Integral. Given an input image $pSrc$ and the specified value $nVal$, the pixel value of the integral image $pDst$ at coordinate (i, j) will be computed as. NVIDIA continuously works to improve all of our CUDA libraries. NPP is a particularly large library, with + functions to maintain. We have a realistic goal of. Name, cuda-npp. Version, Summary. Description, CUDA package cuda-npp. Section, base. License, Proprietary. Homepage. Recipe file.

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For this reason it is recommended that cudaDeviceSynchronize or at least cudaStreamSynchronize be called before making an nppSetStream call to change to a new stream ID.

The replacements cannot be found in either CUDA 7. Just for the sake of comparison, I timed my function against NPP. Cudw one this has the benefit that the library will not allocate memory unbeknownst to the user.

The result would be clamped to be A subset of NPP functions performing rounding as part of their functionality do allow the user to specify which rounding mode is used through a parameter of the NppRoundMode type.

These allow to specify filter matrices, which I interpret as a sign of quality improvement and a confession on the poor quality of the ResizeSqrPixel? Sign up or log in Cyda up using Google. I’m not saying it should be removed.

cuda-npp 9.0.252-1

For details please see http: Scratch-buffer memory is unstructured and may be passed to the primitive in uninitialized form. The mirroring operations will be memory bound fuda newer devices are flexible in which types of memory access patterns they will handle efficiently. That is that all cufa arguments in those APIs are device pointers. The buffer size is returned via a host pointer as allocation of the scratch-buffer is performed via CUDA runtime host code.


A naive implementation may be close to optimal on newer devices. My guess here is that it should be 0. Stack Overflow works best with JavaScript enabled.

Libraries typically n;p fewer assumptions so that they are more widely applicable. I don’t know yet how this affects the algorithms, but a first test with the shifts changed to 0.

NVIDIA Performance Primitives

And if the shift was 1. We encourage folks to continue to try and outdo NVIDIA libraries, because overall it advances the state of the art and benefits the computing ecosystem. By clicking “Post Your Answer”, you acknowledge that you have read our updated terms of serviceprivacy policy and cookie policyand that your continued use of the website is subject to these policies.

Since NPP is a C API and therefore does not allow for function overloading for different data-types the NPP naming convention addresses the need to differentiate between different flavors of the same algorithm or primitive function but for various data types.

The following script can be used to detect the issue. Intel have marked the corresponding function and variations as deprecated as of IPP v7. The issue can be observed with CUDA 7. It isn’t hard to beat standard sorting methods, if you know a lot about your data and are willing to bake those assumptions into the code. The same problem could be said of many SW packages that arise from HW companies. With a large library to cdua on a large and growing hardware base, the work to optimize it is never done!

No, there is more than one bug.

NVIDIA Performance Primitives (NPP): Integral

You may be confusing “deprecated” with “removed”. I may have found something. Not all primitives in NPP that perform rounding as part of their functionality allow the user to specify the round-mode used. After getting some info from the Nvidia forums and further reading is this the situation as it presents itself to me: To be safe in all cases however, this may require that you increase the memory allocated for your source image by 1 in both width and height. The initial set of functionality in the library focuses on imaging and video processing and is widely applicable for developers in these areas.


Last modified 2 years ago. You’ll have to complain to Nvidia about that. For best performance the application should first call nppGetStream and only call nppSetStream if the stream ID needs to change, nppSetStream will internally call cudaStreamSynchronize if necessary before changing stream IDs.

cuda – Aren’t NPP functions completely optimized? – Stack Overflow

You can get the memory bandwidth stats for your kernel from the profiler and compare them to the maximum for your device. If I had to guess I’d say there is an optimization going wrong or the scaler could be running into a hardware limitation.

It’s then better to give users a “heads up” by declaring it as deprecated, not to make fuda a secret, and to hope it’s going to change in the future. When the aspect ratio is changed with the size then it behaves as expected again.

I have posted the problem on the Nvidia forums. When you roll your own, you can use all the assumptions specific to your situation to speed things up.

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