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Here is a list of tings I read/watched/played/etc. this week that you might find interesting or enjoyable: Things to read Link to heading SIMD in the 90s — Great article explaining what Intel’s MMX was all about.
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Here is a list of tings I read/watched/played/etc. this week that you might find interesting or enjoyable: Things to read Link to heading The fastest double-to-string algorithm you’ve never heard of — I always enjoy reading about how floating point formats
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Here is a list of tings I read/watched/played/etc. this week that you might find interesting or enjoyable: Things to read Link to heading A Deep Dive into C++26 std::hive: The Ultimate Container for Active Data — Some details about the internals of C++ 26…
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Detecting whether a user tapped on an arbitrary shape represented by a Path is an unfortunately difficult task on Android.
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I recently realized that I have never discussed graphics programming on this blog, and decided it was time to correct this.
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There is a simple but often overlooked technique to optimize performance-sensitive code: merging (or manually inlining) functions.
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The Android Runtime (ART) offers a nice memory safety feature when accessing the content of an array.
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Before we dive into today’s topic, I would like to make it clear that what follows is specific to how Android, and more precisely the Android RunTime (ART), works.
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In the last post, we saw that benchmarks don’t always measure what we think they measure.
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Optimizing code can be a difficult task because there are so many traps you need to avoid at every step of the way.
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BlurHash is a compact representation of placeholders for images.
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Today I would like to show you a micro-optimization I recently used more for the fun of it than for its real impact.
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Jake Wharton recently caused me to go down yet another silly optimization rabbit hole when he nonchalantly linked to a piece of code used to count the number of digits in a Long during a Slack conversation about Kotlin’s lack of ternary operator.
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I recently discussed an optimization that I worked on following Leland’s successful nerd snipe.
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I recently gave a talk called Practical Optimizations at Android Makers 2024.
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Leland and I were recently discussing how to best implement a new data structure to speed up a specific aspect of Jetpack Compose.
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I was recently optimizing a small part of the Jetpack Compose runtime when I stumbled upon a seemingly harmless API, isBlank().
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The Kotlin standard library is a wonderful set of APIs, but it sometimes hides… interesting surprises.
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While looking for optimization opportunities in various parts of Jetpack Compose, I recently discovered that calling the cube root function was taking a non-neligible amount of times in two areas of the Toolkit: when evaluating cubic Bézier easing curves…
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Hash maps are extremely common data structures, and Jetpack Compose unsurprisingly takes advantage of them for various tasks.
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In the previous post, we saw how we could micro-optimize Int.sign to save a few instructions.
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While my work responsibilities do not leave me much time to write code nowadays, I have managed to make a few small contributions to Jetpack Compose in the last few months, mostly focusing on performance.