I first pointed to Jubatus here.
The presentation reviews some impressive performance numbers and one technique that merits special mention.
Intermediate results are shared among the servers during processing to improve their accuracy. That may be common in distributed machine learning systems but it was the first mention I have encountered.
In parallel processing of topic maps, has anyone considered sharing merging information across servers?