Evaluation strategies and synchronization: things to watch for

A talk presented at the weekly Galois Developer Symposium.

The source of the talk and examples are all online.

This talk shows two areas relating to evaluation strategies in Haskell, and how they can subtly interact with threads, synchronization and performance. In addition, we briefly look at the interaction with asynchronous exceptions.

In particular,

  • Be careful with lazy values in synchronization variables
  • Consider using the strict-concurrency package for synchronization
  • All modify* functions should be very very carefully used
  • Interactions between async exceptions (e.g. block/killThread) and threads need expert eyes.

Fusion makes functional programming fun!

It’s deeply satisfying when the stream fusion optimization kicks in, in GHC Haskell. It is a powerful illustration of how the absence of side effects makes the compiler’s job so much easier, and lets it be far more aggressive.

Here’s a little example to show the real expressive power that we get when we have representation-changing optimizations like fusion to rely on. A simple example showing how good GHC is doing at this kind of stuff, these days: add a sequence of numbers. I’ve kept it super minimal so you can mentally compute each transformation to see GHC at work. (Of course there are lots of other ways to write trivial computations like this, but anything more substantial will make the assembly much harder to follow. So for teaching purposes, we’ll keep the source problem simple. Now, you can also use any of the 100+ other functions that fuse to construct all sorts of interesting programs with no closed form solution. Get at it!).

We begin with a pipeline of multiple recursive functions, brought together with function composition. As high level as it gets, and any junior Haskell program should be able to write stuff like this:

import qualified Data.Vector as U

    main = print . U.sum $ U.enumFromTo 1 (100000000 :: Int)

Now, semantically, there is an intermediate 100 million element array being created by enumFromTo. Try doing that in your favorite strict language. However…

… fusion kicks in, and GHC turns our program, via the source-to-source optimization phase, into a single tail recursive function, using unboxed values only .. with no intermediate array! No heap allocaiton, no stack, only one loop. A different algorithm!

    loop :: Int# -> Int# -> Int#
    loop x y = case y <= 100000000 of
          False -> x
          True  -> loop (x + y) (y + 1)

The compiler reordered our computations pretty wildly, and it can do this because there are no side effects by default — making large scale transformations like this far more feasible. I used ghc 6.13 with the llvm patches, and ghc -Odph -fllvm –make A.hs

The programmer didn’t have to think about recursion at all in the original program, let alone manual unboxing. Thanks GHC!

And now this tail recursive function gets compiled into an imperative loop via the codeGen phase. First, with the native code backend to GHC:

       cmpq $10000000,%rsi
       jle .Lc219
       movq %r14,%rbx
       movq (%rbp),%rax
       jmp *(%rax)
       addq %rsi,%r14
       incq %rsi
       jmp loop

And again, with the LLVM backend to GHC:

    leaq    1(%rsi), %rax
    addq    %rsi, %r14
    cmpq    $10000001, %rax
    jge     .LBB1_5
    addq    $2, %rsi
    addq    %rax, %r14
test:                                # %tailrecurse
    cmpq    $10000001, %rsi
    jl      loop

And that’s it. Our kernel code is as low level and un-functional as you get, but our input program was the most classic of high level approaches: a pipeline of functions held together with composition, with intermediate data structures all over the place.

And it performs!

With -fasm: mean: 92.9 ms:

With -fllvm: mean: 79.3ms (15% improvement with -fllvm):

Maybe our compiler is — sometimes — sufficiently smart.

To me, the fusion optimization is the epitome of what functional programming is about. I can write code at a very high level, throwing functions and composition around like candy, and the compiler will make stunning, complexity-changing transformations on my code, so it just runs damn fast. Can your language — functional or otherwise — do this?

Modern Benchmarking in Haskell

Thanks to work by Bryan O’Sullivan, there has been a rennaissance in performance benchmarking tools for Haskell, built upon Criterion.

Compared to most other benchmarking frameworks (for any programming language, not just Haskell), criterion focuses on being easy to use, informative, and robust.

The Galois Tech Talk of Feb 23rd presented this work. You can read the slides online, or find the source and examples here.

Criterion uses statistically robust mechanisms for sampling and computing sound microbenchmark results, and is more stable in the presence of noise on the system than naive timings.

Criterion has in turn spawned some extensions:

  • Progression: compare different criterion graphs
  • NoSlow: a new array benchmark suite based on Criterion

In this talk I will present these tools, how to use them, and how to make your performance benchmarks in Haskell, or languages Haskell can talk to, more reliable. In addition, we’ll explore benchmarks using the new vector package, and GHC’s llvm backend.

Smoking fast Haskell code using GHC’s new LLVM codegen

In this post we’ll play with GHC’s new LLVM code generator backend, and see how much faster some Haskell programs are when compiled with LLVM instead of GCC.

For the kind of loops we get from stream fusion, the -fllvm backend produced a lot better code, up to 3x faster in some cases. There are pretty graphs, and some smoking hot new technology.


This week David Terei announced that his work on an LLVM code generator backend for the Glasgow Haskell Compiler was ready to try out. Initial reports from his undergraduate thesis held that the LLVM code generator was competitive with the current GHC native code generator, a bit slower than the C backend in general (which uses GCC for code generation), but, tantalisingly, should produce big speedups for particular Haskell programs. In particular, tight loops of the kind generated by the bytestring, vector, data parallel arrays or text libraries. David reported speedups of 25% over the previous best performance we’d got from GHC for data parallel code.

I was very keen to try it out on the vector library — a fast, fusible numerical arrays package (similar to NumPy), which generates some very tight loops. Under the C backend, GCC has been failing to spot that the code GHC generates were actually loops, and this lead to GCC optimizing the generated code pretty badly. The native code generator does ok, but doesn’t have a lot of the clever low-level optimizations we need for really good bare metal performance.

So how would the new LLVM backend do?

Setting up

To try out the LLVM backend I followed the instructions on the wiki.

  • Check out GHC HEAD from darcs.
  • Apply the LLVM patch.
  • Check out LLVM from svn
  • Apply the GHC patch
  • Build your GHC.

This worked out of the box, and I now have a GHC 6.13 with the -fllvm flag.

$ ghc --info
 [("Project name","The Glorious Glasgow Haskell Compilation System")
 ,("Project version","6.13.20100221")
 ,("Booter version","6.12.1")
 ,("Have interpreter","YES")
 ,("Object splitting","YES")
 ,("Have native code generator","YES")
 ,("Have llvm code generator","YES")
 ,("Support SMP","YES")
 ,("Tables next to code","NO")
 ,("Win32 DLLs","")
 ,("RTS ways","l debug  thr thr_debug thr_l  ")
 ,("Leading underscore","NO")
 ,("Debug on","False")

Running on a dual core Core 2 laptop:

$ uname -msr
 Linux 2.6.32-ARCH x86_64

You can then install packages as normal, via cabal, and add the -fllvm flag to see GHC build things via the new backend:

$ cabal install primitive --ghc-options=-fllvm

The packages I’m interested in are:

And some helper code in:

I also modifed the ghc-core tool to support showing the LLVM generated assembly.

Warm up lap

Let’s check the backend is working (remember to add the -fllvm flag):

$ ghc -O2 --make A.hs -fllvm -fforce-recomp
[1 of 1] Compiling Main             ( A.hs, A.o )
Linking A ...
$ time ./A
./A  0.00s user 0.00s system 61% cpu 0.005 total

Good! The LLVM backend is generating working code for x86_64/Linux. Now, something more ambitious … a program from the shootout.

A shootout program

So let’s find some code that’s already been optimized. I’l compile the pidgits shootout benchmarks (where Haskell’s already the fastest entry).

First, with the native code gen:

$ ghc -O2 -fasm A.hs –make -fforce-recomp

$ time ./A 10000 > /dev/null
./A 10000 > /dev/null 3.19s user 0.03s system 91% cpu 3.509 total

With the old GCC backend:

$ ghc -O2 -fvia-C -optc-O3 A.hs –make -fforce-recomp

$ time ./A 10000 > /dev/null
./A 10000 > /dev/null 2.89s user 0.03s system 97% cpu 2.988 total

And with the -fllvm backend:

$ ghc -O2 -fllvm A.hs –make -fforce-recomp

$ time ./A 10000 > /dev/null
./A 10000 > /dev/null 2.86s user 0.02s system 98% cpu 2.936 total

Woo. It runs, and we get a speedup! Now for some serious business.

The Vector Package

Vector is a Haskell library for working with arrays. It provides several array types (boxed, unboxed, C), with a rich interface similar to the lists library, and some functions reminiscent of Data Parallel Haskell. There’s a tutorial on how to use it.

The interface is built entirely around stream fusion combinators — a general form of classic loop fusion made possible by purity. When you do multiple passes over the data (e.g. sum/map/fold/filter/…) the compiler will common up the loops, and discard intermediate arrays, making the code potentially very fast.

The loops that are generated tend to be very register heavy, do no heap allocation, and benefit from clever imperative loop optimizations. Unfortunately, the GCC backend to GHC doesn’t spot that these are actually loops, so doesn’t get to fire many optimizations.

The promise of the LLVM backend is that it will recognize the loops GHC generates from fused code. Let’s see how it performs.

To benchmark these programs, I’ll use the criterion and progression benchmarking libraries. (I had to build the darcs version of gtk2hs, and compiler data accessor-template with the -ftemplate_2_4 flag)

Simple loops

To start off, let’s generate 1 billion ints, sum them, print the result. That should tell us if our loops are efficient:

import qualified Data.Vector as U
main = print . U.sum $ U.enumFromTo 1 (1000000000 :: Int)

There are two loops in this program. enumFromTo and sum.

The core

GHC compiles these two loops into a single loop, when compiled with -O2 or -Odph:

loop  :: Int# -> Int# -> Int#
loop x y =
     case <=# y 1000000000 of
         False -> x
         True  ->  loop (x +# y) (y +# 1)

This is perfect. We write “sum (enumFromTo 1 n)” and we get a non-allocating loop.

The native backend

GHC 6.13 with the native code generator generates the following assembly for the inner loop:

 cmpq $1000000000,%rsi
 jle .Lc21x
 movq %r14,%rbx
 movq (%rbp),%rax
 jmp *(%rax)
 addq %rsi,%r14
 incq %rsi
 jmp Main_mainzuzdszdwfoldlMzqzuloop_entry

which runs in:

$ time ./enum
 ./enum  1.00s user 0.00s system 99% cpu 1.008 total

The  C backend

GHC 6.12.1 with the C backend, (-fvia-C -optc-O3) (I’m having trouble linking programs with the C backend and GHC 6.13), yields a pretty small loop:

 cmpq    $1000000000, %r14
 movq    %r14, %rax
 jle     .L2
 movq    %rsi, %rbx
 jmp     *(%rbp)
 leaq    1(%r14), %r14
 addq    %rax, %rsi
 jmp     Main_mainzuzdszdwfoldlMzqzuloop_info

Which runs slower than the native code generator:

$ time ./enum
 ./enum  1.09s user 0.00s system 99% cpu 1.100 total

The LLVM backend

With -O2 -fllvm we get very different code, and it is a bit harder to work out what is going on. LLVM transforms the code far more aggressively.

 leaq    1(%rsi), %rax
 addq    %rsi, %r14
 cmpq    $1000000001, %rax
 jge     .LBB1_5                 # loop exit
 addq    $2, %rsi
 addq    %rax, %r14
 .LBB1_1:                        # %tailrecurse
 cmpq    $1000000001, %rsi
 jl      .LBB1_2

And the proof is in the pudding:

$ time ./enum
 ./enum  0.48s user 0.01s system 99% cpu 0.488 total

This is the fastest Haskell we’ve ever generated for this little benchmark (at least without manual loop unrolling)!

The LLVM backend more than halved the running time for this simple loop. But remember: general benchmarks aren’t seeing these kind of speedups — LLVM is really excelling itself at the tight numeric code.

Here’s the data presented in a slightly different form, with criterion and progression. The numbers are slightly different, since we won’t inline the length of the vector argument, and we’re wrapping the code in benchmarking wrappers. I wasn’t able to get -fvia-C programs to link under the HEAD, so we’ll exclude those from graphs, but report them in text form.

With the -fasm backend:

With the LLVM backend:

Or side-by-side with the progression package:

The -fasm backend under the progression tool ran around ~1s for each billion ints, while -fllvm was around 0.8s. Note that we get slightly different timings with the loops under each benchmarking tool, due to how the benchmark program and wrapper are optimized.


