Not sure, but two possibilities come to mind: - I'm not sure it works with keyword functions. One way to check would be to modify base to create an`svds_` function (note the underscore) that takes all those parameters as regular arguments, and have `svds` call `svds_`. Then precompile `Base.svds_` for your desired argument types. If this fixes the problem, you could submit a pull request making such a change official, and see how people like it.
- I haven't even glanced at the svds code, but it's possible it has some parts that can't fully infer the types; in that case, the precompilation chain gets broken and it waits until runtime when it knows what the types are. You could try also precompiling some of its dependent functions for the actual types it uses. I generally find it's hard to guess which ones need precompilation, and easier just to snoop in on the compiler and see what it's doing. If you're up for such things, try uncommenting https://github.com/JuliaLang/julia/blob/bef6bf335773cafdc2786f1fbabacc01e0be6260/src/codegen.cpp#L604 You'll also want to print the argument types: https://github.com/JuliaLang/julia/blob/bef6bf335773cafdc2786f1fbabacc01e0be6260/src/julia.h#L148 using `jl_`: http://docs.julialang.org/en/latest/devdocs/debuggingtips/ Good luck! --Tim On Wednesday, February 25, 2015 08:57:57 PM Todd Leo wrote: > I am a little bit confused when trying to add precompile for svds in > userimg.jl, since svds uses nsv as keyword arguments: > > function svds{S}(X::S; nsv::Int = 6, ritzvec::Bool = true, tol::Float64 = > 0.0, maxiter::Int = 1000) > > hence, calling svds maybe looks like: svds(X, nsv = 2), where X is a sparse > matrix. In this case, does the following precompile code work as what I > intended to? > > precompile(svds, (SparseMatrixCSC{Float64,Int64}, Int64)) > > Surprisingly, I get no speed boost after rebuild julia with the precompile > configuration. So I guess I must have missed something. In addition, I > tried to precompile sprand function, and it did boost up quite a lot. > > Cheers, > Todd Leo > > On Wednesday, February 25, 2015 at 6:02:52 PM UTC+8, Tim Holy wrote: > > Keep in mind that compilation involves multiple stages: > > - parsing > > - lowering > > - type inference > > - generation of LLVM IR > > - generation of machine native code > > > > Just because it gets built into julia does not mean it goes through all of > > those steps for all possible data types (in fact, that's not even possible > > in > > principle). The first two steps are the primary reason why "using Gadfly" > > is > > slow. The last 3 are the reasons why calling sprand the second time is > > faster > > than the first. > > > > Like I said, look at base/precompile.jl. All of those functions are > > built-in > > to julia, so if there were no point in issuing those precompile > > statements, > > why would we bother having that file? > > > > You could make your own userimg.jl file with > > > > precompile(svds, (Matrix{Float64},)) > > > > rebuild julia, and then I predict you'll get much faster response on the > > first > > use. But if you use matrices of different element types (other than > > Float64), > > you'll need to include those, too---the inferred, LLVM IR, and machine > > native > > code is specific for the element type. > > > > --Tim > > > > On Wednesday, February 25, 2015 01:14:53 AM Todd Leo wrote: > > > That's exactly how I understand, and I'm using 0.4, so Tim's suggestion > > > above won't work...I presume this is not related to the implementation > > > > of > > > > > svds, since other built-in functions also takes much more time and > > > > memory > > > > > on the first running: > > > > > > julia> @time sprand(5,5,0.1) > > > elapsed time: 0.314905039 seconds (11 MB allocated) > > > julia> @time sprand(5,5,0.1) > > > elapsed time: 3.0776e-5 seconds (976 bytes allocated) > > > > > > > > > Cheers, > > > Todd Leo > > > > > > On Wednesday, February 25, 2015 at 5:00:21 PM UTC+8, Viral Shah wrote: > > > > No need to precompile. svds was recently added to base - but it won't > > > > exist in 0.3. > > > > > > > > -viral > > > > > > > > On Wednesday, February 25, 2015 at 12:32:09 PM UTC+5:30, Todd Leo > > > > wrote: > > > >> Isn't svd/svds already included in base? Do I need to precompile > > > > them? > > > > > >> On Saturday, February 7, 2015 at 3:48:50 AM UTC+8, Tim Holy wrote: > > > >>> Since it allocates 120MB on the first call and only 9kB on the > > > > second, > > > > > >>> all that > > > >>> memory is just due to compilation. > > > >>> > > > >>> The only way I know to fix that is to precompile some of the > > > > functions > > > > > >>> when you > > > >>> build julia. If this is a big deal to you, consider adding your own > > > >>> custom > > > >>> userimg.jl (and see base/precompile.jl for a model). > > > > http://docs.julialang.org/en/latest/manual/modules/?highlight=userimg#mo > > > > > >>> dule-initialization-and-precompilation > > > >>> > > > >>> --Tim > > > >>> > > > >>> On Friday, February 06, 2015 11:23:52 AM Viral Shah wrote: > > > >>> > This is because the internal operations with svds are allocating > > > > new > > > > > >>> > vectors every iteration. This PR should address it, but needs to > > > > be > > > > > >>> > completed, > > > >>> > > > > >>> > https://github.com/JuliaLang/julia/pull/7907 > > > >>> > > > > >>> > Also, array indexing giving views by default will also help here. > > > >>> > > > > >>> > -viral > > > >>> > > > > >>> > On Friday, February 6, 2015 at 7:50:24 PM UTC+5:30, Todd Leo > > > > wrote: > > > >>> > > Hi Julians, > > > >>> > > > > > >>> > > I am trying to apply svd on a very large sparse matrix using > > > > svds. > > > > > >>> When > > > >>> > > > >>> > > warming up svds function, I used a very small random vector, and > > > > the > > > > > >>> > > number > > > >>> > > of singular values (nsv) is set to one. However, this simple set > > > > up > > > > > >>> > > results > > > >>> > > in a considerable high cost of time, and as much as 120MB memory > > > > is > > > > > >>> > > occupied. To me, it doesn't make sense, and one can reproduce it > > > > by: > > > >>> > > julia> @time svds(sprand(3,3,0.1), nsv = 1) > > > >>> > > > > > >>> > >> elapsed time: 2.640233094 seconds (117 MB allocated, 1.06% gc > > > > time > > > > > >>> in 5 > > > >>> > > > >>> > >>> pauses with 0 full sweep) > > > >>> > > > > > >>> > > Regards, > > > >>> > > Todd
