[英]run haskell operations in parallel or multithreaded
I have just started learning functional programming and Haskell and am I not even close to understanding the syntax and how it works because it is very different from what I have been used to before (ruby, php, C# or node for instance, which are OOP languages - more or less). 我刚刚开始学习函数式编程和Haskell,我甚至还不了解语法及其工作方式,因为它与我以前使用过的语法(例如,ruby,php,C#或node,它们是OOP)有很大的不同。语言-或多或少)。
I am am trying to put together a small program that checks for solutions for Brocard's Problem or so called Brown Numbers and I first created a draft in ruby, but I discovered that Haskell is much more appropriate for doing mathematical expressions. 我试图将一个小程序组合在一起,以检查Brocard问题的解决方案或所谓的Brown Numbers ,我首先用红宝石创建了草稿,但是我发现Haskell更适合于做数学表达式。 I've asked a question previously and I got an answer pretty quick on how to translate my ruby code to Haskell:
我之前曾问过一个问题,并且很快就将红宝石代码转换为Haskell得到了答案:
results :: [(Integer, Integer)] --Use instead of `Int` to fix overflow issue
results = [(x,y) | x <- [1..1000], y <- [1..1000] , 1 + fac x == y*y]
where fac n = product [1..n]
I changed that slightly so I can run the same operation from and up to whatever number I want, because the above will do it from 1
up to 1000
or any hardcoded number but I would like to be able to decide the interval it should go through, ergo: 我稍作更改,因此我可以一直执行相同的操作,直到我想要的任何数字,因为上面的操作可以从
1
到1000
或任何硬编码的数字进行,但是我希望能够确定应该经历的时间间隔,因此:
pairs :: (Integer, Integer) -> [(Integer, Integer)]
pairs (lower, upper) = [(m, n) | m <- [lower..upper], n <- [lower..upper], 1 + factorial n == m*m] where factorial n = product [1..n]
The reason I ask this questions is that I was thinking if I could run this operations in parallel (and/or on multiple threads and/or cores) and append the results to the same variable. 我问这个问题的原因是我在考虑是否可以并行(和/或在多个线程和/或内核上)运行此操作并将结果附加到同一变量中。
I have not yet understood the parallel processing in Haskell or the threading model, but in ruby a good example of how the threading model works is something like this : 我尚未了解Haskell或线程模型中的并行处理,但是在ruby中,线程模型如何工作的一个很好的例子是这样的 :
def func1
i=0
while i<=2
puts "func1 at: #{Time.now}"
sleep(2)
i=i+1
end
end
def func2
j=0
while j<=2
puts "func2 at: #{Time.now}"
sleep(1)
j=j+1
end
end
puts "Started At #{Time.now}"
t1=Thread.new{func1()}
t2=Thread.new{func2()}
t1.join
t2.join
puts "End at #{Time.now}"
And in my case, the func1
and func2
would be the same function results
computed on different intervals ( [1..1000] -> [i..j]
). 在我的情况下,
func1
和func2
将是在不同时间间隔( [1..1000] -> [i..j]
)上计算出的相同函数results
。
I would appreciate some help with this as I am not capable of doing it myself at this point :) 我很乐意为此提供一些帮助,因为我目前无法自己做:)
Parallel and Concurrent Programming in Haskell has a lot of good information, and async is a good library for this stuff. Haskell中的并行和并发编程有很多很好的信息,而异步是一个很好的库。
At the bottom level though, you'll find forkIO
to start a new lightweight thread. 不过,在最底层,您会发现
forkIO
启动一个新的轻量级线程。
Of course that's concurrency , not deterministic parallelism, parallel is the library for that, and also covered in the book. 当然,这是并发的 ,而不是确定性的并行性, parallel是该类的库,并且在本书中也进行了介绍。
Your example translates to: 您的示例转换为:
import Data.Time.Clock (getCurrentTime)
main = do
start <- getCurrentTime
putStr "Started At " >> print start
_ <- forkIO func1
_ <- forkIO func2
end <- getCurrentTime
putStr "End at " >> print end
func1 = helper "func1" 2
func2 = helper "func2" 1
helper name sleepTime = go 0
where
go 3 = return ()
go n = do
now <- getCurrentTime
putStr name >> putStr " at: " >> print now
threadDelay sleepTime
go $ succ n
I recommend learning the parallel and/or async libraries mentioned above, though, instead of writing your own threading stuff, at least initially. 但是,我建议学习上述并行和/或异步库,而不是至少在最初时编写自己的线程东西。
Here's a not-so-great example of running tests on 8-ish processors using parallel: 这是一个不太好的示例,使用并行在8-ish处理器上运行测试:
import Control.Parallel.Strategies
factorial = product . enumFromTo 1
pairs (lower, upper) = map fst . filter snd . withStrategy sparkTest
$ [ ((m, n), b)
| m <- [lower..upper]
, n <- [lower..upper]
, let b = 1 + factorial n == m*m
]
sparkTest = evalBuffer 8 $ evalTuple2 rseq rpar
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