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The Los Alamos National Lab (LANL) has installed a supercomputer testbed built from a cluster of 750 Raspberry Pis, which could grow to 10,000 Pi boards next year.
A huge cluster of cool-running Raspberry Pi boards has given LANL the answer to a unique challenge faced by people who develop software for 'exascale' supercomputers, such as LANL's giant Cray Trinity, one of the world's top 10 fastest supercomputers.
Software developers have little time to test their software on these high-cost systems because they're fully occupied running many trillions of calculations for actual scientific research.
LANL hasn't revealed the exact cost of the Raspberry Pi cluster, but it suggests it is significantly cheaper and more power-efficient than the alternatives.
For the past seven years LANL has been using older and retired machines with lots of nodes for software development and testing. However, it didn't scale to a Trinity-sized environment, which has 20,000 nodes. It was expensive to run, requiring water towers for cooling and other equipment.
According to Gary Grider, head of its LANL's HPC division, the new Raspberry Pi cluster can offer the same testing capabilities as a dedicated testbed, which could cost $250m and use 25MW of energy.
To buy 750 Raspberry Pi boards at $25 a piece would cost just under $19,000, though that figure is unlikely to reflect the actual cost of the setup. Grider highlights power-efficiency benefits too and estimates that each board in a several thousand node Pi-based system would use 2W to 3W.
The current 3,000-core Pi cluster is a pilot, and LANL intends to boost this setup to 40,000 cores next year, according to the Raspberry Pi Foundation. That increase would mean a cluster of around 10,000 Raspberry Pi boards.
The pilot cluster was built by Australian developer BitScope and distributed by US firm Sicorp. It's constructed out of five rack-mounted Pi Cluster Modules, which consist of 150 four-core Raspberry Pi ARM-based boards. This totals 750 CPUs, representing 3,000 cores.
NANL believes the Pi cluster has applications beyond HPC software development, including better simulation of large-scale sensor networks and HPC network topology research to improve production performance.
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