Zips are another good candidate, since they turn into nested loops. So, e.g.

import qualified Data.Vector as U
import Data.Bits
main = print . U.sum . U.map (`shiftL` 1) $ U.zipWith (*)
                        (U.enumFromTo 1 (100000000 :: Int))
                        (U.replicate (100000000 :: Int) 42)

Which fuses to this set of loops:

loop  :: Int# -> Int# -> Int# -> Int#
loop =
  \ (sc_s29b :: Int#)
    (sc1_s29c :: Int#)
    (sc2_s29d :: Int#) ->
    case <=# sc1_s29c 100000000 of _ {       False -> sc_s29b;
      True ->
        case <=# sc2_s29d 0 of _ {           False ->
                 sc_s29b (uncheckedIShiftL# (*# sc1_s29c 42) 1))
              (+# sc1_s29c 1)
              (-# sc2_s29d 1);
          True -> sc_s29b

Which, again, is perfect Core. All those functions combined into a single non-allocating loop.


        cmpq $100000000,%rsi
        jle .Lc2aE
        movq %r14,%rbx
        movq (%rbp),%rax
        jmp *(%rax)
        testq %rdi,%rdi
        jle .Lc2aH
        movq %rsi,%rax
        imulq $42,%rax
        shlq $1,%rax
        addq %rax,%r14
        incq %rsi
        decq %rdi
        jmp Main_mainzuzdszdwfoldlMzqzuloop_entry
        movq %r14,%rbx
        movq (%rbp),%rax
        jmp *(%rax)

Which is reasonable:

$ time ./zipwith
./zipwith 0.24s user 0.00s system 99% cpu 0.246 total

With the -fvia-C -optc-O3 backend, just the inner loop, since that’s easy to read:

        cmpq    $100000000, %rsi
        jg      .L6
        testq   %r14, %r14
        jle     .L6
        leaq    (%rsi,%rsi,4), %rcx
        leaq    -1(%r14), %r14
        leaq    (%rsi,%rcx,4), %rcx
        leaq    1(%rsi), %rsi
        leaq    (%rdi,%rcx,4), %rdi
        jmp     Main_mainzuzdszdwfoldlMzqzuloop_info

Which runs in about the same time as the -fasm backend:

$ time ./zipwith
./zipwith  0.25s user 0.00s system 99% cpu 0.251 total

With -fllvm the code is wildly different, and I find it pretty hard to reconstruct what transformatoins LLVM has done.

# BB#0:                                 # %c2cf
        subq    $8, %rsp
        imulq   $84, %rsi, %rax
        jmp     .LBB1_1
.LBB1_3:                                # %n2cN
                                        #   in Loop: Header=BB1_1 Depth=1
        incq    %rsi
        decq    %rdi
        addq    %rax, %r14
        addq    $84, %rax
.LBB1_1:                                # %tailrecurse
                                        # =>This Inner Loop Header: Depth=1
        cmpq    $100000001, %rsi        # imm = 0x5F5E101
        jge     .LBB1_4
                                        #   in Loop: Header=BB1_1 Depth=1
        testq   %rdi, %rdi
        jg      .LBB1_3
.LBB1_4:                                # %n2ck
        movq    (%rbp), %rax
        movq    %r14, %rbx
        movq    (%rax), %r11
        addq    $8, %rsp
        jmpq    *%r11  # TAILCALL

The “inner loop” is interesting. Nothing like what -fasm or -fvia-C generate. And it’s way faster:

$ time ./zipwith
./zipwith 0.15s user 0.00s system 99% cpu 0.154 total

So yeah, 40% faster!


Here, under criterion (same code, but different values of n), With the -fasm backend, mean execution time 186ms:

With the -fllvm backend, 135 ms  (27% improvement):


Heavily nested zips are probably the best cases for LLVM, and we see the -fllvm backend do some pretty wild stuff with this:

import qualified Data.Vector.Unboxed as U import Data.Bits main = print . U.sum $ U.zipWith3 (\x y z -> x * y * z) (U.enumFromTo 1 (100000000 :: Int)) (U.enumFromTo 2 (100000001 :: Int)) (U.enumFromTo 7 (100000008 :: Int))

