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Measuring hardware overhang
Peter Jin*22

Thanks for writing this post. I have a handful of quick questions: (a) What was the reference MIPS (or the corresponding CPU) you used for the c. 2019-2020 data point? (b) What was the constant amount of RAM you used to run Stockfish? (c) Do I correctly understand that the Stockfish-to-MIPS comparison is based on the equation [edit: not sure how to best format this LaTeX...]:

So, your post piqued my interest to investigate the Intel 80486 a bit more with the question in mind... (read more)

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3hippke
(a) The most recent data points are from CCRL. They use an i7-4770k and the listed tournament conditions. With this setup, SF11 has about 3500 ELOs. That's what I used as the baseline to calibrate my own machine (an i7-7700k). (b) I used the SF8 default which is 1 GB. (c) Yes. However, the hardware details (RAM, memory bandwidth) are not all that important. You can use these SF9 benchmarks on various CPUs. For example, the AMD Ryzen 1800 is listed with 304,510 MIPS and gets 14,377,000 nodes/sec on Stockfish (i.e., 19.9 nodes per MIPS). The oldest CPU in the list, the Pentium-150 has 282 MIPS and reaches 5,626 nodes/sec (i.e., 47.2 nodes per MIPS). That's about a factor of two difference, due to memory and related advantages. As we're getting that much every 18 months due to Moore's law, it's a small (but relevant) detail, and decreases the hardware overhang slightly. Thanks for bringing that up! Giving Stockfish more memory also helps, but not a lot. Also, you can't give 128 GB of RAM to a 486 CPU. The 1 GB is probably already stretching it. Another small detail which reduces the overhang by likely less than one year. There are a few more subtle details like endgame databases. Back then, these were small, constrained by disk space limitations. Today, we have 7-stone endgame databases through the cloud (they weigh in at 140 TB). That seems to be worth about 50 ELO.
What are the most important papers/post/resources to read to understand more of GPT-3?
Answer by Peter Jin60

nostalgebraist's blog is a must-read regarding GPT-x, including GPT-3. Perhaps, start here ("the transformer... 'explained'?"), which helps to contextualize GPT-x within the history of machine learning.

(Though, I should note that nostalgebraist holds a contrarian "bearish" position on GPT-3 in particular; for the "bullish" case instead, read Gwern.)

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2Adam Shimi
Thanks for the answer! I knew about the "transformer explained" post, but I was not aware of its author's position on GPT-3.