, too, but it's a bit hazy. This makes spreading the work across multiple GPUs harder. I've tried starting a new thread for

It seems like 2 million floats should be no big deal, only 8MBs of 1GB of GPU RAM. I am able to allocate that much at times and sometimes more than that with no trouble. I get CL_OUT_OF_RESOURCES when I do a clEnqueueReadBuffer, which seems odd. Am I able to sniff out where the trouble really started? OpenCL shouldn't be failing like this at clEnqueueReadBuffer right? It should be when I allocated the data right? Is there some way to get more details than just the error code? It would be cool if I could see how much VRAM was allocated when OpenCL declared CL_OUT_OF_RESOURCES. Eric Towers Not

I'm trying to just get a basic program to work using clCreateProgramWithBinary. This is so I know how to use it rather than a "true" application. I see that one of the parameters is a list of binaries. How exactly would I go about creating a binary to test with? I have some test code which creates a program from source, builds and enqueues it. Is there a binary created at some point during this process which I can feed into clCreateProgramWithBinary? Here is some of my code, just to give an idea of my overall flow. I've omitted comments and error checks for simplicity. program =