Technical Article

Оптимизировали, оптимизировали, да не выоптимизировали!

Оптимизация? Конечно, каждый сталкивался с данной задачей при разработке своих, сколь-нибудь значительных, требующих определённых вычислений, приложений. При этом способов оптимизировать код существует огромное множество, и, как следствие, различных путей сделать это в автоматическом режиме с помощью опций компилятора. Вот здесь и возникает проблема – как выбрать то, что нужно нам и не запутаться?

The Ultimate Question of Programming, Refactoring, and Everything

Yes, you've guessed correctly - the answer is "42". In this article you will find 42 recommendations about coding in C++ that can help a programmer avoid a lot of errors, save time and effort. The author is Andrey Karpov - technical director of "Program Verification Systems", a team of developers, working on PVS-Studio static code analyzer.

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  • Analyzing Open vSwitch* with DPDK Bottlenecks Using Intel® VTune™ Amplifier

    This article shows how we used Intel® VTune™ Amplifier to identify and fix an MMIO transaction performance bottleneck at the microarchitecture level in OVS-DPDK.
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  • OpenCL™ Out-of-Order Queue on Intel® Processor Graphics

    This paper details the implementation of out of order queues, an OpenCL™ construct that allows independent kernels to execute simultaneously whenever possible, and thus keep all GPU assets fully utilized.
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  • 借助针对英特尔® 架构优化的 Caffe* 来训练和部署深度学习网络

    Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL) 2017 optimized for Advanced Vector Extensions (AVX)-2 and AVX-512 instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors (among others). This article describes how to build Caffe optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks. In addition, various functionalities of Caffe are explored in detail including how to fine-tune, extract and view features of different models, and use the Caffe Python API.
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