memory performance

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.

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.

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 2019 talk introducing Mesh, a drop-in malloc replacement that compacts memory in unmodified C and C++ programs. It tackles fragmentation by merging pages without changing pointers.

Chandler Carruth's CppNow 2023 talk describes Carbon as a successor language to C++ rather than a superset, focusing on C++ interop and a path to memory safety in large existing codebases. It explains why Carbon takes a different approach from Cpp2 and Circle.

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.

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 Zig SHOWTIME talk by Benjamin Feng on what a memory allocator is, followed by an interview. It covers allocation strategies and different allocator types in Zig.

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.

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.

Herb Sutter's deep C++ and Beyond 2012 talk on atomics and the C++ memory model, covering why programmers chasing top performance need to understand hardware ordering. This is part one of two.

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.

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.

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.

Fedor Pikus's CppCon 2017 talk on C++ atomics, looking at what they let the programmer express and what the hardware actually does. It covers memory ordering and lock-free programming from basic to advanced.

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.

John McCall of Apple's Swift team presents at CppNow 2023 on introducing a memory-safe successor language into large C++ code bases. He argues incremental improvement of C++ hits hard limits and explains Swift's C++ interoperability for gradual migration.

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.

Part two of Herb Sutter's C++ and Beyond 2012 session on atomic weapons, a deep dive into the C++ memory model, atomics, memory ordering, and hardware behavior. It ends with the modern meaning of volatile.

RustConf 2020 closing keynote by Sian Griffin that dives into Pokemon Blue glitches, notably Missingno and arbitrary-code-execution tricks. It uses them to explain how memory unsafety works and how memory-safe languages like Rust prevent such bugs.

In this TED talk, Nobel laureate Daniel Kahneman distinguishes the experiencing self from the remembering self, using examples such as vacations and colonoscopies. He explains how the two perceive happiness differently and what that means for economics, policy and self-awareness.

Ahmon Dancy of Franz Inc explains low-level programming in Common Lisp, including off-heap data structures used in AllegroGraph to manage memory outside the garbage collector.

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.

Bobby Powers presents Mesh, a drop-in malloc replacement that eliminates memory fragmentation in unmodified C/C++ programs by compacting memory. The talk covers how it works despite C's fixed object addresses, and its benefits for programs like Redis.

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.

Gor Nishanov's CppCon 2018 talk on using tiny C++ coroutines to hide memory latency, interleaving useful work while prefetches bring cache lines in from main memory.

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.

Casey Muratori argues against smart pointers and RAII, advocating grouped-element thinking, zero-is-initialization and stubs as better ways to structure memory and handle errors.

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.

Kiran Pamnany's Cambridge Julia Meetup talk on Julia's shared memory parallelism and its new task scheduler. It argues that fork-join and work-stealing models nest poorly and presents depth-first scheduling for composable parallelism.

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.

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.

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.