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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I don’t see a reason to deprecate it. Many NPP functions require converting floating-point values to integers. I’m using Cyda 5. Sign up using Email and Password. Nvidia uses this fact to point to Intel’s documentation when developers have questions about it.

Before the results of an operation are clamped to the valid output-data range by multiplying them with.

The NPP library is written to maximize flexibility, while maintaining high performance. In order to give the NPP user maximum control regarding memory allocations and performance, it is the user’s responsibility to allocate and delete those temporary buffers. I’m not saying it np be removed.

NVIDIA Performance Primitives (NPP): Integral

All the code in ffmpeg does it passing the interpolation-method on to libnpp. As an aside, I don’t think any library can ever be “fully optimized”. The default stream ID is 0. According to their documentation: There are no more identical outputs. The same problem could be said of many SW packages that arise from HW companies. I don’t know yet how this affects the algorithms, but a first test with the shifts changed to 0.


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

It’s an upstream bug, and it still gets the job done, just not with the correct scaling type. What was the difference, in percent? The square of which would be clamped to if no result scaling is performed.

NVIDIA Performance Primitives

Oldest first Newest first Threaded. However by looking at one of Nvidia’s programming samples do they use 0.

The minimum scratch-buffer size for a given primitive e. Calling cudaDeviceSynchronize frequently can kill performance so minimizing the frequency of these calls is critical for good performance. It’s then better to give users a “heads up” by declaring it as deprecated, not to make it npp secret, and to hope it’s going to change in the future.

This list of sub-libraries is as follows:.

The mirroring operations will be memory bound and newer devices are flexible in which types of memory access patterns they will handle efficiently. See TracTickets for help on using tickets. If a primitive consumes different type data from what it produces, both types will be listed in the order of consumed to produced data type.


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. It also allows the user the maximum flexibility regarding which of the various memory transfer mechanisms offered by the CUDA runtime is used, e.

NVIDIA Performance Primitives (NPP): NVIDIA Performance Primitives

The maximum value of a 8-bit value is I see a light at the horizon For details please see http: I’d like to wait for a response by Nvidia. Because of this fixed-point nature of the representation many numerical operations e. Specially as there is no replacement. Post as a guest Name.

To minimize library loading and CUDA runtime startup times it is recommended to use the static library s whenever possible.