Tacit knowledge/mechanical sympathy

“Welcome to the Jungle” (CPU design and Moore’s law; cf. “How Many Computers In Your Computer?”)

“There’s plenty of room at the Top: What will drive computer performance after Moore’s law?”, Leiserson et al 2020 (gains from End-To-End Principle-based systems design¹)

“Scalability! But at what COST?”, McSherry et al 2015 (when laptops > clusters: the importance of good baselines; “Big Data” doesn’t fit in a laptop); “Command-line Tools can be 235× Faster than your Hadoop Cluster”

“What it takes to run Stack Overflow”, Nick Craver; “We stand to save $7m over 5 years from our cloud exit”, David Heinemeier Hansson; “Production Twitter on One Machine? 100Gbps NICs and NVMe are fast”

“Anatomy of a hack: How crackers ransack passwords like qeadzcwrsfxv1331; For Ars, 3 crackers have at 16,000+ hashed passcodes—with 90% success”; “Computing Adler32 Checksums at 41 GB/s”

“The Science of Brute Force”

“Bracket pair colorization 10,000× faster”

“Parsing Gigabytes of JSON per Second”, Langdale & Lemire2019

59 GB/s FizzBuzz; “Modern storage is plenty fast. It is the APIs that are bad.”; “10M IOPS, one physical core. #io_uring #linux”; “Achieving 11M IOPS & 66 GB/s IO on a Single ThreadRipper Workstation”

“The C10K problem”, Kegel2001; “Serving Netflix Video at 50 gigabyte/s on FreeBSD”, Gallatin2021/“Serving Netflix Video Traffic at 100 gigabyte/s and Beyond”, Gallatin2022

“Multicast and the Markets” (network optimization & mechanical sympathy in high-frequency trading)

“True Stories of Algorithmic Improvement”

Ultra-low bit-rate audio for tiny podcasts with Codec2 and WaveNet decoders (1 hour = 1MB)

“The computers are fast, but you don’t know it” (replacing slow pandas with 6,000× faster parallelized C++)

“Scaling SQLite to 4M QPS on a Single Server (EC2 vs Bare Metal)”, Expensify; “50% faster than 3.7.17” (“… We have achieved this by incorporating hundreds of micro-optimizations. Each micro-optimization might improve the performance by as little as 0.05%.”)

“Optimization story—quantum mechanics simulation speedup”; “14,000× Speedup a.k.a. Computer Science For the Win”

“Cutting The Pipe: Achieving Sub-Second Iteration Times”, Frykholm2012 (optimizing game development)

“The Billion Row Challenge (1BRC): Step-by-step in Java from 71s to 1.7s” (and 4s in Go & 0.5s in C)

“GCC Compiler vs Human—119× Faster Assembly 💻🆚🧑‍💻”; “Python, C, Assembly—2,500× Faster Cosine Similarity 📐”; “Summing ASCII encoded integers on Haswell at almost the speed of memcpy” (“320× faster than the following naive C++ solution”)

“The Prospero Challenge”(rendering a vector graphic toy language; 15,000ms simple implementation → 3,750ms → 6.3ms → 3.9ms)

Demoscene:

• “8088 Domination”: “how I created 8088 Domination, which is a program that displays fairly decent full-motion video on a 1981_44ya IBM PC”/part 2

• “A Mind Is Born” (256 bytes; for comparison, this line of Markdown linking the demo is 194 bytes long, and the video several million bytes)

Casey Muratori: refterm v2, Witness pathing (summary)

Algorithmic Progress:

• “Algorithmic Progress in 6 Domains”, Grace2013

• “A Time Leap Challenge for SAT Solving”, Fichte et al 2020 (hardware overhang: ~2.5× performance increase in SAT solving since 2000_25ya, about equally due to software & hardware gains, although slightly more software—new software on old hardware beats old on new.)

• 2019 Unlocking of the LCS35 Challenge

• “Measuring hardware overhang”, hippke (“with today’s algorithms, computers would have beat the world chess champion already in 1994_31ya on a contemporary desk computer”)

Memory Locality:

• “What Every Programmer Should Know About Memory”, Drepper2007

• “You’re Doing It Wrong: Think you’ve mastered the art of server performance? Think again.”, Poul-Henning Kamp 2010_15ya (on using cache-oblivious B-heaps to optimize Varnish performance 10×)

• “The LMAX Architecture”, Fowler2011 (TigerBeetle)

• “5000× faster CRDTs: An Adventure in Optimization”

