https://www.suse.com/documentation/sles11/book_sle_tuning/data/sec_util_memory.html SLES 11/12 OS Tuning & Optimization Guide – Part 1 https://www.suse.com/c/sles-1112-os-tuning-optimisation-guide-part-1/ SLES 11/12: Network, CPU Tuning and Optimizat…

2017-02-22 在经过Trace的分析并重新优化之后 CPU: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.6GHz 24core 如上图,CPU使用率平均低于60% 内存: 32G 1.100并发 2.200并发 3.300并发 内存: 128G 1.100并发 2.200并发 3.300并发 从上述同比分析看出 1.此次优化之后无论是32G还是128G的状况下QPS均有100%的提升.在100并发下32G与128G的QPS差距不超过6%,200和300并发的…

Which are the frequently asked interview questions for Java Engineers ? Vivek Vermani, www.buggybread.com | Programme... (more) 265 upvotes by Ridox Liu, Shivani Sahni Vermani, Viet Thang, (more)     Java的基础知识   For a Core Java Developer , Questions…

CPU Tuning Cache share tuning 对于物理 CPU,同一个 core 的 threads 共享 L2 Cache,同一个 socket 的 cores 共享 L3 cache,所以虚拟机的 vcpu 应当尽可能在同一个 core 和 同一个 socket 中,增加 cache 的命中率,从而提高性能.IBM 测试过,合理绑定 vcpu 能给 JVM 来的 16% 的性能提升[2].       实现策略:虚拟机 vcpu 尽可能限定在一个 core 或者一个 socke…

Oracle SQL Tuning and CBO Internals: Based Optimizer with CBO Internals and SQL Tuning Optimization https://vimeo.com/ondemand/oraclemechanisms/ http://allthingsoracle.com/author/jonathan-lewis/ https://alexanderanokhin.wordpress.com/2012/03/19/dtrac…

Frequently Bought Together + + Total price: $131.71 Add all three to CartAdd all three to List Buy the selected items together This item:Pro SQL Server Internals by Dmitri Korotkevitch Paperback $53.69 Microsoft SQL Server 2014 Query Tuning & Optimiz…

Oracle SQL Tuning and CBO Internals: Based Optimizer with CBO Internals and SQL Tuning Optimization https://vimeo.com/ondemand/oraclemechanisms/ http://allthingsoracle.com/author/jonathan-lewis/ https://alexanderanokhin.wordpress.com/2012/03/19/dtrac…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. 请不要ctrl+c/ctrl+v作业. Optimization Methods Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can spee…

Optimization Welcome to the optimization's programming assignment of the hyper-parameters tuning specialization. There are many different optimization algorithms you could be using to get you to the minimal cost. Similarly, there are many different p…

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…

Lesson 2 Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第二门课程的课程笔记. 参考了其他人的笔记继续归纳的. 训练,验证,测试集 (Train / Dev / Test sets) 在机器学习发展的小数据量时代,常见做法是将所有数据三七分,就是人们常说的 70% 训练集,30% 测试集.如果明确设…

Tuning process 下图中的需要tune的parameter的先后顺序, 红色>黄色>紫色,其他基本不会tune. 先讲到怎么选hyperparameter, 需要随机选取(sampling at random) 随机选取的过程中,可以采用从粗到细的方法逐步确定参数 有些参数可以按照线性随机选取, 比如 n[l] 但是有些参数就不适合线性的sampling at radom, 比如 learning rate α,这时可以用 log Andrew 很幽默的讲到了两种选参数的实际场景…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Initialization Welcome to the first assignment of "Improving Deep Neural Networks". Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Regularization Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough. Sure it do…

Tensorflow Welcome to the Tensorflow Tutorial! In this notebook you will learn all the basics of Tensorflow. You will implement useful functions and draw the parallel with what you did using Numpy. You will understand what Tensors and operations are,…

训练.验证.测试划分的量要保证数据来自一个分布偏差方差分析如果存在high bias如果存在high variance正则化正则化减少过拟合的intuitionDropoutdropout分析其它正则化方法数据增加(data augmentation)early stoppingensemble归一化输入归一化可以加速训练归一化的步骤归一化应该应用于:训练.验证.测试梯度消失/爆炸权重初始化通过数值近似计算梯度优化算法mini-batchmomentumRMSpropAdam调参顺序批规范化Ba…

