gpgpu

I am writing a fragment shader in order to median 9 images together. I have never worked with GLSL before, but it seemed like the right tool for the job, as OpenCL isn't available on iOS and medianing on the CPU is inefficient. Here's what I have so far: uniform sampler2D frames[9]; uniform vec2 wh; void main(void) { vec4 sortedFrameValues[9]; float sortedGrayScaleValues[9]; for (int i = 0; i < 9; i++) { sortedFrameValues[i] = texture2D(frames[i], -gl_FragCoord.xy / wh); sortedGrayScaleValues[i] = dot(sortedFrameValues[i].xyz, vec3(0.299, 0.587, 0.114)); } // TODO: Sort sortedGrayScaleValues

When looking at the name of the performance counters in NVIDIA Fermi architecture (the file Compute_profiler.txt in the doc folder of cuda), I noticed that for L2 cache misses, there are two performance counters, l2_subp0_read_sector_misses and l2_subp1_read_sector_misses. They said that these are for two slices of L2. Why do they have two slices of L2? Is there any relation with the Streaming Multi-processor architecture? What would be the effect of this division to the performance? Thanks I don't think there is any direct relation with the streaming multiprocessor. I just think that slice is

Summary Array [A - B - - - C] in device memory but want [A B C] - what's the quickest way with CUDA C? Context I have an array A of integers on device (GPU) memory. At each iteration, I randomly choose a few elements that are larger than 0 and subtract 1 from them. I maintain a sorted lookup array L of those elements that are equal to 0: Array A: @ iteration i: [0 1 0 3 3 2 0 1 2 3] @ iteration i + 1: [0 0 0 3 2 2 0 1 2 3] Lookup for 0-elements L: @ iteration i: [0 - 2 - - - 6 - - -] -> want compacted form: [0 2 6] @ iteration i + 1: [0 1 2 - - - 6 - - -] -> want compacted form: [0 1 2 6] (

In the documentation for CUDA 6.5 has written: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#ixzz3PIXMTktb 5.2.3. Multiprocessor Level ... 8L for devices of compute capability 3.x since a multiprocessor issues a pair of instructions per warp over one clock cycle for four warps at a time, as mentioned in Compute Capability 3.x. Does this mean that the GPU Kepler CC3.0 processors are not only pipelined architecture, but also superscalar? Pipelining - these two sequences execute in parallel (different operations at one time): LOAD [addr1] -> ADD -> STORE [addr1] -> NOP NOP ->

In slide 25 of this talk by Twitter's Head of Open Source office, the presenter says that Mesos allows one to track and manage even GPU (I assume he meant GPGPU) resources. But I cant find any information on this anywhere else. Can someone please help? Besides Mesos, are there other cluster managers that support GPGPU? Mesos does not yet provide direct support for (GP)GPUs, but does support custom resource types. If you specify --resources="gpu(*):8" when starting the mesos-slave, then this will become part of the resource offer to frameworks, which can launch tasks that claim to use these