Enhance your running performance with tailored physical therapy at District Performance & Physio in Washington DC. Personalized programs target biomechanical imbalances to prevent injuries and improve efficiency.
If you plan to store UUID values in a Primary Key column, then you are better off using a TSID (time-sorted unique identifier).
One such implementation is offered by the Hypersistence TSID OSS library, which provides a 64-bit TSID that’s made of two parts:
a 42-bit time component
a 22-bit random component
The random component has two parts:
a node identifier (0 to 20 bits)
a counter (2 to 22 bits)
The node identifier can be provided by the tsid.node system property when bootstrapping the application:
-Dtsid.node="12"
Currently adding a column to a table with a non-NULL default results in
a rewrite of the table. For large tables this can be both expensive and
disruptive. This patch removes the need for the rewrite as long as the
default value is not volatile. The default expression is evaluated at
the time of the ALTER TABLE and the result stored in a new column
(attmissingval) in pg_attribute, and a new column (atthasmissing) is set
to true. Any existing row when fetched will be supplied with the
attmissingval. New rows will have the supplied value or the default and
so will never need the attmissingval.
Thousands of students and teachers across Wales will benefit from cutting-edge data analytics technology to improve student engagement, retention and performance as a result of a funding boost to be announced today by the Higher Education Funding Council for Wales (HEFCW) and Jisc.
The training impulse (TRIMP), the heart rate stress score (HRSS) and the running stress score (rTSS) are all measures of training load but are just one piece of the puzzle. Empower yourself to train smart with exercise science articles from Thomas Solomo
Hardware performance monitoring counters have recently received a lot of attention. They have been used by diverse communities to understand and improve the quality of computing systems: for example, architects use them to extract application characteristics and propose new hardware mechanisms; compiler writers study how generated code behaves on particular hardware; software developers identify critical regions of their applications and evaluate design choices to select the best performing implementation. In this paper, we propose that counters be used by all categories of users, in particular non-experts, and we advocate that a few simple metrics derived from these counters are relevant and useful. For example, a low IPC (number of executed instructions per cycle) indicates that the hardware is not performing at its best; a high cache miss ratio can suggest several causes, such as conflicts between processes in a multicore environment. We also introduce a new simple and flexible user-level tool that collects these data on Linux platforms, and we illustrate its practical benefits through several use cases.
Recorded at SpringOne Platform 2016. Speaker: Adrian Cole Slides: http://www.slideshare.net/SpringCentral/how-to-properly-blame-things-for-causing-latency La...
Jeff Greene and Matt Bernacki are learning scientists in the UNC-Chapel Hill School of Education. They leverage the data that students create when they use digital resources to help them learn.
Researchers and educators have developed computer-based tools, such as automated writing evaluation (AWE) systems, to increase opportunities for students to produce natural language responses in a variety of contexts and subsequently to alleviate some of the pressures facing writing instructors due to growing class sizes
In this post, the Netflix Performance Engineering team will show you the first 60 seconds of an optimized performance investigation at the command line, using standard Linux tools.
Talk from SREcon2016 by Brendan Gregg. Video: https://www.usenix.org/conference/srecon16/program/presentation/gregg . "There's limited time for performance ana…
A website speed test tool to compare uBlock Origin with plain Chrome. Check the weight of your ad implementation. Please consider the environment before loading a bunch of ads on your website.
Here are the good reasons for you to get learning analytics in education or training, if you choose eLearning platform (LMS) for learning and development.
when the application hasn’t used lambda expressions before, even the framework for generating the lambda classes has to be loaded (Oracle’s current implementation uses ASM under the hood). This is the actual cause of the slowdown, loading and initialization of a dozen internally used classes, not the lambda expression itself.
React.memo() increases the performance of pure functional components by preventing useless re-renders. But such performance tweaks must be applied wisely.
Timothy A. McKay is data scientist and professor of Physics, Astronomy, and Education at the University of Michigan. He joined UBC faculty, graduate students and staff on June 20, 2019, to deliver a keynote about his experiences at the University of Michigan.
