Hi, Simon.
I ported the array benchmark to a slower machine, in order to detect smaller
variations in speed. Clean compiler does not show huge differences in speed
when one switches from boxed to unboxed arrays, or from lazy to strict arrays.
As you can see below, adding unboxed annotation, or strictness annotation does
not change performance noticeably. This is a feature that I like in Clean, and
I would like to see in Haskell. Correct programs are almost equally fast.
Therefore, the programmer does not need to worry about boxed, unboxed, strict
or lazy applications. What I miss in Clean is an interpreter, like GHCi. I am
writing a somewhat large genetic programming system to diagnostic electric
installations, and electronic circuits. I mean, I am working for people who
are writing the programs and designing the circuits. The programs are working
in Lisp and Scheme (SBCL and Gambit). The Clean version is almost functional. I
am struggling with the Haskell
version. I will write a private letter to you explaining how the Clean program
works. I wonder whether you could give me some hints on how to port it to
Haskell. Since Haskell has an interpreter, I think that it could be used to
test the generated applications, and calculate the fitness function.
/* BOXED LAZY
D:\Clean 2.2\geneticprog\bench>boxedlazy
280000000
Execution: 0.04 Garbage collection: 0.17 Total: 0.21
D:\Clean 2.2\geneticprog\bench>boxedlazy
280000000
Execution: 0.06 Garbage collection: 0.17 Total: 0.23
D:\Clean 2.2\geneticprog\bench>boxedlazy
280000000
Execution: 0.04 Garbage collection: 0.17 Total: 0.21
*/
module boxedlazy
import StdEnv
fn i a acc | i<1 = acc
fn i a=:{[i]=b} acc
# a= {a&[i]= b+3.0}
# (c, a)= a![i]
= fn (i-1) a (c+acc)
Start= fn 2000000 vt 0.0
where
vt:: .{Real}
vt = createArray 2000001 137.0
module arrays
import StdEnv
fn i a acc | i<1 = acc
fn i a=:{[i]=b} acc
# a= {a&[i]= b+3.0}
# (c, a)= a![i]
= fn (i-1) a (c+acc)
Start= fn 2000000 vt 0.0
where
vt:: .{#Real}
vt = createArray 2000001 137.0
/* unboxedlazy
D:\Clean 2.2\geneticprog\bench>unboxedlazy.exe
280000000
Execution: 0.03 Garbage collection: 0.00 Total: 0.03
D:\Clean 2.2\geneticprog\bench>unboxedlazy.exe
280000000
Execution: 0.04 Garbage collection: 0.00 Total: 0.04
D:\Clean 2.2\geneticprog\bench>unboxedlazy.exe
280000000
Execution: 0.03 Garbage collection: 0.00 Total: 0.03
*/
module unboxedlazy
import StdEnv
fn i a acc | i<1 = acc
fn i a=:{[i]=b} acc
# a= {a&[i]= b+3.0}
# (c, a)= a![i]
= fn (i-1) a (c+acc)
Start= fn 2000000 vt 0.0
where
vt:: .{#Real}
vt = createArray 2000001 137.0
/* unboxedstrict
D:\Clean 2.2\geneticprog\bench>unboxedstrict.exe
280000000
Execution: 0.03 Garbage collection: 0.00 Total: 0.03
D:\Clean 2.2\geneticprog\bench>unboxedstrict.exe
280000000
Execution: 0.04 Garbage collection: 0.00 Total: 0.04
D:\Clean 2.2\geneticprog\bench>unboxedstrict.exe
280000000
Execution: 0.04 Garbage collection: 0.00 Total: 0.04
*/
module unboxedstrict
import StdEnv
fn i a acc | i<1 = acc
fn i a=:{[i]=b} acc
# a= {a&[i]= b+3.0}
# (c, a)= a![i]
= fn (i-1) a (c+acc)
Start= fn 2000000 vt 0.0
where
vt:: !.{#!Real}
vt = createArray 2000001 137.0
--- On Thu, 10/15/09, Simon Peyton-Jones <[email protected]> wrote:
From: Simon Peyton-Jones <[email protected]>
Subject: RE: [Fwd: Re: [clean-list] Clean versus Haskell]
To: "Adrian Hey" <[email protected]>, "[email protected]"
<[email protected]>
Received: Thursday, October 15, 2009, 1:22 AM
I submitted Philippos as a performance bug to GHC's trac, here:
http://hackage.haskell.org/trac/ghc/ticket/3586
If you follow the link you'll see lots of commentary, including a version that
generates code twice as fast as Clean's, and is purely functional.
That said, I think it's v bad that a straightforward program runs so slowly,
and it's certainly true that this is an area we could pay more attention to.
(Trouble is, there are so many such areas!)
Meanwhile, I'm curious: are the arrays in Philippos's program strict? Or
lazy? If strict, that's a pretty big difference. (The "STU" arrays mentioned
in the above link are strict and unboxed; that's what the "U" means.)
