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Back in 2016, I gave a talk outlining the causes and effects of the terrible performance of web apps built using popular tools on the fastest-growing device segment: low-end to mid-range Android phones. A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage.
Google’s industry benchmarks from 2018 also provide a striking breakdown of how each second of loading affects bounce rates. Compressing, minifying and caching assets. We can compress our assets, minify our styles and scripts, and cache things responsibly so we’re serving what the user needs in the most efficient way possible.
“Memory Bandwidth and System Balance in HPC Systems” If you are planning to attend the SuperComputing 2016 conference in Salt Lake City next month, be sure to reserve a spot on your calendar for my talk on Wednesday afternoon (4:15pm-5:00pm).
This was a keynote presentation at the “2nd International Workshop on Performance Modeling: Methods and Applications” (PMMA16), June 23, 2016, Frankfurt, Germany (in conjunction with ISC16 ). Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.
As an engineer on a browser team, I'm privy to the blow-by-blow of various performance projects, benchmark fire drills, and the ways performance marketing (deeply) impacts engineering priorities. With each team, benchmarks lost are understood as bugs. All modern browsers are fast, Chromium and Safari/WebKit included. Content Indexing.
Budgets are scaled to a benchmark network & device. Deciding what benchmark to use for a performance budget is crucial. Simulated packet loss and variable latency, however, can make benchmarking extremely difficult and slow. The true median device from 2016 sold at about ~$200 unlocked. Global Ground-Truth.
Let the web developer handle all of the necessary speed optimizations like caching and file minification while you take on the following design tips and strategies: 1. Because service workers exist outside of the web browser and are not contingent on the speed of the user’s network, they load cached content for visitors more quickly.
This was a keynote presentation at the “2nd International Workshop on Performance Modeling: Methods and Applications” (PMMA16), June 23, 2016, Frankfurt, Germany (in conjunction with ISC16 ). Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.
From 2007 until 2016, Intel was able to successfully execute their Tick-Tock release strategy, where they would introduce a new processor microarchitecture roughly every two years (a Tock release). Intel has been stuck at 14nm in the server space since the Broadwell release in Q4 of 2016. So, what has changed my mind?
The L3 cache size is 64MB. I wrote about using CPU-Z to benchmark the Intel Xeon E5-2673 v3 processor in an Azure VM in this article. The slightly newer Intel Xeon E5-26xx v4 (Broadwell) series which was introduced in Q1 of 2016, increased that to 2400MHz. Figure 1: CPU-Z Benchmark Results for LS16v2. Azure Lsv2 Details.
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