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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If it turns out to be with Nvidia then who knows when or if this gets fixed. However by looking at one of Nvidia’s programming samples do they use 0. A naive implementation may be close to optimal on newer devices. To minimize library loading and CUDA runtime startup times it is recommended to use the static library s whenever possible. It’s an upstream bug, and it still gets the job done, just not with the correct scaling type.

Libraries typically make fewer assumptions so that they are more widely applicable. You are right that NPP’s performance is lacking. This convention enables the individual developer to make smart choices about memory management that minimize the number of memory cura.

I’m using CUDA 5. In order to map the maximum value of to in the result, one would specify an integer result scaling factor of 8, i. It may only be the filter will get removed due to this lack of support, for having a low image quality and being bound cyda a specific hardware and an external library.

I don’t know yet how this affects the algorithms, but a first test with the shifts changed to 0. With a large library to support on a large and growing hardware base, the work to optimize it is never done! Not all primitives in NPP that perform rounding as part of their functionality allow the user to specify the round-mode used.


In the meantime, a possible work around would be to increase oSrcROI. This list of sub-libraries is as follows:.

Opened 2 years ago Last modified 2 years ago. According to their documentation: To improve loading and runtime performance when using dynamic libraries, NPP recently replaced it with a full set of nppi sub-libraries.

Calling cudaDeviceSynchronize frequently can kill performance so minimizing the frequency of these calls is critical for good performance. I tested on 4 types of images and 2 different sizes. Some primitives of NPP require additional device memory buffers scratch buffers for calculations, e.

The final result for cura signal value of being squared and scaled would be:. 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. In cases where the results exceed the original range, these functions clamp the result values back to the valid range. What was the difference, in percent?

cuda-npp 9.0.252-1

After getting some info from the Nvidia forums and further reading is this the situation as it presents itself to me: The issue can be observed with CUDA 7. Tunacode in Pakistan has some stuff too. This disambiguation of different flavors of a primitive cuxa done via a suffix containing data type and other disambiguating information. 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.


So far the only response I got was to send in a feature request for Nvidia to provide the new functions, which I’ve done.

Before the results of an operation are clamped to the valid output-data range by multiplying them with. Cuuda don’t see a reason to deprecate it. Where the algorithms produced identical output for all 50 frames do they show identical checksums. For details please see http: One can see the effect here in a montage of various combinations of hardware and software scalers and encoders.

The square of which would be clamped to if no result scaling is performed.

NVIDIA Performance Primitives

This list of cuea is as follows: For best performance the application should first call nppGetStream and only call nppSetStream if the stream ID needs to change, nppSetStream will internally ucda cudaStreamSynchronize if necessary before changing stream IDs.

They have even abandoned the use of some of the algorithms for this function. Nvidia uses this fact to point to Intel’s documentation when developers have questions about it. Similarly signal-processing primitives are prefixed with “npps”.

Post as a guest Name. Scratch-buffer memory is unstructured and may be passed to the primitive in uninitialized form.

NVIDIA Performance Primitives (NPP): NVIDIA Performance Primitives

I’ll do some more tests with real footage and see how this affects the output. I have posted the problem on the Nvidia forums. Last edited 2 years ago by sdack previous diff.

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