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 . (`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).

20 thoughts on “Smoking fast Haskell code using GHC’s new LLVM codegen

  1. Is there still no way of plotting criterion graphs together, or changing the scales so they match? Not a big deal, but it makes it harder to read (although I suppose they’re sharp enough that it’d end up as two spikes on a graph.)

    Anyway, that looks fantastic. Is it a full backend? Should FFI still work?

  2. Interestingly, from a quick scan through the thesis, it looks like David attributes a lot/at least some of the tight loops performance advantage to not having pinned the STG registers except at function entrance and exit.

    Specifically, the bottom of page 42 and top of page 43 detail how he used a custom LLVM custom calling convention (this seems to be what the LLVM patch is for) to make sure the STG registers were always set at function entry and exit. This is sufficient to make the rest of the RTS happy.

    What it means, though, is that the LLVM compiler is free to spill the registers in the code body. THe bottom of page 53 and top of page 54 comment on this can be critical to speeding up tight loops (specifically, DPH ones where he was seeing some impressive performance gains).

  3. How is the code generation from LLVM to assembly set up?
    I presume it’s running ‘opt’ somewhere in there?
    The flags to the optimizer probably needs some tweaking, for instance, you might want to unroll loops a bit for all of the loops in your examples, since it can make a big difference.

  4. BTW, most transformations the LLVM code generator has made look pretty normal: changing loops to have a single jump, unrolling, change of induction variable…

  5. With modern chips the code and scratch data will be in the cache; doesn’t the time to access data in large arrays determine execution time – and the number of instructions executed is less important?

  6. I am hoping the LLVM backend will make a x86_64 backend on OS X easier to create. Does anyone know if it will have such an effect?

  7. Any chance of a follow up with some comparisons to C compiled with GCC? I’m more interested how well we’re competing at the moment than how much better we’re doing (C gives a nice “damn we’re good, look at us!” baseline for when we’re faster than it :)).

  8. Question from a Haskell novice:

    If (boxed) vectors from the new vector package can handle Algorithmic Datatypes as well as offering improved performance, what, if any, is the remaining use of ordinary Haskell lists?

    The only thing I can think of right now is that you wouldn’t have to import the Data.Vector package, but surely there’s some other remaining advantage?

  9. Lists have a lazy spine, which means than can be used for control structures, even without fusion, so they’ll continue to have uses.

    For storing actual data, I think I’ll probably use vector for anything with a performance concern.

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