4 ms·

Congrats, Adam! Do we have performance information yet, even on some basic, common use cases? Also, the docs mention EJML as the backend for Saddle's data str

• aklein 14y ago
  Thanks! I will do some follow-up posts on performance, but know that it has been a MAJOR design consideration. Consider the following in Saddle: val s1 = Series(vec.rand(10000), Index(Vec(array.randIntPos(10000)) % 100)) val s2 = Series(vec.rand(10000), Index(Vec(array.randIntPos(10000)) % 100)) clock { s1.join(s2, how=index.OuterJoin) } This clocks in at 19ms on my machine after Hotspot kicks in. The equivalent pandas: In [10]: ix1 = np.random.random_integers(0, 100, 10000) In [11]: ix2 = np.random.random_integers(0, 100, 10000) In [12]: df1 = DataFrame({'x' : np.random.rand(10000)}, ix1) In [13]: df2 = DataFrame({'y' : np.random.rand(10000)}, ix2) In [14]: %timeit df1.join(df2, how='outer') 10 loops, best of 3: 37.7 ms per loop
  • aklein 14y ago
    PS Regarding EJML, after extensive research, I found it hands down the fastest pure-java implementation for doing linear algebra. While it's maybe 2x-4x slower than JNI wrapped ATLAS or MKL, for the cases I deal with, it just doesn't matter vs ease of use. That said, it's LGPL, so I made it easy to swap out for other matrix libraries if you need.