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Google DeepMind’s game-playing AI just found another way to make code faster [technologyreview.com]:

It has also found a way to speed up a key algorithm used in cryptography by 30%. These algorithms are among the most common building blocks in software. Small speed-ups can make a huge difference, cutting costs and saving energy.

“Moore’s Law is coming to an end, where chips are approaching their fundamental physical limits,” says Daniel Mankowitz, a research scientist at Google DeepMind. “We need to find new and innovative ways of optimizing computing.”

“It’s an interesting new approach,” says Peter Sanders, who studies the design and implementation of efficient algorithms at the Karlsruhe Institute of Technology in Germany and who was not involved in the work. “Sorting is still one of the most widely used subroutines in computing,” he says.

DeepMind published its results in Nature [springernature.com] today. But the techniques that AlphaDev discovered are already being used by millions of software developers. In January 2022, DeepMind submitted its new sorting algorithms to the organization that manages C++, one of the most popular programming languages in the world, and after two months of rigorous independent vetting, AlphaDev’s algorithms were added to the language. This was the first change to C++’s sorting algorithms in more than a decade and the first update ever to involve an algorithm discovered using AI.

DeepMind added its other new algorithms to Abseil, an open-source collection of prewritten C++ algorithms that can be used by anybody coding with C++. These cryptography algorithms compute numbers called hashes that can be used as unique IDs for any kind of data. DeepMind estimates that its new algorithms are now being used trillions of times a day.

AlphaDev is built on top of AlphaZero, the reinforcement-learning model that DeepMind trained to master games such as Go and chess. The company’s breakthrough was to treat the problem of finding a faster algorithm as a game and then train its AI to win it—the same approach it used last year to speed up matrix multiplications [technologyreview.com].

In AlphaDev’s case, the game involves choosing computer instructions and placing them in order so that the resulting lines of code make up an algorithm. AlphaDev wins the game if the algorithm is both correct and faster than existing ones. It sounds simple, but to play well, AlphaDev must search through an astronomical number of possible moves.

DeepMind chose to work with assembly, a programming language that can be used to give specific instructions for how to move numbers around on a computer chip. Few humans write in assembly; it is the language that code written in languages like C++ gets translated into before it is run. The advantage of assembly is that it allows algorithms to be broken down into fine-grained steps—a good starting point if you’re looking for shortcuts.

Journal Reference:
Mankowitz, Daniel J., Michi, Andrea, Zhernov, Anton, et al. Faster sorting algorithms discovered using deep reinforcement learning [open], Nature (DOI: 10.1038/s41586-023-06004-9 [doi.org])

Original Submission