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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.
作者: 最后更新时间: 2019/07/31 - 14:30
Article

Improving Averaging Filter Performance Using Intel® Cilk™ Plus

Intel® Cilk™ Plus is an extension to the C and C++ languages to support data and task parallelism.  It provides three new keywords to i

作者: Anoop M. (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

Vectorizing Loops with Calls to User-Defined External Functions

Introduction

作者: Anoop M. (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

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....
作者: 最后更新时间: 2018/12/12 - 18:00
File Wrapper

Parallel Universe Magazine - Issue 22, September 2015

作者: 管理 最后更新时间: 2018/12/12 - 18:08
File Wrapper

Parallel Universe Magazine - Issue 24, March 2016

作者: 管理 最后更新时间: 2018/12/12 - 18:08
Article

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.
作者: 最后更新时间: 2019/10/15 - 15:30
Article

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.
作者: Neil V. (Intel) 最后更新时间: 2019/10/15 - 15:30
Article

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.
作者: 最后更新时间: 2019/10/15 - 16:50