Which fuses to:

main_$s$wfoldlM'_loop [Occ=LoopBreaker]
  :: Int#     -> Int# -> Int# -> Int# -> Int#

main_$s$wfoldlM'_loop =
  \ (sc_s2jh :: Int#)
    (sc1_s2ji :: Int#)
    (sc2_s2jj :: Int#)
    (sc3_s2jk :: Int#) ->
    case  sc_s2jh;
      True ->
        case  sc_s2jh;
          True ->
            case  sc_s2jh;
              True ->
                     sc_s2jh (*# (*# sc1_s2ji sc2_s2jj) sc3_s2jk))
                  (+# sc1_s2ji 1)
                  (+# sc2_s2jj 1)
                  (+# sc3_s2jk 1)

Great core. With the -fasm backend:

        cmpq $100000000,%rsi
        jle .Lc2ls
        movq %r14,%rbx
        movq (%rbp),%rax
        jmp *(%rax)
        cmpq $100000001,%rdi
        jle .Lc2lu
        movq %r14,%rbx
        movq (%rbp),%rax
        jmp *(%rax)
        cmpq $100000008,%r8
        jle .Lc2lx
        movq %r14,%rbx
        movq (%rbp),%rax
        jmp *(%rax)
        movq %rdi,%rax
        imulq %r8,%rax
        movq %rsi,%rcx
        imulq %rax,%rcx
        addq %rcx,%r14
        incq %rsi
        incq %rdi
        incq %r8
        jmp Main_mainzuzdszdwfoldlMzqzuloop_entry

Straight forward, and running it:

$ time ./zipwith3
./zipwith3  0.47s user 0.01s system 98% cpu 0.484 total

With -fvia-C -optc-O3:

        .p2align 4,,15
        .align 8
        .type Main_mainzuzdszdwfoldlMzqzuloop_info, @function
# 38 "/tmp/ghc10013_0/ghc10013_0.hc" 1
# 0 "" 2
        cmpq    $100000000, %rdi
        jg      .L9
        cmpq    $100000001, %rsi
        jg      .L9
        cmpq    $100000008, %r14
        .p2align 4,,5
        jg      .L9
        movq    %rsi, %r10
        leaq    1(%rsi), %rsi
        imulq   %rdi, %r10
        leaq    1(%rdi), %rdi
        imulq   %r14, %r10
        leaq    1(%r14), %r14
        leaq    (%r10,%r8), %r8
        jmp     Main_mainzuzdszdwfoldlMzqzuloop_info

And we get a faster result:

$ time ./zipwith3
./zipwith3  0.34s user 0.00s system 99% cpu 0.344 total

-fllvm, looks like some heavy loop unrolling:

