/Performance

Making A Go Program 42% Faster With A One Character Change

- Harry Marr tl;dr: "If you read the title and thought “well, you were probably just doing something silly beforehand”, you’re right! But what is programming if not an exercise in making silly mistakes? Tracking down silly mistakes is where all the fun is to be had! I’ll also state the usual benchmarking caveat up front: the 42% speedup was measured while running the program on my data on my computer, so take that number with a big old pinch of salt."

featured in #369


Seeing Through Hardware Counters: A Journey To Threefold Performance Increase

- Vadim Filanovsky Harshad Sane tl;dr: "There is, however, a class of problems that requires an even stronger level of magnification going deeper down the stack to introspect CPU microarchitecture. In this blogpost we describe one such problem and the tools we used to solve it."

featured in #368


Where Exactly Does Python 3.11 Get Its ~25% Speedup?

- Beshr Kayali tl;dr: "Python 3.11 was released a few days ago and as usual, it comes with more than a few new features that make it ever more interesting, from exception groups and fine-grained error locations and tracebacks to TOML parsing support in the standard library and of course the much awaited speedup as part of the faster CPython project. CPython 3.11 is 25% faster than CPython 3.10 on average according to benchmarks with pyperformance."

featured in #367


Prioritizing App Stability - Mobile Performance @ Lyft

- Wen Zhao tl;dr: We focused our investment in mobile performance into the 3 metrics with the highest opportunity for improvement: (1) Time to interact: continuing reducing app startup time. (2) Stability: reducing the number of crashes any given user experiences. (3) Rendering performance: maintaining a high, buttery smooth frame rate. 

featured in #358


Why Your Website Should Be Under 14B In Size 

tl;dr: "Having a smaller website makes it load faster — that's not surprising. What is surprising is that a 14kB page can load much faster than a 15kB page — maybe 612ms faster — while the difference between a 15kB and a ;16kB page is trivial. This is because of the TCP slow start algorithm. This article will cover what that is, how it works, and why you should care. But first we'll quickly go over some of the basics."

featured in #346


Python 3.11 Is Up To 10 - 60% Faster Than Python 3.10

tl;dr: "CPython 3.11 is on average 25% faster than CPython 3.10 when measured with the pyperformance benchmark suite, and compiled with GCC on Ubuntu Linux. Depending on your workload, the speedup could be up to 10-60% faster. This project focuses on two major areas in Python: faster startup and faster runtime."

featured in #332


The Two Generals Problem

- Seth Archer Brown tl;dr: "Imagine there’s a city in a valley. On either side of the valley, there’s an army commanded by a general. On one side stands General Alice and her army. On the other, General Bob and his army. Alice and Bob want to capture the city, but neither has an army large enough to do so alone. Alice and Bob must attack at the same time, but Alice and Bob can only communicate by sending messengers through the valley, who have a chance of being captured by the city’s army. How do Alice and Bob coordinate their attack?"

featured in #319


How To Speed Up ClickHouse Queries Using Materialized Columns

- Karl-Aksel Puulmann tl;dr: ClickHouse supports speeding up queries using materialized columns to create new columns on the fly from existing data. In this post, I’ll walk through a query optimization example that's well-suited to this rarely-used feature.

featured in #307


The Fastest GIF Does Not Exist

- Ben Phelps tl;dr: "If you're here because you want to fix your GIF and want the quick answer, the solution is: set your frame delay to 20ms instead of 10ms. If you want to learn a bit more about GIFs, exactly why this edge case happens, and some thoughts on how to improve things, keep reading!"

featured in #293


Fixing Performance Regressions Before They Happen

tl;dr: “This post describes how the Netflix TVUI team implemented a robust strategy to quickly and easily detect performance anomalies before they are released — and often before they are even committed to the codebase.”

featured in #288