How can we measure a potential AI or hardware overhang? For the problem of chess, modern algorithms gained two orders of magnitude in compute (or ten years in time) compared to older versions. While it took the supercomputer “Deep Blue” to win over world champion Garry Kasparov in 1997, today’s Stockfish program achieves the same ELO level on a 486-DX4-100 MHz from 1994. In contrast, the scaling of neural network chess algorithms to slower hardware is worse (and more difficult to implement) compared to classical algorithms. Similarly, future algorithms will likely be able to better leverage today’s hardware by 2–3 orders of magnitude. I would be interested in extending this scaling relation to AI problems other than chess to check its universality.
…We may wonder: How do modern (better) chess algorithms perform on slower hardware? I tested this with Stockfish version 8 (SF8), one of the strongest classical chess engine. I simulated 10k matches of SF8 against slower versions of itself and a series of older engines for calibration, using cutechess-cli. In these benchmarks, I varied the total number of nodes to be searched during each game. I kept the RAM constant (this may be unrealistic for very old machines, see below). By assuming a fixed thinking time per game, the experiments scale out to slower machines. By cross-correlating various old benchmarks of Stockfish and other engines on older machines, I matched these ratings to units of MIPS; and finally, MIPS ~to the calendar year. Depending on the actual release dates of the processors, the year axis has a jitter up to 2 years. I estimate the error for the compute estimates to be perhaps 20%, and certainly less than 50%. As we will see, the results measure in orders of magnitude, so that these errors are small in comparison (<10%).
Results: SF8 achieves Kasparov’s 2850 ELOs running on a 486-100 MHz introduced in 1994, three years before the Kasparov-Deep Blue match. These ELOs refer to tournament conditions as in the 1997 IBM games. In other words, with today’s algorithms, computers would have beat the world chess champion already in 1994 on a contemporary desk computer (not a supercomputer). [Followup: “A closer look at chess scalings (into the past)”/“Benchmarking an old chess engine on new hardware”: “How much more compute does Stockfish 3 require to match Stockfish 13? Answer: 32× (uncertainty: 30–35×)…Interpretation: If we accept SF as amongst the very best chess programs in the last decade, we can make a more general assessment of chess compute vs. algorithm. Compute explains 30–50% of the computer chess ELO progress; algorithm improvements explain 50–70%.”]
