To play & participate in the challenge: http://speedperception.meteorapp.com/challenge
SpeedPerception is an open-source, collaborative effort to understand perceptual aspects of end-user web experience. Our goal is to create a free, open-source, benchmark dataset to advance the systematic study of how people perceive the webpage loading process – the above-the-fold rendering in particular. Our belief (and hope) is that this benchmark can provide a quantitative basis to compare different algorithms and spur computer scientists and other web performance engineers to make progress quantifying perceived webpage performance. We plan to open source all the collected data and analysis once there is sufficient participation. Please share this post – we need as many people to participate as possible.
-- Parvez Ahammad, Clark Gao, Prasenjit Dey, Estelle Weyl, Pat Meenan
In our group, we have been interested in the question of how the structure of a webpage influences its performance on the web. It is (in my opinion) one of the key questions at the heart of distributed web application delivery. Thanks to amazing resources like HTTP Archive and BigQueri.es, it is pretty straight forward to access and play with large scale web performance data (measured twice a month across 400,000+ websites and made available for free!).
Note: This blog post describes collaborative work that had contributions from myself, Clark Gao, Matthew Mok and Karan Kumar (all at Instart Logic Inc.). The image above is borrowed from Paul Irish's talk slides with my hand drawn squiggly circle to highlight this blog post's focus.
Sometime last year when I started to dig deeper into web performance and how human end-users perceive the process of webpage loading, it quickly became clear that typical W3C standard metrics weren't sufficient. I also learned (painfully) that popular page-level metrics like onLoad could be easily gamed via scripting tricks. I became particularly fascinated with the problem of measuring above-the-fold webpage loading process - one that's almost a computer vision type of a problem (but my computer vision friends don't know about it yet). Couple of early discoveries kept me going: