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Parallel Labs

Architect和Artisan - Parallel Labs 创业与企业家精神 - Parallel Labs 采访Hadoop创始人Doug Cutting纪要 - Parallel Labs 智能优化&AB测试-实验驱动用户增长@QCon10 PPT分享 - Parallel Labs Druid 6th Meetup资料下载 - Parallel Labs 增长二三事 - Parallel Labs 两个平行世界 - Parallel Labs Shape the world to come - Parallel Labs 2018新年目标 - Parallel Labs 人工智能芯片公司招聘工程师/行政/出纳 - Parallel Labs Druid中国用户组第一次线下技术交流资料分享 - Parallel Labs 再见了,IBM中国研究院 | Parallel Labs 怎样做颠覆式创新? - Parallel Labs 基于OpenStack, Docker和Spark打造SuperVessel大数据公有云 - Parallel Labs 给Vim配置Scala语法高亮显示 - Parallel Labs 一步一步教你怎样给Apache Spark贡献代码 - Parallel Labs 大数据的价值密度 - Parallel Labs IBM研究院(CRL)诚聘 Bigdata/Clould 方向正式员工 - Parallel Labs My Way - Parallel Labs Impala:新一代开源大数据分析引擎 - Parallel Labs Impala与Stinger对比 - Parallel Labs Git快速学习指南 - Parallel Labs 与Google拼音的工程师聊聊中文滑行输入 - Parallel Labs 仰望星空 脚踏实地 - Parallel Labs 记一次诡异的Debug经历 - Parallel Labs 下一代大数据分析技术 - Parallel Labs 多核与异步并行 - Parallel Labs 做好失败的准备 - Parallel Labs Facebook技术分享: Social Networking at Scale - Parallel Labs 为什么NoSQL和Hadoop该一起使用? - Parallel Labs Understanding System and Architecture for Big Data - Parallel Labs C++ AMP异构并行编程解析 - Parallel Labs Intel Nehalem微处理器架构 by Glenn Hinton (Intel Fellow) - Parallel Labs 云计算时代的多核开发 - Parallel Labs X-RIME: 基于Hadoop的开源大规模社交网络分析工具 - Parallel Labs 并行编程中的“锁”难题 - Parallel Labs [已经招到了,谢谢大家!]IBM中国研究院招聘Hadoop实习生 - Parallel Labs IBM中国研究院招聘大规模数据分析实习生 - Parallel Labs 浅析C++多线程内存模型 - Parallel Labs Facebook的Realtime Hadoop及其应用 - Parallel Labs 《程序员的自我修养》中关于加锁不能保证线程安全的一个错误 - Parallel Labs 你好,2011! - Parallel Labs 移动设备进入多核时代! - Parallel Labs 为什么在多核多线程程序中要慎用volatile关键字? | Parallel Labs Jeff Dean关于Google系统架构的讲座 - Parallel Labs Erlang User Conference 2010见闻(兼谈程序员职业生涯) - Parallel Labs 多线程程序常见Bug剖析(下) - Parallel Labs 多线程程序常见Bug剖析(上) - Parallel Labs 史蒂夫乔布斯(Steve Jobs)在Stanford2005年毕业典礼上的演讲 - Parallel Labs 多线程队列的算法优化 - Parallel Labs Google创始人的求职目标 - Parallel Labs 多核的未来 - Parallel Labs 多核编程的难题(二) - Parallel Labs 多核编程的难题(一) - Parallel Labs 二进制的二三事 - Parallel Labs 聊一聊瑞典的程序员 - Parallel Labs 多线程程序中操作的原子性 - Parallel Labs 第三次软件危机 - Parallel Labs 实施并行编程的五大障碍 - Parallel Labs 为什么程序员需要关心顺序一致性(Sequential Consistency)而不是Cache一致性(Cache Coherence?) | Parallel Labs 八条设计多线程程序的简单规则 - Parallel Labs 瑞典Ericsson总部Master Thesis面试回忆录 | Parallel Labs Pthreads并行编程: 线程同步之spin lock与mutex性能比较 | Parallel Labs 09年感悟 - Parallel Labs Proposal for the “Search and sort” competition of Findwise - Parallel Labs 在瑞典打甲流疫苗 - Parallel Labs The Longest Plateau | Parallel Labs Launched my master thesis finally - Parallel Labs Hello world! - Parallel Labs
How to do performance analysis on your parallelized program efficiently? - Parallel Labs
Guancheng (G.C.) · 2010-01-31 · via Parallel Labs

跳至内容

Be a scientist: Gather data. Analyze it. Especially when it comes to parallelism and scalability, there’s just no substitute for the advice to measure, measure, measure, and understand what the results mean. Putting together test harnesses and generating and analyzing numbers is work, but the work will reward you with a priceless understanding of how your code actually runs, especially on parallel hardware—an understanding you will never gain from just reading the code or in any other way. And then, at the end, you will ship high-quality parallel code not because you think it’s fast enough, but because you know under what circumstances it is and isn’t (there will always be an “isn’t”), and why.

Herb Sutter

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