The test I was referring to was the included KMeans algorithm, which uses NumPy for PySpark but can be done without jBlas in scala, so it's more testing basic performance, not matrix libraries.
I can certainly try the ALS test, though note that the scala example you pointed to uses Colt, whereas most of MlLib at this point uses jBlas, so probably most relevant to compare to something using jBlas (or simply rewrite that example to use jBlas). I basically agree with Evan that if you're only using matrices, and not the richer features of SciPy/NumPy, scala is the way to go, but I'll report back with more tests. I also like Josh's suggestion of adding proper PySpark benchmarking, I'll take a stab at that. -- Jeremy -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Python-API-Performance-tp1048p1099.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
