performance optimization

Chandler Carruth's CppCon 2014 talk on writing fast C++, explaining why data structure choice and memory layout matter more than clever algorithms. It covers why standard containers like unordered_map perform poorly on modern CPUs.

Emery Berger explains why performance is hard to measure and deliver on modern hardware, showing how code layout and alignment can swamp optimization effects. He presents Stabilizer for statistically sound evaluation and Coz, a causal profiler.

A Netflix performance engineer presents how Netflix tunes EC2 instances, covering instance type choice, Xen modes, SR-IOV and kernel tuning. The goal is near bare-metal performance in the cloud.

Malte Skarupke's C++Now 2018 talk on hash table performance, explaining why std::unordered_map is slow and memory hungry and presenting faster designs, including his bytell hash map.

Andrei Alexandrescu's ACCU 2016 talk previews material from his book on writing fast code, with a focus on optimization techniques such as sentinels and faster search.

Optiver's Carl Cook explains how to build high-frequency trading systems in C++, where the critical path runs rarely but must be extremely fast. He covers techniques for low and predictable latency, including avoiding main memory and cache misses.

David Schachter describes how he made a data-collection Python program run 114,000 times faster, drawing on decades of experience. The talk covers pipelining, cache effects and other optimization lessons.

Andrei Alexandrescu's CppCon 2019 talk revisits sorting, showing how branchless tricks, hybrid strategies and a cost metric can speed up std::sort-style algorithms on modern hardware.

Alex Russell's Chrome Dev Summit 2016 talk on building web experiences that work on slow, flaky networks and low-power devices. It argues developers should focus on the performance of real-world mobile hardware rather than desktop machines.

Nicholas Ormrod's CppCon 2016 talk on how Facebook optimizes std::string with its open-source replacement fbstring. It covers storage layout tradeoffs, small string optimization and memory allocation behavior.

Matt Godbolt, creator of Compiler Explorer, tours surprising optimizations modern compilers perform, such as turning loops into closed-form sums and using popcount instructions. He explores what the compiler does behind the scenes.

Chandler Carruth walks through tuning C++ performance, from writing benchmarks to analyzing results on real CPUs and compilers. It focuses on small-scale problems such as loop kernels, data structures and algorithms, and on keeping compilers from optimizing benchmarks away.

Cliff Click walks through a small performance example on a modern out-of-order CPU. He covers pipelining, ILP, CISC vs RISC, multicore, and why cache misses and the memory subsystem dominate performance.

Chandler Carruth's CppNow 2013 keynote on how C++ builds complex software from simple emergent structures, and how compilers optimize them through inlining and value semantics.

Chandler Carruth's CppCon 2016 talk on designing hybrid data structures for performance. He covers cache locality, working-set size and allocation overhead, and why standard C++ containers fall short.

Andrew Adams, a Halide developer, presents the Halide language for image processing and tensor computation. The talk explains the separation of algorithm from schedule with illustrated examples of parallelism, locality and redundancy tradeoffs.

Aaron Hsu shows how to manipulate and transform tree data in APL using arrays and inverted tables instead of pointers. He claims much smaller code, better performance and easier parallelisation.

Mike Acton of Insomniac Games argues that a program's purpose is transforming data, and shows how common C++ approaches work against that in performance-critical game development. He covers cache behavior, data layout and the limits of compilers.

Chandler Carruth shows how to squeeze more performance out of already-optimized C++ code, covering how the CPU executes it. The talk goes into pipelining, branch prediction and speculative execution using benchmarks.

Jack Moffitt presents the architecture of Servo, Mozilla's experimental browser engine written in Rust, focusing on parallelism across cores, GPUs and SIMD and the safety properties gained from Rust.

Jan Vitek of Northeastern University gives a meetup talk on the challenges of making the R language run fast. He explains what in R's semantics makes it hard to optimize and to build a JIT compiler for.

A CppCon 2018 talk arguing that OOP has failed to deliver on reuse and performance, and presenting data-oriented design for C++ with practical worked examples from real systems.

Matt Godbolt builds a toy path tracer in C++ in three different styles and compares them on clarity, testability, compile time and run-time performance. The talk was given at CppCon 2019.

Aaron Patterson's RailsConf 2011 talk on Rails 3.1 performance, showing why deeper Rack middleware stacks slow Rails and how a shallower pipeline helps. It also touches on Rack API design.

Ilya Grigorik of Google's Make the Web Fast team explains how to build a mobile site that appears on screen within one second. He covers the network and rendering constraints on mobile.

Cliff Click, author of HotSpot's JIT compiler, shares hard-won advice for people writing virtual machines at the VMSS16 workshop. Topics include interpreters, JITs and performance lessons.

Chandler Carruth's CppCon 2016 talk tries to defuse the heated debate over undefined behavior in C and C++. He explains from the compiler writer's perspective why UB exists and how it enables optimization.

Scott Meyers explains how CPU caches work and why they matter for performance, covering cache levels, access speeds, associativity, and effects in multithreaded code. The focus is C++ and cache-aware, data-oriented programming.

A Google I/O 2013 talk by Android audio engineers on why reliable, low-latency audio is hard on the platform. It covers problem areas across the stack, platform work underway and techniques apps can use.

Steven G. Johnson, MIT professor and FFTW co-author, gives a JuliaCon 2019 keynote on metaprogramming and staged programming in Julia, showing how it enables high-performance numerical code and when not to use macros.

Matt Kulukundis reviews two years of Abseil's Swiss table hashtable, covering post-release optimizations, lessons from rolling it out across Google, design choices, and what global monitoring revealed.

Jafar Husain of Netflix explains at React.js Conf 2015 how Netflix runs React-style rendering without the DOM on smart TVs and game consoles. It uses a canvas-like API for performance.

Nick Caston compares Unity's sample ECS/DOTS project (AngryBots) with his own from-scratch C++ and OpenGL clone. He benchmarks performance and discusses the effort involved in each approach.

A short side-by-side recording by Jake Archibald of the same animation demo running in Chrome, Firefox, Safari and Servo WebRender nightlies on a 60Hz display. It shows the smoothness difference of GPU-based rendering.

Ryan Florence's React.js Conf 2015 talk pushes React into unusual use cases, including a demo comparing rendering in React, Ember and Angular.

Armin Ronacher, creator of Flask, explains how CPython's internals and C API leak into the language's semantics. Using examples like the complexity of the add operation, he shows why Python is hard to optimize or reimplement.

Rick Reed of WhatsApp explains at Erlang Factory 2014 how the service scaled to billions of messages with a very small engineering team. He covers the FreeBSD and Erlang stack, Mnesia usage, server counts and bottleneck work.

Gil Tene of Azul Systems explains common pitfalls in measuring latency and response time, including why averages mislead and the problem of coordinated omission. He introduces open source tools like HdrHistogram for better measurement and reporting.

Chandler Carruth argues in a CppCon 2019 talk that C++ has no truly zero-cost abstractions. Using examples like unique_ptr, ABI constraints and compile-time costs, he shows that abstractions cost runtime, compile time or human effort.

Larry Hastings explains what Python's GIL is, what problems it solves and how it works, and presents his Gilectomy project to remove it, including the performance and C-extension obstacles.