FP Complete

We do a lot of work at FP Complete in the realm of multi-machine computing for our customers. One of the things we need to do efficiently to get good speedups from adding more machines is optimizing serialization. Since our multi-machine work often revolves around scientific and mathematical computing, we primarily need to make sure that there is efficient serialization of primitive data types (like Int64 and Double), strict/unpacked product types (e.g. data Foo = Foo !Int64 !Double), and vectors of any of these.

For the impatient: benchmark results and repo

Not too long ago Francesco discovered and reported some issues with the binary package’s serialization of Doubles. In response to this, we internally switched over to using the cereal package instead, which resulted in a few more performance related PRs. However, when the FP Complete team discussed serialization, and particularly the needs that our multi-machine computing and other client projects require, we realized that we may have some opportunities to do better.

Both the binary and cereal packages are very featureful, and with these features come some overhead. We actually have much simpler requirements for our standard serialization cases, e.g.:

With these ideas in mind, I’ve spent the past few days putting together a variety of different implementations of both encoding and decoding, and believe that for the limited cases I just described, we can get significantly better serialization performance.

Vector SomeData

The benchmarks I’ve used for this experiment focus on a fairly simple encoding and decoding test for a boxed vector of SomeData values. The SomeData type has a single data constructor with strict fields of Int64, Word8, and Double. This is a fairly good representation of the kind of data that we deal with regularly. Serializing more complex datatypes, or more variable-length types like a ByteString, are certainly supported by all of the schemes I’ve described here, but the performance impact of that has not been tested (since it’s not the main focus of our work).


There are three different binary representations of the data used in this benchmark suite:

Let’s start with the benchmark results, and then do some more analysis:

Benchmark results

Unsurprisingly, binary performed the worse by far, mainly due to its Double serialization. Similarly, the big endian approaches were all slower than the little endian approaches, though the cereal package performed significantly worse than the raw bytestring-builder encoding and direct ByteString/bit-shifting decoding.

Since the little endian encoding wins as far as representation, we’ll spend the rest of this approach analyzing the different trade-offs in encoding and decoding, based on the 6 encoding and 3 decoding functions implemented and tested here.

Continuation vs mutable reference

When both encoding and decoding, we need to keep track of the current offset being written to/read from within the buffer. In the case of decoding, we also need to have some way to fail out if the data parsed is not what we expect, or there are insufficient bytes to consume. I tested two approaches for this:

As you can see from the results, the continuation based approach gave a slight performance advantage. While this is what I would have expected, the difference between the two was less than I had expected. Additionally, in some earlier testing before I’d added more optimization, the reference based implementation actually outperformed the continuation based implementation. The details of what’s going on at the core level would be an interesting follow-up analysis.

Optimizing the mutable reference

If you dive into the code, you may be a bit surprised at how the offset reference is represented. Instead of a more standard IORef, we have:

newtype OffsetRef = OffsetRef
    (Data.Vector.Unboxed.Mutable.MVector RealWorld Offset)

The idea here is to take advantage of the more efficient unboxed representation and byte array operations provided by the vector package and GHC. This also avoids a lot of garbage collection overhead used by the boxed nature of IORef. This is the same technique leveraged by the mutable-containers package. Also, check out the Stack Overflow question on the topic.

Storable, bit-shifting, and bytestring-builder

When it comes to storing primitive datatypes in a pointer, it’s unsurprising to see the Storable type class. This class’s peek and poke operations are implemented under the surface with GHC primops. We get away with this because, in our implementation, we are always using host byte order. By contrast, the cereal, binary, and bytestring-builder packages need to do more direct bit-shifting, such as:

word64be :: ByteString -> Word64
word64be = s ->
              (fromIntegral (s `SU.unsafeIndex` 0) `shiftL` 56) .|.
              (fromIntegral (s `SU.unsafeIndex` 1) `shiftL` 48) .|.
              (fromIntegral (s `SU.unsafeIndex` 2) `shiftL` 40) .|.
              (fromIntegral (s `SU.unsafeIndex` 3) `shiftL` 32) .|.
              (fromIntegral (s `SU.unsafeIndex` 4) `shiftL` 24) .|.
              (fromIntegral (s `SU.unsafeIndex` 5) `shiftL` 16) .|.
              (fromIntegral (s `SU.unsafeIndex` 6) `shiftL`  8) .|.
              (fromIntegral (s `SU.unsafeIndex` 7) )

Leveraging Storable whenever possible simplifies our codebase and makes it more efficient. In fact, the Simple typeclass – used for most of our implementations here – provides default implementations of all functions in terms of Storable, so that the instances for primitive types just look like:

instance Simple Int64
instance Simple Word8
instance Simple Double

simpleSize :: Either Int (a -> Int)

You may be wondering: if Storable is so great, why not just base a serialization library on it directly? There’s one downside: it assumes that every datatype has a fixed serialized width. While this works great for Ints and the like, it fails with a Vector, where we need to know – at runtime – the actual length of the Vector to determine its serialized size.