Simon
| -----Original Message-----
| From: [email protected]
[mailto:[email protected]] On
| Behalf Of Adrian Hey
| Sent: 15 October 2009 07:33
| To: [email protected]
| Subject: [Fwd: Re: [clean-list] Clean versus Haskell]
|
|
| -------- Original Message --------
| Subject: Re: [clean-list] Clean versus Haskell
| Date: Thu, 15 Oct 2009 06:41:20 +0100
| From: Adrian Hey <[email protected]>
| To: Philippos Apolinarius <[email protected]>
| References: <[email protected]>
|
| Hello Philippos,
|
| GHC has a long standing performance bug for garbage collection and
| mutable arrays:
|
| http://hackage.haskell.org/trac/ghc/ticket/650
|
| For some reason they haven't (can't?) fixed it. I guess the
| authors of Haskell/ghc shootout entries are aware of this so don't
| use native mutable arrays in their entries (at least not those that
| show haskell/ghc to be "fast" :-)
|
| Regards
| --
| Adrian Hey
|
| Philippos Apolinarius wrote:
| > I wrote a very simple program to check whether Haskell improved its array
| processing libraries or not. Here is how to compile and run the program
arr.hs in
| Haskell (I have used the GHC compiler):
| >
| >> ghc -O arr.hs -o arr.exe
| >
| > $ time arr.exe +RTS -K32000000
| > 2.8e8
| >
| > real 0m3.938s
| > user 0m0.031s
| > sys 0m0.000s
| >
| > The same program in Clean:
| > C:\Clean 2.2\exemplos\console>arraytest.exe
| > 280000000
| > Execution: 0.01 Garbage collection: 0.01 Total: 0.03
| >
| > C:\Clean 2.2\exemplos\console>arraytest.exe
| > 280000000
| > Execution: 0.01 Garbage collection: 0.01 Total: 0.03
| >
| > This means that Clean is 390 times faster than Haskell in this particular
problem.
| These results makes me worder whether Haskell is safer than Clean. It turns
out that
| Haskell checks index out of range at runtime, exactly like Clean. Larger
problems
| make the difference between Clean and Haskell even worse. For instance, neural
| networks like the one described in Schmidtt et al. run 400 times faster in
Clean.
| >
| > Haskell seems to be slow, and not safe. For instance, GHC compiler does not
at a
| program trying to write into a closed handle.
| >
| > module Main where
| > import System( getArgs )
| > import IO
| >
| > main = do
| > args <- getArgs
| > if (length args /= 2)
| > then putStr "Usage: f1a f2a <n>"
| > else (do
| > fromHandle <- openFile (head args) ReadMode
| > contents <- hGetContents fromHandle
| > toHandle <- openFile (head (tail args)) WriteMode
| > hClose toHandle -- Comment this line
| > hPutStr toHandle contents
| > hClose toHandle
| > putStr "Done.")
| >
| > The Clean equivalent program is somewhat smaller. In my opinion it is
easier to
| understand. What is more important, Clean compiler balks at closed handles.
| >
| > module cleancopy
| > import StdEnv, ArgEnv
| >
| > Start w
| > # argv= getCommandLine
| > | size argv < 3 = abort "Usage, etc."
| > # (ok, f, w)= fopen argv.[1] FReadText w
| > (contents, f)= freads f 64000
| > (ok, f, w)= fopen argv.[2] FWriteText w
| > f= fwrites contents f
| > = fclose f w
| >
| > Below you will find the array examples. You can check that Clean is really
much
| faster than Haskell. I wonder why the Benchmarks Game site does not report
such a
| large difference between Haskell and Clean performances. I believe that
people who
| wrote Haskell benchmarks for the Benchmarks Game site cheated in using foreign
| pointers to access arrays.
| >
| > -- arr.hs
| > import Control.Monad.ST
| > import Data.Array.ST
| > main = print $ runST
| > (do arr <- newArray (1,2000000)
| > 137.0 :: ST s
| > (STArray s
| > Int Double)
| > a <- readArray arr 1
| > b <- readArray arr 1
| > fn 2000000 arr 0.0 )
| >
| >
| > fn i a acc | i < 1 = do (return acc)
| > fn i a acc= do
| > b <- readArray a i
| > writeArray a i (b+3.0)
| > c <- readArray a i
| > fn (i-1) a (acc+c)
| >
| > //Clean version
| > module arraytest
| > import StdEnv
| > fn i a acc | i<1 = acc
| > fn i a=:{[i]=b} acc
| > # a= {a&[i]= b+3.0}
| > # (c, a)= a![i]
| > = fn (i-1) a (c+acc)
| >
| > Start= fn 2000000 vt 0.0
| > where
| > vt:: .{#Real}
| > vt = createArray 2000001 137.0
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