The technology of artificial intelligence has become so prevalent in even the most complex domains of science that it now has its own suite of tests to measure its computing time on the world's most powerful computers.
MLPerf, the industry consortium that serves the computer industry by measuring how long it takes to run machine learning, a subset of artificial intelligence, on Wednesday offered an inaugural suite of test results[1] for high-performance computing, or HPC, systems running the machine learning tasks.
The test results, submitted by a variety of research labs, include results for the world's fastest computer, Fugaku.
The effort reflects the fact that supercomputers are more and more incorporating deep learning forms of AI into their calculation of traditional scientific problems.
"We saw an omission in that we didn't have more scientifically-oriented workloads at a time when people are beginning to look at training as potentially an HPC workload, or coupled to, or a component of them," said David Kanter, the head of MLPerf, in a briefing with reporters.
The new results join two existing benchmark test results, one that measures training on ordinary server systems[2], and one that measures machine learning inference[3], making predictions, on servers and on mobile devices.
Also: Nvidia makes a clean sweep of MLPerf predictions benchmark for artificial intelligence[4]
The MLPerf staff that designed the tests are hosting a session Wednesday afternoon to