Reza Zadeh created SPARK-6567:
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Summary: Large linear model parallelism via a join and reduceByKey
Key: SPARK-6567
URL: https://issues.apache.org/jira/browse/SPARK-6567
Project: Spark
Issue Type: Improvement
Components: ML, MLlib
Reporter: Reza Zadeh
To train a linear model, each training point in the training set needs its dot
product computed against the model, per iteration. If the model is large (too
large to fit in memory on a single machine) then SPARK-4590 proposes using
parameter server.
There is an easier way to achieve this without parameter servers. In
particular, if the data is held as a BlockMatrix and the model as an RDD, then
each block can be joined with the relevant part of the model, followed by a
reduceByKey to compute the dot products.
This obviates the need for a parameter server, at least for linear models.
However, it's unclear how it compares performance-wise to parameter servers.
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