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.

Kaze Emanuar explains how he rewrote and optimized the Super Mario 64 source code so it runs far faster on real N64 hardware. He covers performance bottlenecks and fixes in detail for his ROM hacks.

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.

Casey Muratori's first refterm lecture lays out philosophies of optimization, distinguishing optimization from non-pessimization, which means avoiding needless work. It uses his fast terminal renderer project to show how fast computers really are.

Casey Muratori shows how typical 'clean code' object-oriented patterns can cost large factors in performance. He benchmarks them against simpler data-oriented code.

Casey Muratori's University of Twente guest lecture is a case study in how straightforward code can cut thousands of lines and tens of seconds of runtime down to a few dozen lines running in under a second. It uses concrete examples to show why software is often an order of magnitude slower than it should be.

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.

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.

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.

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.

Matt Parker follows the community effort to speed up his slow Wordle-solving code, which ended up running hundreds of millions of times faster. He walks through the techniques contributors used.

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.

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.

Matthias Wandel tests old and new code on a range of PCs, including a new mini PC, to measure how much faster modern hardware really is. He also shows optimization techniques such as branchless code, with benchmark programs on GitHub.

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.

Casey Muratori shows how fast a terminal renderer can run, building a simple reference renderer that handles Unicode and colored text far quicker than Windows Terminal. He argues that common excuses for slow terminals do not hold up.

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.

Casey Muratori responds on-stream to a dismissive Twitter post by demonstrating how slow Visual Studio's startup and debugger watch window are, comparing it to much older software and machines. It is a long rant about software performance and bloat.

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.

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.

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.

The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.

Kaze Emanuar searches for the best sine approximation for the Nintendo 64, comparing the lookup tables in Mario 64 and Ocarina of Time with polynomial alternatives. It walks through numerics and 3rd, 4th and 5th order approximations.

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.

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.

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.

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.

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.