Why using .mex created by MATLAB Coder and GPU Coder doesn't give exactly same results?

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JAI PRAKASH 2018-8-24

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https://ww2.mathworks.cn/matlabcentral/answers/416138-why-using-mex-created-by-matlab-coder-and-gpu-coder-doesn-t-give-exactly-same-results

评论： JAI PRAKASH 2018-8-25

I created an image processing function and transformed it into .mex to speed-up my process. As I have an Nvidia GPU cuda 6.1 compute capability, I thought to create .mex with GPU Coder also.

But why output of both .mex file is not exactly same? I passed same input image and other parameters, but I find ouput image of GPU coded mex little more blurry. See below comarison carefully..

Can somebody explain why? I used below command to create GPU coded mex

cfg = coder.gpuConfig('mex');
cfg.GpuConfig.CompilerFlags = '--fmad=false';
cfg.GpuConfig.ComputeCapability = '6.1';
codegen -args {inputImage, otherParameters} -config cfg imProcessFunction

For detailed analysis, I subtracted above images and then I got below image

difference = CPUimage-GPUimage;

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dpb 2018-8-24

编辑：dpb 2018-8-24

My quick search didn't uncover the link Walter shows and while a detailed read will provide a lot of info, very quickly found two points...

"4.5. Differences from x86 NVIDIA GPUs differ from the x86 architecture in that rounding modes are encoded within each floating point instruction instead of dynamically using a floating point control word. Trap handlers for floating point exceptions are not supported. On the GPU there is no status flag to indicate when calculations have overflowed, underflowed, or have involved inexact arithmetic."

Simply different compilers can use different optimization levels or build different execution chain for the same calculation so that order isn't necessarily the same and floating point isn't necessarily exactly commutative so those effects can show up.

"5.1. Mathematical Function Accuracy ... The consequence is that different math libraries cannot be expected to compute exactly the same result for a given input. This applies to GPU programming as well. Functions compiled for the GPU will use the NVIDIA CUDA math library implementation while functions compiled for the CPU will use the host compiler math library implementation (e.g., glibc on Linux). Because these implementations are independent and neither is guaranteed to be correctly rounded, the results will often differ slightly."

As noted, you may be able to do something with the compiler to try to make rounding more nearly the same (providing you can determine what mode TMW is using) but there's probably nothing you can do about any differences in the libraries.

What Walter was suggesting in his first comment was to try to duplicate the GPU code interactively instead of compiled but I don't think that will likely help because the GPU instructions have to run in that environment so whatever is different is well, "just different".

You could look at pieces of the algorithm perhaps and try to isolate particular calculations and maybe eventually isolate which part is the culprit but I would not hold out much hope for "fixing" it.