Main_mainzuzdszdwfoldlMzqzuloop_entry:  # @Main_mainzuzdszdwfoldlMzqzuloop_entry
# BB#0:                                 # %c2oz
        subq    $56, %rsp
        cmpq    $100000002, %rdi        # imm = 0x5F5E102
        movl    $100000002, %eax        # imm = 0x5F5E102
        movq    $-2, %rdx
        movq    %r9, 40(%rsp)           # 8-byte Spill
        movq    %r15, 48(%rsp)          # 8-byte Spill
        movq    $-3, %r9
        movq    %r12, 32(%rsp)          # 8-byte Spill
        movq    %r8, %rbx
        movq    %r13, 24(%rsp)          # 8-byte Spill
        movq    %r14, 16(%rsp)          # 8-byte Spill
        leaq    1(%rdi), %r13
        cmovgq  %rdi, %rax
        negq    %rax
        leaq    -1(%rdi,%rax), %rcx
        cmpq    $100000009, %r8         # imm = 0x5F5E109
        movl    $100000009, %eax        # imm = 0x5F5E109
        cmovgq  %r8, %rax
        negq    %rax
        leaq    -1(%r8,%rax), %rax
        cmpq    %rcx, %rax
        cmovaq  %rax, %rcx
        cmpq    $100000001, %rsi        # imm = 0x5F5E101
        movl    $100000001, %eax        # imm = 0x5F5E101
        cmovgq  %rsi, %rax
        negq    %rax
        leaq    -1(%rsi,%rax), %rax
        cmpq    %rax, %rcx
        cmovbeq %rax, %rcx
        imulq   %rdi, %rbx
        imulq   %rsi, %r13
        movq    %rcx, %r10
        subq    %rcx, %rdx
        subq    %rcx, %r9
        imulq   %rsi, %rbx
        addq    %rdi, %r13
        notq    %r10
        movq    %r10, %rax
        imulq   %r10, %rbx
        mulq    %rdx
        addq    16(%rsp), %rbx          # 8-byte Folded Reload
        movq    %rax, %r11
        movq    %rdx, %r15
        movq    %r15, %r12
        movq    %r11, %rax
        andq    $1, %r15
        imulq   %r9, %r12
        mulq    %r9
        shldq   $63, %r11, %r15
        leaq    (%r8,%rdi), %r9
        addq    %rdx, %r12
        movq    $-4, %rdx
        addq    %rsi, %r9
        subq    %rcx, %rdx
        movq    %r12, %r14
        andq    $1, %r12
        leaq    6(%r9,%r9), %r10
        movabsq $6148914691236517205, %r9 # imm = 0x5555555555555555
        movq    %rdx, 8(%rsp)           # 8-byte Spill
        imulq   %rdx, %r14
        leaq    1(%rdi,%rsi), %rdx
        shldq   $63, %rax, %r12
        imulq   %r8, %rdx
        imulq   %r12, %r10
        leaq    1(%rdx,%r13), %rdx
        imulq   %r10, %r9
        imulq   %r15, %rdx
        addq    %rdx, %rbx
        mulq    8(%rsp)                 # 8-byte Folded Reload
        subq    %r9, %rbx
        movq    %r8, %r9
        decq    %r8
        subq    %rcx, %r9
        addq    %rdx, %r14
        movq    %rdi, %rdx
        decq    %r9
        shldq   $62, %rax, %r14
        movq    %rsi, %rax
        subq    %rcx, %rdx
        andq    $-2, %r14
        subq    %rcx, %rax
        decq    %rdx
        addq    %rbx, %r14
        decq    %rax
        .align 16
.LBB2_1:                                # %tailrecurse
                                        # =>This Inner Loop Header: Depth=1
        cmpq    $100000001, %rsi        # imm = 0x5F5E101
        jge     .LBB2_4
# BB#2:                                 # %c2oD
                                        #   in Loop: Header=BB2_1 Depth=1
        cmpq    $100000002, %rdi        # imm = 0x5F5E102
        jge     .LBB2_4
# BB#3:                                 # %c2p5
                                        #   in Loop: Header=BB2_1 Depth=1
        incq    %rsi
        incq    %rdi
        incq    %r8
        cmpq    $100000009, %r8         # imm = 0x5F5E109
        jl      .LBB2_1
.LBB2_4:                                # %n2oE
        movq    (%rbp), %rcx
        movq    %r9, %r8
        movq    24(%rsp), %r13          # 8-byte Reload
        movq    32(%rsp), %r12          # 8-byte Reload
        movq    %r14, %rbx
        movq    %rax, %rsi
        movq    %rdx, %rdi
        movq    40(%rsp), %r9           # 8-byte Reload
        movq    48(%rsp), %r15          # 8-byte Reload
        movq    (%rcx), %r11
        addq    $56, %rsp
        jmpq    *%r11  # TAILCALL

And blows them all out of the water! 3x faster than -fasm! Twice as fast as -fvia-C -optc-O3.

$ time ./zipwith3
./zipwith3  0.16s user 0.00s system 99% cpu 0.158 total

From the Statistics package

The statistics package has some more “realistic” microbenchmarks. Let’s look at those. First, computing the mean of a large array of doubles (here all set to ‘pi’).

main = print (mean (V.replicate 1000000000 (pi :: Double)))

With the -fasm backend:

        testq %rsi,%rsi
        jle .Lc2b5
        cvtsi2sdq %r14,%xmm0
        movsd .Ln2b8(%rip),%xmm7
        subsd %xmm5,%xmm7
        divsd %xmm0,%xmm7
        addsd %xmm7,%xmm5
        incq %r14
        decq %rsi
        jmp Main_mainzuzdszdwfoldlMzuloop_entry

Simple, easy.

