12-Core ARM Cluster Benchmarked Against Intel Atom, Ivy Bridge, AMD Fusion

The purpose of Effimaß basically just comes down to having a low-power ARM cluster for some interesting benchmark results, using it as a platform for porting new benchmarks to ARM, bringing new MPI cluster capabilities to the Phoronix Test Suite framework, and making other related improvements for ARM and cluster computing within the open-source Phoronix benchmarking stack.

Before getting any further, thanks go out to Texas Instruments and SVTronics for supporting this cluster by providing the PandaBoard ES development boards at a discount to make it even more affordable. The PandaBoard ES currently retails for about $182 USD, so when everything is said and done, the cluster carries a retail price of around $1,200 USD.

In the Phoronix office tour it was shown where Effimaß was going to be setup, but due short CAT5 cables interfering, for now the cluster is running on a standalone cart and is attached to a 48-port enterprise-grade network switch. The network switch power consumption wasn't monitored as part of the power monitoring since eventually the cluster will move back to its intended location where it will be tapping an already present 24-port enterprise-grade network switch and thus not lead to any net increase in power draw.

As far as the Effimaß name for this 12-core ARM cluster, from the Bavarian who named it, her reasoning came down to: "1. Effizienz for efficiency, in terms of the low-power ARM cores. 2. Maß: basically the measure of all things." So how efficient is the cluster at the moment? While tweaking has only just begun and more tests are still being conducted, the six PandaBoard ES cluster is idling at 15~16 Watts, under load is averaging about 29 Watts, and the peak power consumption I have seen under load is 31 Watts. This is while delivering some rather surprising numbers. I was expecting the 10/100 Ethernet and/or the SDHC-backed storage to be the main bottleneck for the Ubuntu Linux cluster, but that did not actually seem to be the case, at least with the MPI workloads tested thus far.