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This was a chance to talk about other things I've been working on, such as the present and future of hardware performance. The video is on [youtube]: The slides are on [slideshare] or as a [PDF]: I work on many areas of performance, but recently I've had a lot of demand to talk about BPF.
We used this model effectively at Netflix when I was their cloud architect from 2010 through 2013. The layers of platforms start at the bottom with hardware choices such as which CPU architectures and vendors you want to use. There are three current underlying reasons for the platform engineering meme today.
Google and Amazon’s latest AI chips have arrived," [link] Oct 2022 - [Intel 22] Intel, "Intel® Developer Cloud," [link] accessed Dec 2022 I've taken care to cite the author names along with the talk titles and dates, including for Internet sources, instead of the common practice of just listing URLs.
Doubly so as hardware improved, eating away at the lower end of Hadoop-worthy work. ” There’s as much Keras, TensorFlow, and Torch today as there was Hadoop back in 2010-2012. Between Google (Vertex AI and Colab) and Amazon (SageMaker), you can now get all of the GPU power your credit card can handle.
Examples include popular home assistants and smart displays like the Amazon Echo, Google Home, Apple HomePod, and many others. In Privacy in Context, Nissenbaum talks about the privacy implications of Google Street View when it places photos of people’s houses on Google Maps. Source: Google Face Match video, [link] ).
This was a chance to talk about other things I've been working on, such as the present and future of hardware performance. The video is on [youtube]: The slides are [here] or as a [PDF]: first prev next last / permalink/zoom I work on many areas of performance, but recently I've had a lot of demand to talk about BPF.
Hardware Past As Performance Prologue. Regardless, the overall story for hardware progress remains grim, particularly when we recall how long device replacement cycles are: Tap for a larger version. Sadly, data on latency is harder to get, even from Google's perch, so progress there is somewhat more difficult to judge.
As for attending USENIX conferences: I finally started attending and speaking at them in 2010 when a community manager encouraged me to (thanks Deirdre Straughan), and since then I've met many friends and connections, including Amy who is now USENIX President, and Rikki with whom I co-chaired the USENIX LISA18 conference.
HTML, CSS, images, and fonts can all be parsed and run at near wire speeds on low-end hardware, but JavaScript is at least three times more expensive, byte-for-byte. decoding="async" loading="lazy" /> Google spent more on the SoC for the Pixel 4a and enjoyed a later launch date, boosting performance relative to the A51.
The webp format was introduced by Google around 2010. Google added decoding support on Android devices and Chrome browser and also released libraries that developers could add to their apps on other platforms, for example iOS. There is a potential benefit in reusing the hardware in place for video compression/decompression.
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