Intel® IPP ZLIB Coding Functions

1. Overview
作者: Chao Y (Intel) 最后更新时间: 2019/07/31 - 14:30

Big Datasets from Small Experiments

作者: Andrey Vladimirov 最后更新时间: 2019/07/04 - 18:46

Intel® CPU Excels in MLPerf* Reinforcement Learning Training

Today, MLPerf* consortium, a group of 40 companies and university research institutes, published the 2nd round of the benchmark results based upon ML

作者: Koichi Yamada (Intel) 最后更新时间: 2019/09/30 - 16:50

Thread Parallelism in Cython*

Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/10/15 - 16:40

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/10/15 - 16:50

Tutorial for Intel® DAAL: Using Simple C++ Examples

System Environment

Intel® DAAL version : 2016 Gold Initial Release (w_daal_2016.0.110.exe)

OS : Windows* 8.1

IDE : Visual Studio 2013


作者: JON J K. (Intel) 最后更新时间: 2019/10/15 - 21:14

Running Intel® Parallel Studio XE Analysis Tools on Clusters with Slurm* / srun

Since HPC applications target high performance, users are interested in analyzing the runtime performance of such applications.

作者: Michael Steyer (Intel) 最后更新时间: 2019/10/15 - 21:21

Transform Enterprise, HPC & AI, Accelerate Parallel Code

作者: 管理 最后更新时间: 2019/10/15 - 21:26

OpenStack App Developer Survey

As part of a long-term commitment to enhance ease-of-use, the OpenStack UX project, with support of the OpenStack Foundation and the Technical Committee, is now bu

作者: Mike P. (Intel) 最后更新时间: 2019/12/09 - 12:20

The JITter Conundrum - Just in Time for Your Traffic Jam

In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
作者: David S. (Blackbelt) 最后更新时间: 2019/12/09 - 12:20