Question

我在Julia Studio（Julia 0.2.0，OSX 10.8.2）中编写了一个非常基本的算法，计算了Hearthstone中给定法术力曲线每回合剩下的平均法力值。完成算法后，我向所有变量添加了类型声明，认为这有助于提高整体速度。惊喜！添加的类型声明使代码运行速度慢了4倍（从~7s到~28s）。造成这种奇怪行为的原因是什么，我该如何解决？感觉添加类型应该有助于编译器生成更快的代码，或者至少没有区别。

这是没有类型声明的代码（运行时间6.76s）：

function all_combinations(n)
    result = Array{Int64}[]
    for x in [1:n]
        append!(result, collect(combinations(1:n,x)))
    end
    return result
end

curve = [2, 3, 4, 5, 5, 4, 3, 2, 1, 1]

games = Array{Int64}[]

function execute()
    for game_n in [1:5000]

        deck = mapreduce(
            (x) -> fill(x[1], x[2]),
            append!,
            enumerate(curve))

        function drawcard()
            card = splice!(deck, rand(1:length(deck)))
        end

        hand = [drawcard() for n in [1:3]]

        turn_leftovers = Int64[]

        for mana in [1:10]

            push!(hand, drawcard())

            possible_plays = all_combinations(length(hand))
            map!(
                play -> map(i -> hand[i], play),
                possible_plays)
            filter!(x -> sum(x) <= mana, possible_plays)

            if  !isempty(possible_plays)

                play = reduce(
                    (a, b) -> sum(a) > sum(b) ? a : b,
                    possible_plays)
                for card in play
                    splice!(hand, findfirst(hand, card))
                end
                push!(turn_leftovers, mana - sum(play))
            else
                push!(turn_leftovers, mana)
            end

        end

        push!(games, turn_leftovers)

    end
end

println(@elapsed execute())

println("Averaging over $(length(games)) games")
for turn in [1:length(games[1])]
    avrg = mean(map(game -> game[turn], games))
    println("Left on turn $turn: $avrg")
end
println("Average mana leftover: $(mean(reduce(vcat, games)))")
println("Done")

这是带有类型声明的代码（运行时间28.48s）：

function all_combinations(n)
    result = Array{Int64}[]
    for x in [1:n]
        append!(result, collect(combinations(1:n,x)))
    end
    return result
end

curve::Array{Int64} = [2, 3, 4, 5, 5, 4, 3, 2, 1, 1]

games = Array{Int64}[]

function execute()
    for game_n::Int64 in [1:5000]

        deck::Array{Int64}
        deck = mapreduce(
            (x) -> fill(x[1], x[2]),
            append!,
            enumerate(curve))

        function drawcard()
            card::Int64 = splice!(deck, rand(1:length(deck)))
        end

        hand::Array{Int64}
        hand = [drawcard() for n in [1:3]]

        turn_leftovers::Array{Int64}
        turn_leftovers = Int64[]

        for mana::Int64 in [1:10]

            push!(hand, drawcard())

            possible_plays::Array{Array{Int64}} = all_combinations(length(hand))
            map!(
                play -> map(i::Int64 -> hand[i], play),
                possible_plays)
            filter!(x::Array{Int64} -> sum(x) <= mana, possible_plays)

            if  !isempty(possible_plays)

                play::Array{Int64} = reduce(
                    (a::Array{Int64}, b::Array{Int64}) -> sum(a) > sum(b) ? a : b,
                    possible_plays)
                for card::Int64 in play
                    splice!(hand, findfirst(hand, card))
                end
                push!(turn_leftovers, mana - sum(play))
            else
                push!(turn_leftovers, mana)
            end

        end

        push!(games, turn_leftovers)

    end
end

println(@elapsed execute())

println("Averaging over $(length(games)) games")
for turn in [1:length(games[1])]
    avrg = mean(map(game -> game[turn], games))
    println("Left on turn $turn: $avrg")
end
println("Average mana leftover: $(mean(reduce(vcat, games)))")
println("Done")

值得注意的是，即使是最快的版本也比用JavaScript编写的等效代码慢一点。这可能只是因为糟糕的实施。毫无疑问，更好的算法会在一周的任何一天都超过JS。

Answer 1

减速的一个来源：你正在使用许多匿名函数和更高阶函数，例如，

map!( play -> map(i::Int64 -> hand[i], play), possible_plays ) filter!(x::Array{Int64} -> sum(x) <= mana, possible_plays)

在目前的Julia中，编译器不容易优化这两种结构。用列表推导或循环来替换它们可以改善事情。

添加类型声明会以某种方式显着降低Julia算法的速度

1 个答案: