Hash benchmarks

[Luben Karavelov]

 Hello guys,

 I was looking for a way to speed up some heavy computations
 we are making @work so I have written small benchmark that:

 1. reads 5000 sparse vectors from disk
 2. computes inner product of the last 100 vectors
    with all the vectors before them

 The representation that we are using for sparse vectors is
 hash tables. I have written this small test case in
 several languages, including PIR. In every program the load
 time is under 2 seconds and the math is quite simple, so
 what this is stress testing hash tables code.

 Here are the results (some of them surprising)
 
         time    mem
 clojure 32      449536
 racket  70      168060
 c++     40      75180       map<int,float>
 c++     43      68960       unordered_map<int,float>
 perl    33      117904
 lua     26      108540
 luajit  6       68072
 haskell 23      1027504     Data.IntMap Float (compiled)
 parrot  28      360992      Hash PMC_keys PMC_vals
 parrot  15      263628      Hash int_keys PMC_vals

 So, my small comment: we are not bad at all. Luajit comes
 first but we are quite fast even without JIT.

 The biggest disappointment for me are statically typed
 compiled languages - they had all the time to optimize
 the code, they had proper information in order to use
 native, unboxed values but their performance is quite bad
 C++ uses 7x the time of the first (luajit) and haskel
 uses 10x times the memory.

 Another observation: it looks like luajit infers key and
 value datatypes and stores them unboxed.


 I hope you find this interesting
 Best regards

-- 
 Luben Karavelov