在做一些基准测试以回答this关于连接数组的最快方法的问题时,我很惊讶当我使用jRuby执行相同的基准测试时,测试速度要慢得多。
这是否意味着关于jRuby比MRI Ruby更快的旧的慢板消失了?或者这是关于如何在jRuby中处理数组的?
这里的基准测试和MRI Ruby 2.3.0和jRuby 9.1.2.0的结果
两者都运行在64位Windows 7机箱上,所有4个处理器都占用50-60%,内存使用率为±5.5GB。必须使用参数-J-Xmx1500M
启动jRuby以提供足够的堆空间。由于堆栈级别太深,我不得不使用push删除测试,并且还删除了最慢的方法,使测试时间不长。使用Jave运行时:1.7.0_21
require 'Benchmark'
N = 100
class Array
def concat_all
self.reduce([], :+)
end
end
# small arrays
a = (1..10).to_a
b = (11..20).to_a
c = (21..30).to_a
Benchmark.bm do |r|
r.report('plus ') { N.times { a + b + c }}
r.report('concat ') { N.times { [].concat(a).concat(b).concat(c) }}
r.report('splash ') { N.times {[*a, *b, *c]} }
r.report('concat_all ') { N.times { [a, b, c].concat_all }}
r.report('flat_map ') { N.times {[a, b, c].flat_map(&:itself)} }
end
#large arrays
a = (1..10_000_000).to_a
b = (10_000_001..20_000_000).to_a
c = (20_000_001..30_000_000).to_a
Benchmark.bm do |r|
r.report('plus ') { N.times { a + b + c }}
r.report('concat ') { N.times { [].concat(a).concat(b).concat(c) }}
r.report('splash ') { N.times {[*a, *b, *c]} }
r.report('concat_all ') { N.times { [a, b, c].concat_all }}
r.report('flat_map ') { N.times {[a, b, c].flat_map(&:itself)} }
end
这个问题与使用的不同方法无关,请参阅原始问题。 在这两种情况下,MRI都快7倍! 有人能解释我为什么? 我也很好奇其他实现如RBX(Rubinius)
C:\Users\...>d:\jruby\bin\jruby -J-Xmx1500M concat3.rb
user system total real
plus 0.000000 0.000000 0.000000 ( 0.000946)
concat 0.000000 0.000000 0.000000 ( 0.001436)
splash 0.000000 0.000000 0.000000 ( 0.001456)
concat_all 0.000000 0.000000 0.000000 ( 0.002177)
flat_map 0.010000 0.000000 0.010000 ( 0.003179)
user system total real
plus 140.166000 0.000000 140.166000 (140.158687)
concat 143.475000 0.000000 143.475000 (143.473786)
splash 139.408000 0.000000 139.408000 (139.406671)
concat_all 144.475000 0.000000 144.475000 (144.474436)
flat_map143.519000 0.000000 143.519000 (143.517636)
C:\Users\...>ruby concat3.rb
user system total real
plus 0.000000 0.000000 0.000000 ( 0.000074)
concat 0.000000 0.000000 0.000000 ( 0.000065)
splash 0.000000 0.000000 0.000000 ( 0.000098)
concat_all 0.000000 0.000000 0.000000 ( 0.000141)
flat_map 0.000000 0.000000 0.000000 ( 0.000122)
user system total real
plus 15.226000 6.723000 21.949000 ( 21.958854)
concat 11.700000 9.142000 20.842000 ( 20.928087)
splash 21.247000 12.589000 33.836000 ( 33.933170)
concat_all 14.508000 8.315000 22.823000 ( 22.871641)
flat_map 11.170000 8.923000 20.093000 ( 20.170945)
答案 0 :(得分:4)
一般规则(如评论中所述)JRuby / JVM需要预热。
通常bmbm
是合适的,虽然TIMES=1000
应该增加(至少对于小数组的情况),1.5G可能还不足以实现JRuby的最佳性能(注意到相当大的变化)数字从-Xmx2g到-Xmx3g)。结果如下:
ruby 2.3.1p112 (2016-04-26 revision 54768) [x86_64-linux]
$ ruby concat3.rb
Rehearsal -----------------------------------------------
plus 0.000000 0.000000 0.000000 ( 0.000076)
concat 0.000000 0.000000 0.000000 ( 0.000070)
splash 0.000000 0.000000 0.000000 ( 0.000099)
concat_all 0.000000 0.000000 0.000000 ( 0.000136)
flat_map 0.000000 0.000000 0.000000 ( 0.000138)
-------------------------------------- total: 0.000000sec
user system total real
plus 0.000000 0.000000 0.000000 ( 0.000051)
concat 0.000000 0.000000 0.000000 ( 0.000059)
splash 0.000000 0.000000 0.000000 ( 0.000083)
concat_all 0.000000 0.000000 0.000000 ( 0.000120)
flat_map 0.000000 0.000000 0.000000 ( 0.000173)
Rehearsal -----------------------------------------------
plus 43.040000 3.320000 46.360000 ( 46.351004)
concat 15.080000 3.870000 18.950000 ( 19.228059)
splash 49.680000 4.820000 54.500000 ( 54.587707)
concat_all 51.840000 5.260000 57.100000 ( 57.114867)
flat_map 17.380000 5.340000 22.720000 ( 22.716987)
------------------------------------ total: 199.630000sec
user system total real
plus 42.880000 3.600000 46.480000 ( 46.506013)
concat 17.230000 5.290000 22.520000 ( 22.890809)
splash 60.300000 7.480000 67.780000 ( 67.878534)
concat_all 54.910000 6.480000 61.390000 ( 61.404383)
flat_map 17.310000 5.570000 22.880000 ( 23.223789)
...
