Google is the modern data poster-child for parallel computing. It’s famous for splintering enormous calculations into tiny pieces that can then be processed across an epic network of machines. But when it comes to spreading workloads across multi-core processors, the company has called for a certain amount of restraint.

With a paper (PDF) soon to be published in IEEE Micro, the IEEE magazine of chip and silicon design, Google Senior Vice President of Operations Urs Hölzle – one of the brains overseeing the web giant’s famous back-end – warns against the use of multi-core processors that take parallelization too far. Chips that spread workloads across more energy-efficient but slower cores, he says, may not be preferable to chips with faster but power-hungry cores.

Hölzle sees this as the battle of the “wimpy” cores and the “brawny” cores.

“Slower but energy efficient ‘wimpy’ cores only win for general workloads if their single-core speed is reasonably close to that of mid-range ‘brawny’ cores,” he says. The problem, he explains, is that wimpy cores run into Amdahl’s law (PDF). In essence, Amdahl’s law says that when you parallelize only part of a system, there is a limit to performance improvement.

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