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The Ultimate Question of Programming, Refactoring, and Everything

Yes, you've guessed correctly - the answer is "42".

Authored by Andrey Karpov (Blackbelt) Last updated on 06/20/2019 - 22:52
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Benign Data Races: What Could Possibly Go Wrong?

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Authored by Dmitry Vyukov Last updated on 07/04/2019 - 17:06
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最快线程间数据交换算法,有效避免锁竞争 -- TwoQueues

处理多线程数据共享问题注意的几个要点:

1、锁竞争:尽量减少锁竞争的时间和次数。

2、内存:尽量是使用已分配内存,减少内存分配和释放的次数。尽量是用连续内存,减少共享占用的内存量。

多线程数据交换简单方案A:

定义一个list,再所有操作list的地方进行加锁和解锁。

简单模拟代码:

Authored by Last updated on 07/04/2019 - 21:30
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Intel® Xeon® Scalable Processor Cryptographic Performance

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  One Stop for Optimizing Your Data Center From AI to Big Data to HPC: End-to-end Solutions
Authored by admin Last updated on 07/06/2019 - 16:40
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基于英特尔® 架构加速金融应用

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Authored by George Raskulinec (Intel) Last updated on 07/06/2019 - 16:40
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Accelerating Financial Applications on Intel® architecture

Learn more about an in-depth analysis of code modernization performance conducted by optimizing original CPU code and re-running tests on the latest GPU/CPU hardware.
Authored by George Raskulinec (Intel) Last updated on 07/06/2019 - 16:40
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Fast Computation of Huffman Codes

The generation of Huffman codes is used in many applications, among them the DEFLATE compression algorithm. The classical way to compute these codes uses a heap data structure. This approach is fairly efficient, but traditional software implementations contain lots of branches that are data-dependent and thus hard for general-purpose CPU hardware to predict. On modern processors with deep...
Authored by James Guilford (Intel) Last updated on 07/09/2019 - 16:09