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.
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T. Mytkowicz, A. Diwan, M. Hauswirth, and P. Sweeney. Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation, page 187--197. ACM, (June 2010)
Revathi. International Journal of Innovative Research in Information Security, 09 (2):
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M. Straesser, J. Mathiasch, A. Bauer, and S. Kounev. Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering, page 187-198. New York, NY, USA, Association for Computing Machinery, (2023)
M. Straesser, J. Mathiasch, A. Bauer, and S. Kounev. Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering, page 187-198. New York, NY, USA, Association for Computing Machinery, (2023)