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OpenMP* and the Intel® IPP Library

How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
Authored by Last updated on 07/31/2019 - 14:30
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Improving the Compute Performance of Video Processing Software Using Intel® Advanced Vector Extensions (Intel® AVX) Instructions

This paper describes a case study in which AVX instructions are used to enhance the performance of a de-saturation algorithm (a common video filter). The case study takes the algorithm from a non-SIMD state to AVX based SIMD.
Authored by Last updated on 07/10/2019 - 16:54
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Fast Gathering-based SpMxV for Linear Feature Extraction

This algorithm can be used to improve sparse matrix-vector and matrix-matrix multiplication in any numerical computation. As we know, there are lots of applications involving semi-sparse matrix computation in High Performance Computing. Additionally, in popular perceptual computing low-level engines, especially speech and facial recognition, semi-sparse matrices are found to be very common....
Authored by Last updated on 12/12/2018 - 18:00
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Programming for Multicore and Many-core Products including Intel® Xeon® processors and Intel® Xeon Phi™ X100 Product Family coprocessors

The programming models in use today, used for multicore processors every day, are available for many-core coprocessors as well. Therefore, explaining how to program both Intel Xeon processors and Intel Xeon Phi coprocessor is best done by explaining the options for parallel programming. This paper provides the foundation for understanding how multicore processors and many-core coprocessors are...
Authored by James R. (Blackbelt) Last updated on 10/15/2019 - 15:11
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Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Authored by Last updated on 10/15/2019 - 15:30
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Vector API Developer Program for Java* Software

This article introduces Vector API to Java* developers. It shows how to start using the API in Java programs, and provides examples of vector algorithms. It provides step-by-step details on how to build the Vector API and build Java applications using it. It provides the location for downloadable binaries for Project Panama binaries.
Authored by Neil V. (Intel) Last updated on 10/15/2019 - 15:30
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面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Authored by Last updated on 10/15/2019 - 16:50
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How Intel® Advanced Vector Extensions 2 Improves Performance on Server Applications

The latest Intel® Xeon® processor E5 v3 family includes a feature called Intel® Advanced Vector Extensions 2 (Intel® AVX2), which can potentially improve application performance related to

Authored by Thai Le (Intel) Last updated on 10/15/2019 - 20:18
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Intel® AVX-512 Instructions

The latest Intel® Architecture Instruction Set Extensions Programming Reference includes the definition of Intel® Advanced Vector Extensions 512 (Intel® AV

Authored by James R. (Blackbelt) Last updated on 10/15/2019 - 20:39
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Additional Intel® AVX-512 instructions

Additional Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

Authored by James R. (Blackbelt) Last updated on 10/15/2019 - 20:40