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Read our article on FreeBSD vs. Linux: Virtualization Showdown. Compare VM performance on FreeBSD's bhyve and Linux's KVM to make informed virtualization decisions.
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.
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
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.
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
A discussion of some key Bakhtinian and Volosinovian notions and nudges towards their possible utility. These ideas might re-define character in theatre performances.