A naive implementation of this would look like:

simpleSize :: a -> Int

However, this signature implies that every type’s size may change at runtime. This would require that, when serializing a Vector, we always inspect every single value, which introduces significant CPU overhead. For example, given that the representation of a Vector is an 8-byte integer length plus the sizes of all elements, this would look like:

simpleSize :: Vector a -> Int
simpleSize v = 8 + V.sum (V.map simpleSize v)

But in the case of statically-known sizes, we’d like to replace that sum with a simple multiplication. To make this work, we have a slightly smarter type signature for the size function:

simpleSize :: Either Int (a -> Int)

If this function returns Left, there’s no need to inspect individual values. For Right, we’re stuck with checking each thing. Putting this together, here’s our implementation for Vectors.

simpleSize :: Either Int (Vector a -> Int)
simpleSize = Right $ v ->
    case simpleSize of
        Left s -> s * V.length v + 8
        Right f -> V.sum (V.map f v) + 8

Notice how, for a Vector Int64, we’d be able to follow the Left branch and avoid the costly sum. This ends up giving us really cheap knowledge of the total buffer size needed, which we take advantage of when encoding, e.g.:

encodeSimpleRaw :: Simple a => a -> ByteString
encodeSimpleRaw x = unsafeCreate
    (either id ($ x) simpleSize)
    (p -> simpleRawPoke p 0 x)

Monadic vs Monoidal interface

Ever since blaze-html famously provided a broken Monad interface in the name of performance, there’s been a concern (at least by me) that providing a proper Monad instance instead of just a Monoid instance could slow things down. Therefore, this benchmark included encoding functions that are both monadic (encodeSimplePokeMonad and encodeSimplePokeRefMonad) and monoidal (encodeSimplePoke and encodeSimplePokeRef). To my surprise, they performed nearly identically.

The monadic interface though has many advantages over a monoidal one:

Overall abstraction overhead

In addition to the nice newtype-wrapped monads and monoids, I implemented a raw version of both encoding and decoding, just to see if the abstraction added an overhead. Fortunately, this was not the case, which lets us stick with relatively simple high-level code such as:

simplePeek = SomeData
    <$> simplePeek
    <*> simplePeek
    <*> simplePeek

instead of:

readInt64  bs  $ bsX x ->
readWord8  bsX $ bsY y ->
readDouble bsY $ bsZ z -> do
    MV.unsafeWrite mv idx (SomeData x y z)
    loop (idx + 1) bsZ

ST-like monad

If you look at the implementation of the Binary instance for Vector, you’ll see that it needs to resort to unsafePerformIO. This is because, in order to efficiently create a Vector, it needs to perform mutations, but the Get monad from binary does not provide any ST or IO like behavior for mutating a buffer.

Since we’re designing a new decoding interface from scratch, and have Vector as a first-class use case, I decided to bake in ST-like semantics to allow directly playing around with mutable vectors. As a result, the type of Peek has an extra state token parameter, just like ST:

newtype Peek s a

Also, there is a PrimMonad instance for Peek, which allows for the mutable vector operations to occur within the monad without any lifting. To see how this works, take a look at the implementation of simplePeek for Vectors:

simplePeek :: Simple a => Peek (Vector a)
simplePeek = do
    len :: Int64 <- simplePeek
    let len' = fromIntegral len
    mv <- MV.new len'
    let loop i
            | i >= len' = V.unsafeFreeze mv
            | otherwise = do
                x <- simplePeek
                MV.unsafeWrite mv i x
                loop $! i + 1
    loop 0

All of this also applies to the PeekEx type.


The criterion link I shared above has the results of all of these benchmarks, but for the lazy, here’s the result graph again:

Benchmark results

As you can see: the little-endian encoding regularly outperformed the other two formats by a significant margin. This isn’t surprising, given that there is less CPU work that needs to be done, and that we are able to leverage primops from GHC to do most of the work.

Similarly, while bytestring-builder is a very highly tuned library, for a narrow use case like ours where the resulting buffer size is easily computed in advance, constructing ByteStrings manually gives a significant performance boost.

To my surprise, the mutable reference/exception based approach to peeking and poking was fairly competitive with the continuation-based approach. Nonetheless, overall the continuation-based approach outperforms it, and is more elegant an approach.

While the monadic and monoidal interfaces for poking/encoding give similar results, the performance of the monoidal interface can be unpredictable if used incorrectly. Since monadic combinators are more commonly optimized and more ubiquitous, it’s safer to expose the monadic interface. (And, for what it’s worth, we can always provide a Monoid instance for any Monad.)

And thankfully, the abstractions we use to make our encoding and decoding easy to work with and nicely composable do not introduce a slowdown versus the low-level implementation. So no need to force people to drop down to manual continuation passing.

What’s next

Based on this analysis, it seems like we have a clear set of choices for a new serialization library. This blog post already clarifies the design goals of such a library, and its limitations (e.g., no streaming decoding). Such a library is something we’ll almost certainly want to leverage for our multi-machine computation work at FP Complete.

I’m interested on feedback from others on this topic, including ways to better optimize the code, alternative strategies, and additional use cases you might have for such a library.

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