6.2. Faster: producing a program that runs quicker
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## 6.2. Faster: producing a program that runs quicker

The key tool to use in making your Haskell program run faster are GHC's profiling facilities, described separately in Chapter 5, Profiling. There is no substitute for finding where your program's time/space is really going, as opposed to where you imagine it is going.

Another point to bear in mind: By far the best way to improve a program's performance dramatically is to use better algorithms. Once profiling has thrown the spotlight on the guilty time-consumer(s), it may be better to re-think your program than to try all the tweaks listed below.

Another extremely efficient way to make your program snappy is to use library code that has been Seriously Tuned By Someone Else. You might be able to write a better quicksort than the one in `Data.List`, but it will take you much longer than typing `import Data.List`.

Please report any overly-slow GHC-compiled programs. Since GHC doesn't have any credible competition in the performance department these days it's hard to say what overly-slow means, so just use your judgement! Of course, if a GHC compiled program runs slower than the same program compiled with NHC or Hugs, then it's definitely a bug.

Optimise, using `-O` or `-O2`:

  This is the most basic way to make your program go faster. Compilation time will be slower, especially with `-O2`.

  At present, `-O2` is nearly indistinguishable from `-O`.

Compile via LLVM:

  The LLVM code generator can sometimes do a far better job at producing fast code than the native code generator. This is not universal and depends on the code. Numeric heavy code seems to show the best improvement when compiled via LLVM. You can also experiment with passing specific flags to LLVM with the `-optlo` and `-optlc` flags. Be careful though as setting these flags stops GHC from setting its usual flags for the LLVM optimiser and compiler.

Overloaded functions are not your friend:

  Haskell's overloading (using type classes) is elegant, neat, etc., etc., but it is death to performance if left to linger in an inner loop. How can you squash it?

  Give explicit type signatures:

    Signatures are the basic trick; putting them on exported, top-level functions is good software-engineering practice, anyway. (Tip: using `-fwarn-missing-signatures` can help enforce good signature-practice).

    The automatic specialisation of overloaded functions (with `-O`) should take care of overloaded local and/or unexported functions.

  Use `SPECIALIZE` pragmas:

    Specialize the overloading on key functions in your program. See Section 7.18.8, “SPECIALIZE pragma” and Section 7.18.9, “SPECIALIZE instance pragma ”.

  “But how do I know where overloading is creeping in?”:

    A low-tech way: grep (search) your interface files for overloaded type signatures. You can view interface files using the `--show-iface` option (see Section 4.7.7, “Other options related to interface files”).

        % ghc --show-iface Foo.hi | egrep '^[a-z].*::.*=>'

Strict functions are your dear friends:

  and, among other things, lazy pattern-matching is your enemy.

  (If you don't know what a “strict function” is, please consult a functional-programming textbook. A sentence or two of explanation here probably would not do much good.)

  Consider these two code fragments:

      f (Wibble x y) =  ... # strict

      f arg = let { (Wibble x y) = arg } in ... # lazy

  The former will result in far better code.

  A less contrived example shows the use of `cases` instead of `lets` to get stricter code (a good thing):

      f (Wibble x y)  # beautiful but slow
        = let
              (a1, b1, c1) = unpackFoo x
              (a2, b2, c2) = unpackFoo y
          in ...

      f (Wibble x y)  # ugly, and proud of it
        = case (unpackFoo x) of { (a1, b1, c1) ->
          case (unpackFoo y) of { (a2, b2, c2) ->
          ...
          }}

GHC loves single-constructor data-types:

  It's all the better if a function is strict in a single-constructor type (a type with only one data-constructor; for example, tuples are single-constructor types).

Newtypes are better than datatypes:

  If your datatype has a single constructor with a single field, use a `newtype` declaration instead of a `data` declaration. The `newtype` will be optimised away in most cases.

“How do I find out a function's strictness?”

  Don't guess—look it up.

  Look for your function in the interface file, then for the third field in the pragma; it should say `Strictness: <string>`. The `<string>` gives the strictness of the function's arguments: see the GHC Commentary for a description of the strictness notation.

