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openCL CL_OUT_OF_RESOURCES issue

Hi,

I'm Trying to convert a code written in Cuda to openCL and run into some trouble. My final goal is to implement the code on an Odroid XU3 board with a Mali T628 GPU.

In order to simplify the transition and save time trying to debug openCL kernels I've taken the following steps:

  1. Implement the code in Cuda and test it on a Nvidia GeForce 760
  2. Implement the code in openCL and test it on a Nvidia GeForce 760
  3. test the openCL code on an Odroid XU3 board with a Mali T628 GPU.

I know that different architectures may have different optimizations but that isn't my main concern for now. I manged to run the openCL code on my Nvidia GPU with no apparent issues but keep getting strange errors when trying to run the code on the Odroid board. I know that different architectures have different handling of exceptions etc. but I'm not sure how to solve those issues.


Since the openCL code works great on my Nvidia I assume that I managed to do the correct transition between thread/blocks -> workItems/workGroups etc. I already fixed several issues that relate to the cl_device_max_work_group_size issue so that can't be the cause.When running the code i'm getting a "CL_OUT_OF_RESOURCES" error.


I've narrowed the cause of the error to 2 lines in the code but not sure to fix those issues.

the error is caused by the following lines in the kernel code attached :

  1. lowestDist[pixelNum] = partialDiffSumTemp; both variables are private variables of the kernel and therefor I don't see any potential issue.
  2. d_disparityLeft[globalMemIdx + TILE_BOUNDARY_WIDTH - WINDOW_RADIUS + 0] = bestDisparity[0]; Here I guess the cause is "OUT_OF_BOUND" but not sure how to debug it since the original code doesn't have any issue.

Is there any tool that can help debugging those issues on the Odroid ? I saw that using "printf" inside the kernel isn't possible. Is there another available command ?


Thanks

Yuval

stereoKernel.cl.zip
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  • Hi lrdxgm,

    What you say is mostly true, however if your kernel is ALU bound, then you will benefit from forcing the local workgroup size to 128 because the extra memory accesses caused by the register spilling will be hidden by the ALU operations and the GPU utilisation will be much better resulting in better performance.

    Hope this makes sense.

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  • Hi lrdxgm,

    What you say is mostly true, however if your kernel is ALU bound, then you will benefit from forcing the local workgroup size to 128 because the extra memory accesses caused by the register spilling will be hidden by the ALU operations and the GPU utilisation will be much better resulting in better performance.

    Hope this makes sense.

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