朱莉娅，加快评估 [英] Julia, speeding up eval

问题描述

Julia具有访问其自己的语法树的非常不错的功能，可以很容易地以编程方式生成新函数，但是它比普通的Julia代码要慢得多.

Julia has the very nice feature of having access to its own syntactic tree, which makes it easy to generate new functions programatically, but it's much slower than the normal Julia code.

例如:

julia> timing = @time for i in [1:100] tan(pi/2*rand()); end
elapsed time: 1.513e-5 seconds (896 bytes allocated)

julia> timing = @time for i in [1:100] x = pi/2*rand(); eval(:(tan(x))); end
elapsed time: 0.0080231 seconds (23296 bytes allocated)

julia> timing = @time for i in [1:100]  eval(:(tan(pi/2*rand()))); end
elapsed time: 0.017245327 seconds (90496 bytes allocated)

有没有办法使eval的速度与普通Julia代码的速度相同?

Is there a way to give to eval the same speed as the normal Julia code?

我可以使用precompile函数稍微加快评估速度，但这还不够:

I was able to slightly speed up eval using the precompile function, but that still not enough:

julia> tmp3 = :(sin(x))
:(sin(x))

julia> timing = @time for i in [1:100000] x = pi/2*rand(); eval(tmp3); end
elapsed time: 8.651145772 seconds (13602336 bytes allocated)

julia> precompile(tmp3,(Float64,Float64))

julia> timing = @time for i in [1:100000] x = pi/2*rand(); eval(tmp3); end
elapsed time: 8.611654016 seconds (13600048 bytes allocated)

@Ivarne建议我提供有关我的项目的详细信息.好吧，我想使用Julia的元编程功能来计算符号导数并运行它们.

@Ivarne suggested me to provide details on my project. Well, I would like to use the meta-programming capabilities of Julia to calculate the symbolic derivatives and run them.

我编写了一个函数derivative(ex::Expr,arg::Symbol)，该函数带有和表达式以及一个自变量，并返回一个新表达式，该表达式是ex对arg的派生.不幸的是，生成的Expr花费的时间太长.

I wrote a function derivative(ex::Expr,arg::Symbol) that takes and expression and an argument, and returns a new expression that is the derivative of ex with respect to arg. Unfortunately, the resulting Expr takes too long to evaluate.

作为结论，使用@eval而不是eval进行表演:

as a conclusion, the performances using @eval instead of eval:

julia> timing = @time for i in [1:100000] x = pi/2*rand(); @eval(tmp3); end
elapsed time: 0.005821547 seconds (13600048 bytes allocated)

tmp3仍然是:(sin(x))

推荐答案

如果需要速度，则不应该使用eval，因为eval每次都需要做大量工作才能生成优化的快速代码.

If you need speed, you shouldn't use eval, because it has to do lots of work to generate optimized fast code every time.

如果要操作表达式，则应查看宏.它们对表达式进行运算并返回将被编译一次的表达式.请参见 http://docs.julialang.org/en/latest/manual/metaprogramming/.

If you want to manipulate expressions, you should look at macros instead. They operate on expressions and return expressions that will be compiled once. See http://docs.julialang.org/en/latest/manual/metaprogramming/.

如果您提供有关问题的一些详细信息，不仅是评估性能，还将更容易为您指明正确的方向.使julia中的eval更快是一个项目，而不是StackOverflow的问题.

If you provide some details on your problem, and not only performance testing on eval, it will be easier to point you in the right direction. Making eval in julia faster is a project, not a question for StackOverflow.

修改: Calculus.jl 中已经有某些功能，我认为如果执行以下操作将是最好的: /p>

Edit: There is already some of that functionality in Calculus.jl, and I think it will be best if you do something like:

myexpr = :(sin(x))
myexpr_dx = derivative(myxpr)
@eval myfun(x) = $myexpr
@eval myfun_dx(x) = $myexpr_dx 

这样您就可以求值而不是表达式了.然后，您可以对myfun(x)和myfun_dx()进行性能测试

So that you get a function you can evaluate instead of an expression. You can then do performance testing on myfun(x) and myfun_dx()

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