Subscription added. Subscription removed. Sorry, you must verify to complete this action. Please click the verification link in your email. You may re-send via your profile. I have managed to program that in OpenMP, with an histogram for every thread and a summation of all the histogram at the end. Is there a way to do that using OpenMP 4? OpenMP 4 adds C max min reductions to the previous options for parallel reduction , as well as adding omp simd reductions 1 thread, using the simd parallelism.
These are usually implemented as a tree reduction so might approximate your desire. Unfortunately on KNC I haven't seen my own parallel reduction usage showing a gain over omp parallel with a critical section so it is important not to use more threads than optimum no more than 2 threads per cache tile If the basic omp reduction operations simd or parallel don't apply for your algorithm you may have to write out the tree reduction.
The simple critical section choice may speed up a reduction but would retain the likelihood of showing a linear time behavior. Openmp 4 includes c array reduction. I've seen it work effectively for 1 or 2 threads, but otherwise the atomic is likely to be better. Can you post your code? A lot of performance difference will depend on how well vectorization is used. Which Xeon Phi are you using?
Is this an offload or native application? The fundamental operation of a histogram is an atomic increment, which can be implemented in hardware fairly efficiently.
The OpenMP 4. From the examples, it looks like you want all of the threads to update the histogram array using something like:. This approach ought to be able to exploit the hardware's ability to do atomic updates, which should be much more efficient than a critical section. It is mainly for an "academic" purpose as it is for a code modernization workshop. I understand that this code is memory bound but, it could be useful to launch many threads on a NUMA system, and a Xeon Phi KNL in quadrant mode should be considered as a quad-socket system.
Quadrant mode is a uniform memory space. I see you code that builds the random distribution in x[], But I do not see the code inside the timed region that builds the histogram. I agree that this is not an histogram. Instead of adding one to the "bin", we add the value. It looked to me close enough to the histogram algorithm that I have simplified things.
Here, the part where we finalize the reduction is linear in the number of threads. I want to make it in log of the number of threads. For more complete information about compiler optimizations, see our Optimization Notice. These image-processing applications use Intel IPP:. Reduce the number of bits needed to store or transmit data. Intel IPP highly optimizes these common compression standards:.
Achieve significant performance gains with plug and play functions on applications such as these:. Enable information generation, transformation, and interpretation. Pull meaning from broad sources of data, helping modern communications that include:. Optimize commonly used signal-processing functions for a wide variety of Intel architectures, including:.
Protect against cyberattacks and intrusion in the field of autonomous, self-driving cars with functions for:. The cryptography library is available as an open-source library. View All Documentation. Use them to create build-in or offloaded applications on the architecture. This example shows how to use the libraries on Intel Xeon Phi coprocessors. Runtime Version. Give Us Your Feedback. Performance varies by use, configuration and other factors.
Learn more at www. Skip To Main Content. Safari Chrome Edge Firefox. Its royalty-free APIs help developers: Take advantage of Single Instruction, Multiple Data SIMD instructions Improve the performance of computation-intensive applications, including signal processing, data compression, video processing, and cryptography Reduce cost and time to market for software development and maintenance Release Notes.
A Comprehensive Set of Primitives Access thousands of optimized functions covering frequently used fundamental algorithms, including those for creating: Digital media Enterprise data Embedded communications Scientific, technical, and security applications The library includes more than 2, primitives for image processing, 1, for signal processing, for computer vision, and for cryptography.
Image Processing Take visual information and convert it into manageable, usable data for further analysis and decision-making. These image-processing applications use Intel IPP: Healthcare medical imaging Computer vision E-commerce visual search Digital surveillance Biometric identification Factory machine vision Advanced driver assistance systems ADAS for autonomous driving Printing and printers Image recognition and enhancement Remote equipment operation Gesture recognition Illegal image recognition Optical correction.
Data Compression Reduce the number of bits needed to store or transmit data. Signal Processing Enable information generation, transformation, and interpretation. Pull meaning from broad sources of data, helping modern communications that include: Voice recognition Biotechnology Wearable technology Hearing aids Speech synthesis Optimize commonly used signal-processing functions for a wide variety of Intel architectures, including: Discrete Fourier transform DFT Fast Fourier transforms FFT Convolution Filtering Statistics These signal-processing applications use Intel IPP: Telecommunications Energy Ultrasound machines Medical scanning Record, enhance, and playback audio and non-audio signals Echo cancellation: filter, equalize, and emphasis Simulation of environment or acoustics Games with sophisticated audio content or effect Interfaces for voice-controlled personal assistants.
Sample Description Multithread Image Resize Learn how to use the ippiResize functionality in single and multithread modes. Since internal threading is deprecated, it is important to know how to externally thread a generic Intel IPP function.
View all Show less. Get the Single Component A stand-alone version of this component is available.
0コメント