1. Setting up your Machine Learning Application 1.1 训练,验证,测试集(Train / Dev / Test sets) 1.2 Bias/Variance(偏差和方差) 高偏差(high bias)称为"欠拟合"(underfitting), 练集误差与验证集误差都高. 高方差(high variance)称为过拟合(overfitting), 训练集误差很低而验证集误差很高. 1.3 Basic "recipe"…

Gradient descent Batch Gradient Decent, Mini-batch gradient descent, Stochastic gradient descent 还有很多比gradient decent 更优化的算法,在了解这些算法前,需要先理解  Exponentially weighted averages 这个概念 Exponentially weighted average 是一种计算平均值的方法,非常省storage 和 memory, 但是不是很精确.…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Gradient Checking Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. You are part of a team working to make mobile payments available globally, and…

Train/Dev/Test set Bias/Variance Regularization  有下面一些regularization的方法. L2 regularation drop out data augmentation(翻转图片得到一个新的example), early stopping(画出J_train 和J_dev 对应于iteration的图像) L2 regularization: Forbenius Norm. 上面这张图提到了weight decay 的概念 Weigh…

Gradient Checking Welcome to this week's third programming assignment! You will be implementing gradient checking to make sure that your backpropagation implementation is correct. By completing this assignment you will: - Implement gradient checking…

第一周:深度学习的实践层面 (Practical aspects of Deep Learning) 1.1 训练,验证,测试集(Train / Dev / Test sets) 创建新应用的过程中,不可能从一开始就准确预测出一些信息和其他超级参数,例如:神经网络分多少层:每层含有多少个隐藏单元:学习速率是多少:各层采用哪些激活函数.应用型机器学习是一个高度迭代的过程. 从一个领域或者应用领域得来的直觉经验,通常无法转移到其他应用领域,最佳决策取决于 所拥有的数据量,计算机配置中输入特征的数量,…

Week 1 Quiz - Practical aspects of deep learning(第一周测验 - 深度学习的实践) \1. If you have 10,000,000 examples, how would you split the train/dev/test set? (如果你有 10,000,000 个样本,你会如何划分训练/开发/测试集?) [ ]98% train . 1% dev . 1% test(训练集占 98% , 开发集占 1% , 测试集占 1%) 答案…

本文译自Wikipedia的Performance tuning词条,原词条中的不少链接和扩展内容非常值得一读,翻译过程中暴露了个人工程学思想和英语水平的不足,翻译后的内容也失去很多准确性和丰富性,需要在之后的时间继续细读. Performance Tuning特指计算机系统的性能优化工作.它通常是根据某个现实或潜在的性能问题而发起的.绝大部分系统都会因为负载的升高而导致性能下降,一个系统对于负载的可承受能力称为可扩展性(scalability),调整系统使其可以承受更大的负载就是perform…

对Oracle数据库整体性能的优化,首先要关注的是在有性能问题时数据库排名前几位等待事件是哪些.Oracle等待事件众多,随着版本的升级,数量还在不断增加,可以通过v$event_name查到当前数据库版本包含的等待事件.例如我在Linux平台查11.2.0.4版本的Oracle是有1367个等待事件.SELECT name FROM V$EVENT_NAME ORDER BY name; 如此多的等待事件自然是要分类汇总,并对常见的等待事件有比较深入的认识,才能在Oracle数据库调优这条路上…

原链接地址:http://www.artzstudio.com/2016/07/website-speed-optimization-guide-for-google-pagespeed-rules/  Website Speed Optimization Guide for Google PageSpeed Rules By Dave Artz  1 week ago  6  534 0   Page Speed/Site speed is termed as the speed with w…

Bruce Momjian POSTGRESQL is an object-relational database developed on the Internet by a group of developers spread across the globe. It is an open-source alternative to commercial databases like Oracle and Informix. POSTGRESQL was originally develop…

http://blog.packagecloud.io/eng/2016/06/22/monitoring-tuning-linux-networking-stack-receiving-data/ Jun 22, 2016 • packagecloud Tags: packagecloud linux kernel networking optimization tuning monitoring TL;DR This blog post explains how computers runn…

http://java.dzone.com/articles/performance-tuning For most typical Spring/Hibernate enterprise applications, the application performance depends almost entirely on the performance of it's persistence layer. This post will go over how to confirm that…

Java Performance Optimization by: Pierre-Hugues Charbonneau reference:http://refcardz.dzone.com/refcardz/java-performance-optimization Java is among the most widely used programming languages in the software development world today. Java applications…