I think partition is slowing you down (but haven't profiled to
verify). Here's a functional version that's about 70% as fast as my
"5":
(defn gaussian-matrix6 [L]
(to-array (for [i (range L)] (into-array Double/TYPE (for [j
(range L)] (next-gaussian))))))
and I'd guess that's about as good as you're going to get, given that
this approach is necessarily going to box and unbox the doubles, and
create intermediate sequences, rather than stuffing the primitive
doubles directly into the result array.
-Jason
On Sep 20, 12:00 pm, Ranjit <rjcha...@gmail.com> wrote:
> Replacing the doseq's with dotimes speeds it up a little more:
>
> (defn gaussian-matrix5 [^"[[D" arr]
> (dotimes [x (alength arr)]
> (dotimes [y (alength (first arr))]
> (aset-double ^doubles (aget arr (int x)) (int y) (next-
> gaussian)))))
>
> but I'm getting reflection warnings on alength. I guess it doesn't
> cause a problem because they're only called once?
>
> Also adding type hints to the more functional version of my first
> attempt speeds things up quite a bit:
>
> (defn gaussian-matrix2 [L]
> (into-array ^doubles
> (map double-array (partition L (repeatedly (* L L) next-
> gaussian)))))
>
> But it's still about 4x slower than gaussian-matrix5 above. There must
> be a way to improve on the inner loop here that doesn't require using
> indices, right?
>
> On Sep 20, 12:32 pm, Jason Wolfe <jawo...@berkeley.edu> wrote:
>
> > Oops, I found aset-double2 with tab completion and figured it was
> > build-in. Forgot it was a utility I built some time ago, a stub for a
> > Java method that does the setting.
>
> > Also, I got the type hint for the "arr" arg wrong, although it didn't
> > seem to matter.
>
> > Here's a fixed version in standard Clojure that's basically as fast:
>
> > user> (defn gaussian-matrix4 [^"[[D" arr ^int L]
> > (doseq [x (range L) y (range L)] (aset-double ^doubles
> > (aget arr (int x)) (int y) (.nextGaussian ^Random r))))
> > #'user/gaussian-matrix4
> > user> (do (microbench (gaussian-matrix3 (make-array Double/TYPE 10
> > 10) 10)) (microbench (gaussian-matrix4 (make-array Double/TYPE 10 10)
> > 10)) )
> > min; avg; max ms: 0.000 ; 0.033 ; 8.837 ( 56828 iterations)
> > min; avg; max ms: 0.009 ; 0.038 ; 7.132 ( 50579 iterations)
>
> > It seems like you should be able to just use aset-double with multiple
> > indices (in place of aset-double2), but I can't seem to get the type
> > hints right.
>
> > -Jason
>
> > On Sep 20, 7:36 am, Ranjit <rjcha...@gmail.com> wrote:
>
> > > Thanks Jason, this is great.
>
> > > I was confused earlier because I wasn't seeing reflection warnings,
> > > but it turns out that was only because I was evaluating the function
> > > definitions in the emacs buffer, and the warnings weren't visible.
>
> > > I have a question about gaussian-matrix3 though. What is "aset-
> > > double2"? Is that a macro that has a type hint for an array of
> > > doubles?
>
> > > Thanks,
>
> > > -Ranjit
> > > On Sep 19, 5:37 pm, Jason Wolfe <jawo...@berkeley.edu> wrote:
>
> > > > Hi Ranjit,
>
> > > > The big perf differences you're seeing are due to reflective calls.
> > > > Getting the Java array bits properly type-hinted is especially tricky,
> > > > since you don't always get good reflection warnings.
>
> > > > Note that aset is only fast for reference types:
>
> > > > user> (doc aset)
> > > > -------------------------
> > > > clojure.core/aset
> > > > ([array idx val] [array idx idx2 & idxv])
> > > > Sets the value at the index/indices. Works on Java arrays of
> > > > reference types. Returns val.
>
> > > > So, if you want to speed things up ... here's your starting point:
>
> > > > user> (set! *warn-on-reflection* true)
> > > > true
> > > > user> (import java.util.Random)
> > > > (def r (Random. ))
>
> > > > (defn next-gaussian [] (.nextGaussian r))
>
> > > > (defn gaussian-matrix1 [arr L]
> > > > (doseq [x (range L) y (range L)] (aset arr x y (next-gaussian))))
>
> > > > (defn gaussian-matrix2 [L]
> > > > (into-array (map double-array (partition L (repeatedly (* L L)
> > > > next-gaussian)))))
>
> > > > Reflection warning, NO_SOURCE_FILE:1 - reference to field nextGaussian
> > > > can't be resolved.
>
> > > > user> (do (microbench (gaussian-matrix1 (make-array Double/TYPE 10
> > > > 10) 10)) (microbench (gaussian-matrix2 10)) )
> > > > min; avg; max ms: 2.944 ; 4.693 ; 34.643 ( 424 iterations)
> > > > min; avg; max ms: 0.346 ; 0.567 ; 11.006 ( 3491 iterations)
>
> > > > ;; Now, we can get rid of the reflection in next-guassian:
>
> > > > user> (defn next-gaussian [] (.nextGaussian #^Random r))
> > > > #'user/next-gaussian
> > > > user> (do (microbench (gaussian-matrix1 (make-array Double/TYPE 10
> > > > 10) 10)) (microbench (gaussian-matrix2 10)) )
> > > > min; avg; max ms: 2.639 ; 4.194 ; 25.024 ( 475 iterations)
> > > > min; avg; max ms: 0.068 ; 0.130 ; 10.766 ( 15104 iterations)
> > > > nil
>
> > > > ;; which has cut out the main bottleneck in gaussian-matrix2.
> > > > ;; 1 is still slow because of its array handling.
> > > > ;; here's a fixed version:
>
> > > > user> (defn gaussian-matrix3 [^doubles arr ^int L]
> > > > (doseq [x (range L) y (range L)] (aset-double2 arr (int x) (int
> > > > y) (.nextGaussian ^Random r))))
> > > > #'user/gaussian-matrix3
>
> > > > user> (do (microbench (gaussian-matrix1 (make-array Double/TYPE 10
> > > > 10) 10)) (microbench (gaussian-matrix2 10)) (microbench (gaussian-
> > > > matrix3 (make-array Double/TYPE 10 10) 10)) )
> > > > min; avg; max ms: 2.656 ; 4.164 ; 12.752 ( 479 iterations)
> > > > min; avg; max ms: 0.065 ; 0.128 ; 9.712 ( 15255 iterations)
> > > > min; avg; max ms: 0.000 ; 0.035 ; 10.180 ( 54618 iterations)
> > > > nil
>
> > > > ;; which is 100x faster than where we started.
>
> > > > A profiler is often a great way to figure out what's eating up time.
> > > > Personally, I've never found the need to use a disassembler.
>
> > > > Cheers, Jason
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