jruby 9.1.6.0 (2.3.1) 2016-11-09 0150a76 Java HotSpot(TM) 64-Bit Server VM 25.112-b15 on 1.8.0_112-b15 +jit [linux-x86_64]
$ jruby -J-Xmx3g concat3.rb
Rehearsal -----------------------------------------------
plus 0.010000 0.000000 0.010000 ( 0.001445)
concat 0.000000 0.000000 0.000000 ( 0.002534)
splash 0.000000 0.000000 0.000000 ( 0.001791)
concat_all 0.000000 0.000000 0.000000 ( 0.002513)
flat_map 0.010000 0.000000 0.010000 ( 0.007088)
-------------------------------------- total: 0.020000sec
user system total real
plus 0.010000 0.000000 0.010000 ( 0.002700)
concat 0.000000 0.000000 0.000000 ( 0.001085)
splash 0.000000 0.000000 0.000000 ( 0.001569)
concat_all 0.000000 0.000000 0.000000 ( 0.003052)
flat_map 0.000000 0.000000 0.000000 ( 0.002252)
Rehearsal -----------------------------------------------
plus 32.410000 0.670000 33.080000 ( 17.385688)
concat 18.610000 0.060000 18.670000 ( 11.206419)
splash 57.770000 0.330000 58.100000 ( 25.366032)
concat_all 19.100000 0.030000 19.130000 ( 13.747319)
flat_map 16.160000 0.040000 16.200000 ( 10.534130)
------------------------------------ total: 145.180000sec
user system total real
plus 16.060000 0.040000 16.100000 ( 11.737483)
concat 15.950000 0.030000 15.980000 ( 10.480468)
splash 47.870000 0.130000 48.000000 ( 22.668069)
concat_all 19.150000 0.030000 19.180000 ( 13.934314)
flat_map 16.850000 0.020000 16.870000 ( 10.862716)
...所以看起来相反 - MRI 2.3比JRuby 9.1慢2.5倍
cat concat3.rb
require 'benchmark'
N = (ENV['TIMES'] || 100).to_i
class Array
def concat_all
self.reduce([], :+)
end
end
# small arrays
a = (1..10).to_a
b = (11..20).to_a
c = (21..30).to_a
Benchmark.bmbm do |r|
r.report('plus ') { N.times { a + b + c }}
r.report('concat ') { N.times { [].concat(a).concat(b).concat(c) }}
r.report('splash ') { N.times {[*a, *b, *c]} }
r.report('concat_all ') { N.times { [a, b, c].concat_all }}
r.report('flat_map ') { N.times {[a, b, c].flat_map(&:itself)} }
end
#large arrays
a = (1..10_000_000).to_a
b = (10_000_001..20_000_000).to_a
c = (20_000_001..30_000_000).to_a
Benchmark.bmbm do |r|
r.report('plus ') { N.times { a + b + c }}
r.report('concat ') { N.times { [].concat(a).concat(b).concat(c) }}
r.report('splash ') { N.times {[*a, *b, *c]} }
r.report('concat_all ') { N.times { [a, b, c].concat_all }}
r.report('flat_map ') { N.times {[a, b, c].flat_map(&:itself)} }
end
答案 1 :(得分:1)
我从这些评论和答案以及我之后自己做过的测试中学到了什么......
最后一个,加上JVM使得MRI更适合短临时脚本,jRuby更适合过程饥饿,运行时间更长的方法经常重复,因此jRuby更适合运行服务器和服务。
我看到的确认:为长期或重复的过程做自己的基准测试。 与早期版本相比,这两种实现都在速度方面取得了很大的进步,让我们不要忘记:Ruby可能会慢一些,但开发人员会更快,如果你将一些额外硬件的成本与一些额外的开发人员进行比较......
感谢所有评论者和凯伦的专业知识。
修改
出于好奇,我还在Docker容器中使用Rubinius运行测试(我在Windows上),rubinius 3.69 (2.3.1 a57071c6 2016-11-17 3.8.0) [x86_64-linux-gnu]
只有concat和flat_map与MRI相同,我想知道这些方法是否在C中,其余的都在纯Ruby中。
Rehearsal -----------------------------------------------
plus 0.000000 0.000000 0.000000 ( 0.000742)
concat 0.000000 0.000000 0.000000 ( 0.000093)
splash 0.000000 0.000000 0.000000 ( 0.000619)
concat_all 0.000000 0.000000 0.000000 ( 0.001357)
flat_map 0.000000 0.000000 0.000000 ( 0.001536)
-------------------------------------- total: 0.000000sec
user system total real
plus 0.000000 0.000000 0.000000 ( 0.000589)
concat 0.000000 0.000000 0.000000 ( 0.000084)
splash 0.000000 0.000000 0.000000 ( 0.000596)
concat_all 0.000000 0.000000 0.000000 ( 0.001679)
flat_map 0.000000 0.000000 0.000000 ( 0.001568)
Rehearsal -----------------------------------------------
plus 68.770000 63.320000 132.090000 (265.589506)
concat 20.300000 2.810000 23.110000 ( 23.662007)
splash 79.310000 74.090000 153.400000 (305.013934)
concat_all 83.130000 100.580000 183.710000 (378.988638)
flat_map 20.680000 0.960000 21.640000 ( 21.769550)
------------------------------------ total: 513.950000sec
user system total real
plus 65.310000 70.300000 135.610000 (273.799215)
concat 20.050000 0.610000 20.660000 ( 21.163930)
splash 79.360000 80.000000 159.360000 (316.366122)
concat_all 84.980000 99.880000 184.860000 (383.870653)
flat_map 20.940000 1.760000 22.700000 ( 22.760643)