$ time ./mean
./mean  5.58s user 0.01s system 99% cpu 5.599 total

With the -fllvm backend:

Main_mainzuzdszdwfoldlMzuloop_entry:    # @Main_mainzuzdszdwfoldlMzuloop_entry
# BB#0:                                 # %c28E
        subq    $8, %rsp
        movsd   .LCPI3_0(%rip), %xmm0
        jmp     .LBB3_1
        .align 16
.LBB3_3:                                # %n28K.i
                                        #   in Loop: Header=BB3_1 Depth=1
        movapd  %xmm0, %xmm5
        cvtsi2sdq       %rcx, %xmm8
        addq    $-2, %rsi
        addq    $2, %r14
        subsd   %xmm7, %xmm5
        divsd   %xmm8, %xmm5
        addsd   %xmm7, %xmm5
.LBB3_1:                                # %tailrecurse
                                        # =>This Inner Loop Header: Depth=1
        testq   %rsi, %rsi
        jle     .LBB3_5
# BB#2:                                 # %n28K
                                        #   in Loop: Header=BB3_1 Depth=1
        movapd  %xmm0, %xmm7
        cvtsi2sdq       %r14, %xmm8
        leaq    -1(%rsi), %rax
        leaq    1(%r14), %rcx
        subsd   %xmm5, %xmm7
        testq   %rax, %rax
        divsd   %xmm8, %xmm7
        addsd   %xmm5, %xmm7
        jg      .LBB3_3
# BB#4:                                 # %c28J.i
        movq    (%rbp), %rdx
        movq    %rcx, %rbx
        movq    %rcx, %r14
        movq    %rax, %rsi
        movapd  %xmm7, %xmm5
        movq    (%rdx), %r11
        addq    $8, %rsp
        jmpq    *%r11  # TAILCALL
.LBB3_5:                                # %c28J
        movq    (%rbp), %rax
        movq    %r14, %rbx
        movq    (%rax), %r11
        addq    $8, %rsp
        jmpq    *%r11  # TAILCALL

And running it:

$ time ./mean
./mean  5.55s user 0.01s system 99% cpu 5.585 total

Some pretty wacky code, but a little faster.


The LLVM backend seems to be holding up to what we hoped: it does a better (some times much better) job on tight loops. We get better code than GHC has ever produced before. It seems pretty robust, so far everything I’ve tried has worked.

David’s benchmarks indicate that with the current — first attempt — at an LLVM backend most large programs aren’t noticeably faster, but I think the promise we see in these small examples justifies spending more time working on the LLVM backend to GHC. It has much more potential than the GCC backend.

Currently we’re not experimenting with the LLVM optimization layer at all — I think there’s likely to be a lot of win just tweaking those settings (and exposing them to the Haskell programmer via GHC flags).

Migrating from uvector to vector

Roman Leschinkskiy has released the 0.5 version of vector, the future standard non-parallel arrays library forGHC Haskell. This post covers some of the differences between it and uvector, and what to watch for when migrating code to use vector.

The summary is — as of Feb 15, 2010 — you can move to vector now.  In almost all cases you will get identical performance to uvector, but with a nicer interface. There are currently a few small gaps in the API, and a couple of performance tweaks are needed to particular functions, but they should not affect most people (and likely will be fixed in coming days). Note that you should use the -Odph optimization flag for the most reliable results.


vector is one of the results of the multi-year Data Parallel Haskell project, to develop high performance parallel bulk array processing for Haskell, allowing us to do very fast arrays (that is, transparently multicore parallel array processing, outperforming C or C++ by using cores more efficiently.)

While this project concentrates on data parallelism, it has also lead to new approaches to flat, sequential arrays for Haskell. The code has been spun-off in two different packages, which replace the fifteen year old array package with faster, more flexible types and functions:

These two libraries share a common origin, but have different engineering goals. Both libraries make heavy use of loop fusion based on streams to achieve excellent performance. (You can read more about that in a separate post).

uvector is a conservative attempt to develop fast, unboxed arrays with a flexible interface based on fusion, to replace Data.Array in the short term, while vector was immature. uvector has been in active service for about two years now, filling a gap while we waited for vector to mature. uvector has several users now, including haskell-criterion haskell-histogram-fill haskell-hnn haskell-monte-carlo haskell-mwc-random haskell-pqueue-mtlhaskell-safe-freeze haskell-statistics haskell-statistics-fusion haskell-uvector-algorithms. These packages in the medium term should consider moving to vector.

The vector library is far more ambitious, aiming to be the standard array library for high performance problems in all circumstances. It cleanly supports:

  • mutable arrays
  • immutable arrays
  • boxed
  • unboxed
  • storable types

As for uvector, unboxed representations are specialized at compile time via type families, and fusion is used throughout the interface. Unlike uvector, vector supports boxed arrays, and provides inplace fusion of mutable array operations.

If you need transparently parallel arrays, you should consider the dph package, distributed with GHC.

