So, where is the performance now compared to the C program? I don't think MKL will give you much if you were on the order of 100x slower to start with.
-viral On Friday, February 20, 2015 at 8:19:50 PM UTC+5:30, Zhixuan Yang wrote: > > Mauro, Sean, and Tim, thanks for your help. > > Following your suggestions, I removed keyword arguments and split the > function to avoid conditional statements. These helped a bit. > > But I got a surprising result after replacing BLAS functions with simple > for loops, for loops is about 1.5x faster than BLAS calls. My Julia is > compiled on my computer with the default configuration (the versioninfo() > is listed below). Do you think it will help to compile a Julia with a > faster and more optimized BLAS implementation such as Intel's MKL? > > Julia Version 0.3.6-pre+70 > Commit 638fa02 (2015-02-12 13:59 UTC) > Platform Info: > System: Darwin (x86_64-apple-darwin14.1.0) > CPU: Intel(R) Core(TM) i7-4650U CPU @ 1.70GHz > WORD_SIZE: 64 > BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell) > LAPACK: libopenblas > LIBM: libopenlibm > LLVM: libLLVM-3.3 > > > Regards, Yang Zhixuan > > 在 2015年2月19日星期四 UTC+8下午10:51:20，Zhixuan Yang写道： >> >> Hello everyone, >> >> Recently I'm working on my first Julia project, a word embedding training >> program similar to Google's word2vec >> <https://code.google.com/p/word2vec/> (the code of word2vec is indeed >> very high-quality, but I want to add more features, so I decided to write a >> new one). Thanks to Julia's expressiveness, it cost me less than 2 days to >> write the entire program. But it runs really slow, about 100x slower than >> the C code of word2vec (the algorithm is the same). I've been trying to >> optimize my code for several days (adding type annotations, using BLAS to >> do computation, eliminating memory allocations ...), but it is still 30x >> slower than the C code. >> >> The critical part of my program is the following function (it also >> consumes most of the time according to the profiling result): >> >> function train_one(c :: LinearClassifier, x :: Array{Float64}, y :: >> Int64; α :: Float64 = 0.025, input_gradient :: Union(Nothing, >> Array{Float64}) = nothing) >> predict!(c, x) >> c.outputs[y] -= 1 >> >> if input_gradient != nothing >> # input_gradient = ( c.weights * outputs' )' >> BLAS.gemv!('N', α, c.weights, c.outputs, 1.0, input_gradient) >> end >> >> # c.weights -= α * x' * outputs; >> BLAS.ger!(-α, vec(x), c.outputs, c.weights) >> end >> >> function predict!(c :: LinearClassifier, x :: Array{Float64}) >> c.outputs = vec(softmax(x * c.weights)) >> end >> >> type LinearClassifier >> k :: Int64 # number of outputs >> n :: Int64 # number of inputs >> weights :: Array{Float64, 2} # k * n weight matrix >> >> outputs :: Vector{Float64} >> end >> >> And the entire program can be found here >> <https://github.com/yangzhixuan/embed>. Could you please check my code >> and tell me what I can do to get performance comparable to C. >> >> Regards. >> Yang Zhixuan >> >
