Malik says there are more practical ways of addressing the problem of “hot data” energy consumption in the near term. “One important area is improving infrastructure efficiency, for example through more energy-efficient processors and advanced cooling techniques such as liquid cooling or free-air cooling,” she says. 

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At the same time, she adds, there is “growing recognition that efficiency must also be addressed at the software and workload level, not just the infrastructure level”.

“In high-performance and cloud computing, performance has traditionally been the dominant metric, but energy efficiency needs to be treated as equally important,” Malik says. “This means designing algorithms and applications that are energy aware.” It also means using the appropriate amount of computing power for the task in hand, she says. “Not every task needs the largest possible AI model or the fastest possible runtime.”

But in the face of exponentially accumulating data, a different kind of radical rethink may also be required, says Malik. Do we really need all of the data that we produce? Increasingly, part of the solution, she says, “is being more intentional about what we choose to keep”.

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