  For an “unpackable” `U(...)` argument, the info inside tells the strictness of its components. So, if the argument is a pair, and it says `U(AU(LSS))`, that means “the first component of the pair isn't used; the second component is itself unpackable, with three components (lazy in the first, strict in the second \& third).”

  If the function isn't exported, just compile with the extra flag `-ddump-simpl`; next to the signature for any binder, it will print the self-same pragmatic information as would be put in an interface file. (Besides, Core syntax is fun to look at!)

Force key functions to be `INLINE`d (esp. monads):

  Placing `INLINE` pragmas on certain functions that are used a lot can have a dramatic effect. See Section 7.18.5.1, “INLINE pragma”.

Explicit `export` list:

  If you do not have an explicit export list in a module, GHC must assume that everything in that module will be exported. This has various pessimising effects. For example, if a bit of code is actually unused (perhaps because of unfolding effects), GHC will not be able to throw it away, because it is exported and some other module may be relying on its existence.

  GHC can be quite a bit more aggressive with pieces of code if it knows they are not exported.

Look at the Core syntax!

  (The form in which GHC manipulates your code.) Just run your compilation with `-ddump-simpl` (don't forget the `-O`).

  If profiling has pointed the finger at particular functions, look at their Core code. `lets` are bad, `cases` are good, dictionaries (`d.<Class>.<Unique>`) [or anything overloading-ish] are bad, nested lambdas are bad, explicit data constructors are good, primitive operations (e.g., `eqInt#`) are good,…

Use strictness annotations:

  Putting a strictness annotation ('!') on a constructor field helps in two ways: it adds strictness to the program, which gives the strictness analyser more to work with, and it might help to reduce space leaks.

  It can also help in a third way: when used with `-funbox-strict-fields` (see Section 4.10.2, “`-f*`: platform-independent flags”), a strict field can be unpacked or unboxed in the constructor, and one or more levels of indirection may be removed. Unpacking only happens for single-constructor datatypes (`Int` is a good candidate, for example).

  Using `-funbox-strict-fields` is only really a good idea in conjunction with `-O`, because otherwise the extra packing and unpacking won't be optimised away. In fact, it is possible that `-funbox-strict-fields` may worsen performance even with `-O`, but this is unlikely (let us know if it happens to you).

Use unboxed types (a GHC extension):

  When you are really desperate for speed, and you want to get right down to the “raw bits.” Please see Section 7.2.1, “Unboxed types” for some information about using unboxed types.

  Before resorting to explicit unboxed types, try using strict constructor fields and `-funbox-strict-fields` first (see above). That way, your code stays portable.

Use `foreign import` (a GHC extension) to plug into fast libraries:

  This may take real work, but… There exist piles of massively-tuned library code, and the best thing is not to compete with it, but link with it.

  Chapter 8, Foreign function interface (FFI) describes the foreign function interface.

Don't use `Float`s:

  If you're using `Complex`, definitely use `Complex Double` rather than `Complex Float` (the former is specialised heavily, but the latter isn't).

  `Floats` (probably 32-bits) are almost always a bad idea, anyway, unless you Really Know What You Are Doing. Use `Double`s. There's rarely a speed disadvantage—modern machines will use the same floating-point unit for both. With `Double`s, you are much less likely to hang yourself with numerical errors.

  One time when `Float` might be a good idea is if you have a lot of them, say a giant array of `Float`s. They take up half the space in the heap compared to `Doubles`. However, this isn't true on a 64-bit machine.

Use unboxed arrays (`UArray`)

  GHC supports arrays of unboxed elements, for several basic arithmetic element types including `Int` and `Char`: see the `Data.Array.Unboxed` library for details. These arrays are likely to be much faster than using standard Haskell 98 arrays from the `Data.Array` library.

Use a bigger heap!

  If your program's GC stats (`-S` RTS option) indicate that it's doing lots of garbage-collection (say, more than 20% of execution time), more memory might help—with the `-M<size>` or `-A<size>` RTS options (see Section 4.17.3, “RTS options to control the garbage collector”).

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