使用 julia 读取文本文件的最快方法是什么

Question

我很难使用 Julia 读取大型文本文件（968MB，870 万行）。每一行是这样的：

0.3295747E+01   0.3045123E+01   0.3325542E+01   0.1185458E+01  -0.4827727E-05  -0.1033694E-04   0.3306459E-03

我曾经parse.(Float64, split(line))将每一行转换为数字。

function openfile()
    datafile = open("data.dat","r")
    lines = readlines(datafile)
    close(datafile)
    lines
end

function parseline(lines::Array{String})
    for line in lines
        zzz = parse.(Float64, split(line))
    end
end

import Base: tryparse_internal
function myreadfile(str::String, T::Type, dlm=' ', eol='\n')
    row, clm, bg, ed = 0, 0, 0, 0
    data = Array{T}(undef,0)
    isnu0, isnu = false, false
    for (idx, chr) in enumerate(str)
        isnu = false
        (chr != eol && chr != dlm) && (isnu = true)
        if isnu0 == false && isnu == true
            bg, isnu0 = idx, true
        end
        if isnu0 == true && isnu == false
            ed, isnu0 = idx-1, false
            push!(data, tryparse_internal(T, str, bg, ed))
        end
    end
    isnu == true && (push!(data, tryparse(T, str[bg:end])))
    data
end

@time lines = openfile()
@time parseline(lines)
using DelimitedFiles
@time readdlm("data.dat")
@time myreadfile(read("data.dat",String), Float64)

并得到

  3.584656 seconds (17.59 M allocations: 1.240 GiB, 28.44% gc time)
 78.099010 seconds (276.14 M allocations: 6.080 GiB, 1.50% gc time)
 52.504199 seconds (185.93 M allocations: 3.960 GiB, 0.53% gc time)
 46.085581 seconds (61.70 M allocations: 2.311 GiB, 0.28% gc time)

与fortran代码比较

call cpu_time(start)
open(10, file="data.dat",status="old")
do i=1, 8773632
    read(10,*) a, b, c, d, e, f, g
end do
call cpu_time(finish)
print '("Time = ",f6.3," seconds.")',finish-start

这是 Time = 14.812 seconds.

似乎 Julia 花更长的时间做同样的事情。有没有更好的方法将字符串转换为浮点数？split 和 parse 太慢了。

Answer 1

正如上面的评论所说，最快的很可能是readdlm函数。这将返回一个最有可能是您想要的矩阵。

如果您确实想手动完成，通常最好通读文件并逐行处理，而不是将所有内容存储在大型中间对象中。内存读写速度很慢。就像是

ret = open("data.dat","r") do datafile
    [parse.(Float64, split(line)) for line in eachline(datafile)]
end

无论如何，它可能不会比你的最后一行快。