This does look like a nice benchmark. I would love to see what it takes to narrow down the gap further. Playing around with it now. Perhaps the threads branch is also worth a shot.
-viral > On 21-Feb-2015, at 1:23 pm, Zhixuan Yang <[email protected]> wrote: > > After recompiled an native arch version of Julia and OpenBLAS, it's about 8x > slower than the C code and I think it's near to the highest performance my > code can achieve. After all, the C code was optimized intensively in the > cache level and all loops were unrolled. But my Julia code is much more > flexible and extensible. > > Maybe I should try to use more computers. Currently my code is paralleled by > using pmap(). I hope the communication overhead will not be a new bottleneck > if I run on a local network cluster. > > Thanks for your help! > > Regards, Yang Zhixuan > > 在 2015年2月21日星期六 UTC+8下午2:23:37，Viral Shah写道： > 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 (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. Could you please check my code and > tell me what I can do to get performance comparable to C. > > Regards. > Yang Zhixuan
