I agree with Elliot, take a look at the performance tips. Also, you may want to move the tic(), toc() out of the function, make sure you compile it first, and then use @time <function calll> to time it.
you may also get a considerable boost by using @simd in your for loops (together with @inbounds) Let us know how it goes ;) cheers. El domingo, 27 de abril de 2014 09:39:03 UTC+2, Freddy Chua escribió: > > Alright, thanks! All these is looking very positive for Julia. > > On Sunday, April 27, 2014 3:36:23 PM UTC+8, Elliot Saba wrote: >> >> I highly suggest you read through the whole "Performance >> Tips<http://julia.readthedocs.org/en/latest/manual/performance-tips/>" >> page I linked to above; it has documentation on all these little features >> and stuff. I did get a small improvement (~5%) by enabling SIMD extensions >> on the two inner for loops, but that requires a very recent build of Julia >> and is a somewhat experimental feature. Neat to have though. >> -E >> >> >> On Sun, Apr 27, 2014 at 12:14 AM, Freddy Chua <fred...@gmail.com> wrote: >> >>> wooh, this @inbounds thing is new to me... At least it does shows that >>> Julia is comparable to Java. >>> >>> >>> On Sunday, April 27, 2014 3:04:26 PM UTC+8, Elliot Saba wrote: >>> >>>> Since we have made sure that our for loops have the right boundaries, >>>> we can assure the compiler that we're not going to step out of the bounds >>>> of an array, and surround our code in the @inbounds macro. This is not >>>> something you should do unless you're certain that you'll never try to >>>> access memory out of bounds, but it does get the runtime down to 0.23 >>>> seconds, which is on the same order as Java. Here's the full >>>> code<https://gist.github.com/staticfloat/11339342>with all the >>>> modifications made. >>>> -E >>>> >>>> >>>> On Sat, Apr 26, 2014 at 11:55 PM, Freddy Chua <fred...@gmail.com>wrote: >>>> >>>>> Stochastic Gradient Descent is one of the most important optimisation >>>>> algorithm in Machine Learning. So having it perform better than Java is >>>>> important to have more widespread adoption. >>>>> >>>>> >>>>> On Sunday, April 27, 2014 2:03:28 PM UTC+8, Freddy Chua wrote: >>>>> >>>>>> This code takes 60+ secs to execute on my machine. The Java >>>>>> equivalent takes only 0.2 secs!!! Please tell me how to optimise the >>>>>> following code.begin >>>>>> >>>>>> begin >>>>>> N = 10000 >>>>>> K = 100 >>>>>> rate = 1e-2 >>>>>> ITERATIONS = 1 >>>>>> >>>>>> # generate y >>>>>> y = rand(N) >>>>>> >>>>>> # generate x >>>>>> x = rand(K, N) >>>>>> >>>>>> # generate w >>>>>> w = zeros(Float64, K) >>>>>> >>>>>> tic() >>>>>> for i=1:ITERATIONS >>>>>> for n=1:N >>>>>> y_hat = 0.0 >>>>>> for k=1:K >>>>>> y_hat += w[k] * x[k,n] >>>>>> end >>>>>> >>>>>> for k=1:K >>>>>> w[k] += rate * (y[n] - y_hat) * x[k,n] >>>>>> end >>>>>> end >>>>>> end >>>>>> toc() >>>>>> end >>>>>> >>>>>> Sorry for repeated posting, I did so to properly indent the code.. >>>>>> >>>>> >>>> >>
