Threading Intel® Integrated Performance Primitives Image Resize with Intel® Threading Building BlocksThreading Intel® IPP Image Resize with Intel® TBB.pdf (157.18 KB) :
We demonstrate how to create a Sierpinski Carpet in OpenCL* 2.0
The code samples for the webinar "Further Vectorization Features of the Intel® Compiler" given on 4/7/2015 are attached below.
While Image convolution is not as effective with the new Read-Write images functionality, any image processing technique that needs be done in place may benefit from the Read-Write images. One example of a process that could be used effectively is image composition. In OpenCL 1.2 and earlier, images were qualified with the “__read_only” and __write_only” qualifiers. In the OpenCL 2.0, images can...
The Storage Performance Development Kit (SPDK) is an open source set of tools and libraries hosted on GitHub that helps you create high-performance and scalable storage applications. This tutorial focuses on the userspace NVMe driver provided by SPDK and illustrates a Hello World example.
You can use LibRealSense and OpenCV* to stream RGB and depth data from your connected Intel® RealSense™ camera. This tutorial and code sample shows how to do this, based on the Ubuntu* operating system. In the end you will have a nice starting point where you use this code base to build upon to create your own LibRealSense / OpenCV applications.
DEFLATE compression algorithms traditionally use either a dynamic or static compression table, with tradeoffs in processing time. The Intel® Intelligent Storage Acceleration Library (Intel® ISA-L) semi-dynamic compression comes close to getting the best of both worlds. Learn how.
The attached code sample compares memcpy and SKDK + Intel I/OAT DMA performance when moving different size data chunks in memory.
Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.