metadata performance

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.

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.

A Google Tech Talk on Yahoo's 14 rules for faster web pages and the YSlow tool. It covers front-end practices like avoiding @import and fixing ETags, with live analysis of popular sites.

Dave Chinner's linux.conf.au 2012 talk on XFS scalability, covering pain points with large numbers of files and complex metadata, and the work to fix XFS metadata performance.

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.

Emery Berger's CppCon 2020 plenary on why performance is hard on modern hardware after the end of Dennard scaling. It covers benchmarking pitfalls and introduces new profiling tools for finding real bottlenecks.

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.

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.

Emery Berger argues that performance is hard to measure and deliver on modern systems, showing how code layout and link order can swamp optimization effects. He presents tools that evaluate performance more reliably.

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.

Journalist Matthew Cole's Black Hat 2013 talk on the CIA's operational security failures in the 2003 Milan kidnapping. Phone and location metadata exposed the team and led to arrest warrants.

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.

A 2009 Google Tech Talk by Nicholas Zakas on the hidden performance pitfalls of JavaScript. It covers what is fast and slow in the language and how to avoid common bottlenecks.

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.

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.

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.

Leptos creator Greg Johnston examines Rust/WebAssembly frontend frameworks against JavaScript frameworks using the JS framework benchmark. He argues that WASM is no longer too slow for DOM-heavy rendering.

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.

Charlie Marsh of Astral explains how uv, a Rust Python package manager, got so fast. He covers allocators, concurrency, zero-copy tricks and the challenges of Python packaging.

Robert Collins explains why rustup installs were painfully slow on Windows and how changes to rustup and Windows cut install time from 3m30s to 14s. The talk covers NTFS, small files and the cost of antivirus hooks on file close.

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.

Matthew Cole's Black Hat 2013 talk on operational security failures by CIA operatives. It covers the Abu Omar case and how sloppy tradecraft with cell phones exposed officers through call records and metadata.

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.

David Ditzel of Esperanto Technologies presents their massively parallel, energy-efficient RISC-V processors at the 7th RISC-V Workshop in 2017. It covers the high-performance many-core design aimed at machine learning.

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.

Allan Jude of Klara presents work on making ZFS scale on fast NVMe storage, covering fixes to the TaskQueue interface and removal of bottlenecks. The talk was given at EuroBSDcon 2022 and describes raising write throughput from about 3 to over 7 GB/sec.

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.

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.

Richard Feldman explains how pure functional languages can match or beat imperative ones on performance. Compilers can turn immutable code into in-place mutation, avoiding naive copying. The talk draws on his work on the Roc language.

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.

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.

Brian Cardarella's RailsConf 2016 talk on why and how teams can move from Rails to Elixir and Phoenix. It covers performance gains without losing productivity and how to introduce Phoenix in an organization.

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.

Alexis King's ZuriHac 2020 talk on how effect systems in Haskell can be implemented efficiently. It looks at the performance cost of effect libraries and how to reduce it.

Reggie Watts performs at TED 2012, blending vocal looping, beatboxing, poetry and comedy across musical genres in a larger-than-life improvised set.

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.

Swift compiler engineers explain how Swift implements generics using dictionary-passing with reified type metadata. They compare it with Java-style uniform representation and C++ or Rust monomorphization.