Hi, I’m Tony Albrecht, an engineer on League. I’m back with some more performance goodness. In the previous article, we tracked a slowdown reported in an LCS game and an issue with Swain that was first reported by players. For this article, we’re stepping back a little further and looking at how we can measure the aggregate performance of League over an entire region, notice a dip, and then narrow down the cause. In this case, we chased a 2ms per frame drop in performance to a single missing ASCII character in our code.
Articles tagged: performance
Hey there. I’m Stephen “Riot FloofyRice” Zhang and I’m a technical developer in QA on the Content Efficiency team, which focuses on building internal tools. I want to talk about our efforts to clean up one of our bigger problems at Riot: data debt.
Hi, I’m Tony Albrecht, an engineer on League. You might remember me from such articles as Profiling: Measurement and Analysis and Profiling: Optimisation where we looked at how we find and optimise performance bottlenecks in the LoL code base. In this article, we’re going to take a step back and look at how we detect and then fix real world performance issues that slip out past our QA and monitoring systems and escape into the wild to plague you, The Player.
Hi, I’m Tony Albrecht, an engineer at Riot, and I’m a performance junkie. This is the second part in a series on how to optimise your code. In Part 1, Measurement and Analysis, we learned how to find and analyse performance bottlenecks in our sample code. We surmised that the issue was due to slow memory access. In this article we’ll look at how we can reduce the cost of memory accesses and thereby speed up our program.
Hi, I’m Tony Albrecht, an engineer at Riot, and I like profiling and optimising things. In this article, I’m going to walk you through profiler basics and analyse some example C++ code as we profile it on a Windows machine. We’ll start at a very high level and delve deeper and deeper into the bowels of the CPU step by step. Once we identify opportunities for optimisation in this article, we’ll implement changes and explore some real-world examples from the League of Legends codebase in a future article. Let’s begin!
Hi, I’m Paul Geerts and I’m one of the engineers on League’s Render Strike Team. A while back my buddy Tony posted an article about how League of Legends currently builds a single frame of the action. Today, I’d like to dive into a component of the rendering pipeline that we’re actively improving: materials. I’ll discuss some of the changes we’ve made and why they’re important for players and developers alike.
With the current scale of a game like League of Legends, it can be hard to remember the humble beginnings: a small group of developers too busy shipping a game and putting out fires to think about fine tuning systems, pipelines, and processes. And while we’ve changed a lot, our priorities remain the same: we’ll always put player experience before tech and process. Sometimes that leads to tech debt, and as we grow, it's important that we look for ways to improve the quality of our work as well as the way we work. Not every step forward has to be revolutionary.
Game developers working on a continuously evolving product like League of Legends are constantly battling against the forces of entropy as they add more and more content into unfortunately finite machines. New content carries implicit cost—not just implementation cost but also memory and performance cost created by more textures, simulation, and processing. If we ignore (or miscalculate) that cost then performance degrades and League becomes less enjoyable. Hitches suck. Lag is infuriating. Frame rate drop is frustrating.
League of Legends has more than 125 champions, each with their own set of unique animations. (One of my faves? Sion’s dance, shown below—just one of his 38 animations.) These movements help to bring the champions to life: from determined movement to powerful spell casting to tragic deaths. (I see that last one too often.) As we’ve continued to introduce and rework champions, the total amount of animation data has become a larger burden on resources like run-time memory, patch sizes, and storage space.