Learning and performance powered by data and analytics: a practical case study Trish Uhl, Senior Consultant, Data Science and Advanced Analytics, Owl’s Ledge LLC
If you use Google Fonts on your website or web application, a few additional steps can lead to much faster load times. In this article, I will show you how to: Google Fonts is hosted on a pretty fast…
Students interacting with universities often leave behind a virtual footprint that is used to gauge how well the university has managed to help and prepare these students. Learning analytics is using this data to analyze, measure, collate data, and more about the progress made by both students and educators.
Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction.
Building interactive sites can involve sending JavaScript to your users. Often, too much of it. Have you been on a mobile page that looked like it had loaded only to tap on a link or tried to scroll…
The goal of bringing basic, decentralised payments to the masses remains outstanding due to Hard scaling problems. These problems are rooted in the mechanics of consensus algorithms and fundamental limits in our ability to shuttle data around a global network.
Preload is a new web standard aimed at improving performance and providing more granular loading control to web developers. It gives developers the ability to **define custom loading** logic without suffering the performance penalty that script-based resource loaders incur. A few weeks ago, I shipped preload support in Chrome Canary, and barring unexpected bugs it will hit Chrome stable in mid-April. But what is that preload thing? What does it do? And how can it help you?
gpu.js is a single-file JavaScript library for GPGPU in the browser. gpu.js will automatically compile specially written JavaScript functions into shader language and run them on the GPU using the WebGL API. In the case where WebGL is not available, the functions will still run in regular JavaScript.
Once we were over the infamous Haskell learning-curve, we began looking for functional programming, immutability, and types everywhere! Given that one-third of our code runs in the browser (via Angular v1 — for now!), it is only a matter of time before we make the switch to typed-FP for front-end development as well.
React is slow - just like other frontend frameworks. But if you care about performance, it's fairly easy to make any React application super fast. Here is how.
React is usually pretty fast, but it’s easy to make small mistakes that lead to performance issues. Slow component mounts, deep component trees, and unnecessary render cycles can quickly add up to an…
Writing inefficient React components can cause them to rerender too often. Peter Bengtsson looks at ways of creating and optimizing stateless components.
Rendering a list of some elements in a page is a common task for almost any web-app. In this post I would like to show how to improve performance for that case. For a test example we will create app…
Service workers are at the core of Progressive Web Apps. They allow caching of resources and push notifications, which are two of the main distinguishing features that have set native apps apart up…
gpu.js is a GPGPU(General purpose Programming on Graphical Processing Units) library that lets you hand over hefty calculations to the GPU for super fast operation and output. It currently runs on the browser and node.js, wherein it is using WebGl API’s in the browser, and a single threaded operation on node.js. OpenCL is on the roadmap.
Lately I’ve been asked a lot about how I would implement a debounce function as an exercise, and I wondered why this question has become prevalent in the front-end engineering world. The more I was…
It’s quite common to divide our program into chunks using functions which we can call later to perform some useful action. Sometimes, a function can become expensive to call multiple times (say, a…
Thanks to mobile device and network proliferation, more people are using the web than ever before. As this user base grows, performance is more important than ever. In this article, find out why performance matters, and learn what you can do to make the web faster for everyone.
As we build sites more heavily reliant on JavaScript, we sometimes pay for what we send down in ways that we can’t always easily see. In this post, I’ll cover why a little discipline can help if…
As web developers, we know how easy it is to end up with web page bloat. But loading a webpage is much more than shipping bytes down the wire. Once the browser has downloaded our page’s scripts it…
We have extended our previous benchmarks to include other popular DOM manipulation libraries: Angular 1 and 2, Mithril.js, cito.js and the standalone independent implementation of React's Virtual DOM algorithm. We have also added more metrics (including memory use).
meta description: Making a deep convolutional neural network smaller and faster.
A user-friendly explanation how to compress CNN models - by removing full filters filters from a layer (GPU friendly, unlike sparse layers). L1-norm used for picking candidates for removal. Optimized MobileNet by 25%.
If you have 10,000 front-end users, having a connection pool of 10,000 would be shear insanity. 1000 still horrible. Even 100 connections, overkill. You want a small pool of a few dozen connections at most, and you want the rest of the application threads blocked on the pool awaiting connections.
imagine three threads (Tn=3), each of which requires four connections to perform some task (Cm=4). The pool size required to ensure that deadlock is never possible is:
pool size = 3 x (4 - 1) + 1 = 10