Sample Video Output with Page Transition Effect. This article assumes the reader is familiar with the OpenCL programming model [1]. Recently, Intel announced the release of the Intel® SDK for OpenCL Applications 2013 with certified OpenCL* 1.2 support on 3rd and 4th generation Intel® Core™ processors with Intel® Iris Graphics and Intel® HD Graphics family. uint y = get_global_id(1); if(eType == e1) { Workload 6: includes a two-track composite created by a Mask Generator filter on the top track’s event. Surprisingly, not all of the settings of the lens flare effect showed performance improvement with this approach. float4 color = read image(input, iSampler, coord); int nElems = (int)pFlareInfo->lensElemCount; // Get properties for each element to evaluate Most native functions yield better performance but not all. The kernel with no SLM took about 76.5 milliseconds to process the effect on the image. uint cy = get_global_id(1); The one-kernel solution sped up three of the five settings while decreasing performance of the other two settings. } end of for loop … }, hostCodeFunction(…) { | `– libintelopencl.conf Open the Registry Editor app from the Windows Start Screen. A number of issues were encountered on the OpenCL compiler and runtime which were resolved. The HDG OpenCL capability was first introduced with the Intel 3rd Generation Core Family of Processors codenamed Ivy Bridge. lutR[blow.s0], lutG[blow.s1], lutB[blow.s2]. I point to clDeviceQuery.cpp [github.com] as the test source code that will be trying to run. int cx = get_global_id(0); int cy = get_global_id(1); float4 color = read image(input, iSampler, coord); void lensflare_e2 ( input, output, …, __constant KernelFlareProp* pFlareInfo). Notice that in addition to other parameters, the kernel also has three parameters (global pointers) for lookup tables; these are lutR, lutG, and lutB. It also shows that if the number of elements to draw is less than 10, then the six kernel implementation yields better performance. |– libigc.so This case study proves that use of HDG SLM can in fact speed up the performance of OpenCL kernels that use LUTs. fVal.y * fVal.x * read imagel(input, UnscaledSampler, iCoords + (int2)( 1, 1)); __kernel void PagePeel( inputA, inputB, outDst, …). Intel® HD Graphics 4000 @ 350 MHz Core Clock. __local uchar lutG[256]; + y * (1 - x) * Ti1j0 Common video effects in a commercial application were optimized with OpenCL, and then evaluated on Intel® HD Graphics 4000. }, Figure 2. return pixel; status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &src); Sample Video Output with Lens Flare Effect. The highlighted code shows LUT table data being used, where the code is indexing through the tables to retrieve LUT data. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. } The release kit consisted of seven workloads designed to test different video effects which are accelerated with OpenCL. Highlighted code in Figure 5 shows differences between the two implementations. // write into output float4 color = read image(input, imageSampler, coord); if (within bounds) { Intel® Graphics Performance Analyzers: Support with profiling applications; Intel® Distribution of OpenVINO™ toolkit: Based on the OpenCL standard, this product uses customized layers in a distributed neural network (DNN) to provide inference support; Intel® Media SDK; Intel® FPGA SDK for OpenCL™ Software Technology; OpenCL™ Runtimes (for Intel® Processors, Stand-Alone Version) Even as OpenCL best practices and optimization guidelines suggest to program kernels with as few instructions as possible, there are exceptions to this advice. For now, we’ll examine how lookup tables can manifest as performance bottlenecks and what can be done to prevent bottlenecks. Change ), You are commenting using your Google account. Don’t have an Intel account? int2 coord = (int2)(cx, cy); Specifically target Intel® HD Graphics, Intel® Iris® Graphics, and Intel® Iris® Pro Graphics if available on Intel® Processors. In general, lookup tables should be avoided in OpenCL kernels if at all possible because LUTs preclude a kernel from being compute bound. Partial Host Code for one-kernel and six-kernel Lens Flare Implementation. It is still possible that the one kernel per element solution might perform better even in lens flare effects with less than 10 elements. __kernel void PagePeel( inputA, inputB, outDst, …) // write into output eType = (int)pFlareInfo->newElemProps[i].eType; { float4 aPixel = read image(inputA, UnscaledSampler, coord); float fXSrc = …progress based calculated X coordinate; float fYSrc = …progress based calculated Y coordanate; float4 biPixel = swBilinearInterpolation(inputB, (float2)(fXSrc, fYSrc)); fPropBuf = clCreateBuffer(clContext,..., sizeBuf, &flarePropBuf, …); status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &input); status = clSetKernelArg(kernel, 1, sizeof(cl_mem), &output); clEnqueueNDRangeKernel(cmdQueue, kernel, 2, NULL, global, NULL, 0, NULL, NULL); flarePropBuf = clCreateBuffer(clContext, …., nSizeBuf, &ElemProps, &status); status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &src); status = clSetKernelArg(kernel, 1, sizeof(cl_mem), &dst); clEnqueueNDRangeKernel(cmdQueue, kernel, 2, NULL, global,NULL, 0, NULL, NULL); void lensflare_e1 (input, output, … , __constant KernelFlareProp* pFlareInfo).
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