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Thread: Calxeda EnergyCore ECX-1000 ARM Server

  1. #11
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    Quote Originally Posted by sturmflut View Post
    Comparing the results to http://openbenchmarking.org/result/1...RA-INTELATOM04, it looks like this quad-core ARM at 1.4 GHz can keep up with an Intel Atom D525 at 1.8 GHz. So the flagship quad-core ARM product ist equivalent to a dual-core Atom from 2010, the Atom running at a slightly higher clock frequency.
    Comparing a low-end/low-power desktop board that has a GPU, WiFi, etc. with this low-power server board which includes support for things like ECC memory and built-in IPMI probably isn't very useful...

    What you really want to benchmark is how this ARM system (and especially a number of them working together) fares doing several typical server tasks, and compare that to other systems/clusters doing the same. And take TCO, including space savings & power consumption, into account while doing so.

    Because clustering a whole bunch of these to work together is what they are supposedly good at:
    http://www.calxeda.com/technology/architecture/fabric/
    That's an on-chip network switch with 8 ports (3 internal, 5 external) that can handle a total capacity of up to 80 Gbit/s, or up to 10 Gbit/s per channel, which can also automatically slow down (to save energy) depending on the current requirements. To compare: a standalone ethernet switch which can handle speeds like that would consume more than 5W on its own (and also require a lot more space, of course).

  2. #12
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    Quote Originally Posted by cb88 View Post
    32bit isn't all that big of a hit if you are using this for webhosting... if you were using it for serving out databases and such it probably would be since those are going to be geard toward big iron thats already 64bit an example being ZFS
    Depends on what type of database...

    You might be right when you talk about a big SQL database that because of its structure doesn't lend itself well to sharding or similar techniques. OTOH, databases like CouchDB and Hadoop might do pretty well, spreading over more nodes as the data grows.

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