• “In-place Superscalar Samplesort (IPS⁴o): Engineering In-place (Shared-memory) Sorting Algorithms”, Axtmann et al 2020; “Vectorized and performance-portable Quicksort”, Blacher et al 2022

• “Fast approximate string matching with large edit distances in Big Data (2015)”

• “ParPaRaw: Massively Parallel Parsing of [CSV] Delimiter-Separated Raw Data”, Stehl & Jacobsen2019

• “Scaling our Spreadsheet Engine from Thousands to Billions of Cells: From Maps To Arrays”, Lukas Köbis2022

• “Persistent RNNs: Stashing Recurrent Weights On-Chip”, Diamos et al 2016; “FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness”, Dao et al 2022

• Algorithms for Modern Hardware

• Solving Problem 14 with Fast Dynamic Programming

DL/Deep Reinforcement Learning:

• Training A3C to solve Atari Pong in <4 minutes on the ARCHER supercomputer through brute parallelism

• “megastep helps you build 1-million FPS reinforcement learning environments on a single GPU”, Jones

• “The Mathematics of 2048: Optimal Play with Markov Decision Processes”

• Bruteforcing Breakout (optimal play by depth-first search of an approximate MDP in an optimized C++ simulator for 6 CPU-years; crazy gameplay—like chess endgame tables, a glimpse of superintelligence)

• “Sample Factory: Egocentric 3D Control from Pixels at 100,000 FPS with Asynchronous Reinforcement Learning”, Petrenko et al 2020; “Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko et al 2021; “Brax: A Differentiable Physics Engine for Large Scale Rigid Body Simulation”, Freeman et al 2021; “Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning”, Makoviychuk et al 2021 (Rudin et al 2021); WarpDrive

• “Large Batch Simulation for Deep Reinforcement Learning”, Shacklett et al 2021

• “Scaling Scaling Laws with Board Games”, Jones2021

• “Mastering Real-Time Strategy Games with Deep Reinforcement Learning: Mere Mortal Edition”, Winter

• “Scaling down Deep Learning”, Greydanus2020

• “Podracer architectures for scalable Reinforcement Learning”, Hessel et al 2021 (highly-efficient TPU pod use: eg. solving Pong in <1min at 43 million FPS on a TPU-2048); “Q-DAX: Accelerated Quality-Diversity for Robotics through Massive Parallelism”, Lim et al 2022; “EvoJAX: Hardware-Accelerated Neuroevolution”, Tang et al 2022; “JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023

• “Training the Ant simulating at 1 million steps per second on CUDA (NVIDIA RTX 2080), using Tiny Differentiable Simulator and C++ Augmented Random Search.” (Erwin Coumans2021)

• “This Snake environment simulates 14M snakes and trains 1M snake steps per second.”

• “TextWorldExpress: Simulating Text Games at One Million Steps Per Second”, Jansen & Côté2022

• “Getting the World Record in HATETRIS”, Dave & Filipe2022

• “NAVIX: Scaling MiniGrid Environments with JAX”

• “Parallel Q-Learning (PQL): Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation”, Li et al 2023

Latency/UI:

• “It’s the Latency, Stupid.”

• “Transatlantic ping faster than sending a pixel to the screen?”

• “Computer latency: 1977^–₄₀2017”

• “Keyboard Latency”

• “Terminal Latency”; improving GNOME terminal

• “Local-first software: You own your data, in spite of the cloud”/Kleppmann et al 2019

• “Web Bloat”²

• “Project Starline: A high-fidelity telepresence system”, Lawrence et al 2021

• “Use GPT-3 incorrectly: reduce costs 40× and increase speed by 5×”, Pullen2023

• HFT: “The accidental HFT firm”

• Shenzhen

Organizations:

• WhatsApp (2013_12ya: “50 staff members” for 200m users; 2015: 50 “engineers”, 900m users)

• Craigslist (2017: ~50 employees)

• Instagram (2012_13ya FB acquisition: 13 employees; architecture)

• YouTube (2006_19ya Google acquisition: ~10 employees)

• Reddit (2011_14ya: 4 engineers; 2021: ~700 total staff, >52m users)

• Wikimedia Foundation (“Internet hosting” in 2021: $2.98^$2.4₂₀₂₁m)

• Twitter: Musk acquisition cut up to 90% of employees+contractors; widespread confident assertions by ex-Twitter staff that the site would suffer crashes (possibly terminal crashes which could not be recovered from), within a month or two, turned out to be false.

• See Also: “Some examples of people quickly accomplishing ambitious things together.”