Current status

uvector is stable, and has gone into maintainance mode only. If you like it, you can safely continue to use it for  the foreseeable future, though any performance improvements in the fusion or array types developed in vector will not be backported to uvector.

vector, as of 0.5, has been declared “beta”. You can begin migrating code to use it.

Migrating your code

I’ve just finished porting the uvector micro benchmark suite to vector, and have the following notes on how to migrate your code to use unboxed arrays in the vector package.


The old uvector namespace was Data.Array.Vector with U suffix appended to names. That goes away, and instead you should:

import qualified Data.Vector as U

Most function names are identical, so we have in vector the obvious counterparts to uvector. All these are basically unchanged:

U.length U.null U.empty U.cons U.snoc U.replicate U.head
U.last U.init U.tail U.take U.drop U.map U.filter U.elem U.notElem
U.product U.sum U.maximum U.minimum U.foldl1 U.foldl U.dropWhile U.break
U.find U.all U.any U.and U.or U.maximumBy U.minimumBy

Some function names have changed:

U.++ replaces appendU
U.! replaces indexU

Some functions are missing:

lookupU repeatU

There are many new functions on vectors, in particular, on mutable arrays, and for bulk operations (backpermute, reverse, accum).

Better types

Notably, vector supports boxed types, so you can more easily store Haskell values in fusible arrrays (so you can have, e.g. Integer arrays).

Performance Wibbles

I found only a few differences in performance compared to uvector, and have notified Roman. These shouldn’t affect many users currently, and will likely disappear in coming days.

First, compile with -Odph instead of -O2, this fixes some optimization issues with zips, and probably other things.

Functions to watch out for:

  • zip, zipwith, zipwith3 — uvector was a lot faster (10x) in simple programs — however, moving to -Odph fixes zips entirely.
  • Different fusion results for ’empty’ (kind of a corner case)
  • eq seems to be about twice as slow. Unsure why.
  • Bools don’t seem to be bit packed? At least, Bool unboxed arrays seem a bit slower than in uvector.
  • U.last appears to be optimized differently, though doesn’t affect performance.
  • Pipelines ending in ‘null’ (another corner case) are fused differently (slightly worse performance).

And that’s about it.

As of this post, I’m officially declaring uvector to be in maintainance-only mode, and will be working to improve vector.

Playing with the new Haskell epoll event library

Bryan and Johan have been working hard on replacing the GHC runtime’s concurrency mechanism based on select, with a new one based on epoll. This should improve scalability of Haskell server code in a number of ways — more connections sec, more concurrent connections, and so on.

You can read about their work in a series of posts:

And you can find the current code for haskell-event on github: http://github.com/tibbe/event

After reading this post, on how epoll-based user land callbacks can outperform general purpose preemptive threads for non-compute bound IO work, I wanted to see what improvements we can hope for from an epoll-based version in Haskell.

The full code, which shows how to set up simple socket event notification using the event lib, is on the Haskell wiki, and will be useful for getting a flavor of the event lib use.

The results:

So while a simple forkIO/accept version, doing Handle and String IO would peak out around 7k req/sec, the epoll based version reached over 15k req/sec.

So that’s useful: good epoll-based event code for Haskell. The next step will be to see the redesign of Control.Concurrent based on epoll being merged into GHC.

Multicore Haskell Now! ACM Reflections | Projections 2009

Here are the

on approaches to multicore programming in Haskell, presented at the ACM Reflections | Projections conference at the University of Illinois.


Multicore computers are here: is your programming language ready for it? Haskell is: you can take an off-the-shelf copy of GHC and write high performance parallel programs right now.

If you want to program a parallel machine, a purely functional language such as Haskell is a good choice: purity ensures the language is by-default safe for parallel execution, (whilst traditional imperative languages are by-default unsafe). This foundation has enabled Haskell to become something of a melting pot for high level approaches to concurrent and parallel programming, all available with an industrial strength compiler and language toolchain, available now for mainstream multicore programming.

This talk will introduce the features Haskell provides for writing high level parallel and concurrent programs. In particular we’ll focus on lightweight semi-explicit parallelism, software transactional memory, and nested data parallelism, so you can go to work writing multicore programs in Haskell.

I’d like to thank Michael Ilseman, Sameer Sundresh and Jeff Green for their hospitality during my visit.


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