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Seq vs Streams

November 17, 2014 23:22 by phil

Last week Gian Ntzik gave a great talk at the F#unctional Londoners meetup on the Nessos Streams library. It’s a lightweight F#/C# library for efficient functional-style pipelines on streams of data.

The main difference between LINQ/Seq and Streams is that LINQ is about composing external iterators (Enumerable/Enumerator) and Streams is based on the continuation-passing-style composition of internal iterators, which makes optimisations such as loop fusion easier.

The slides (using FsReveal) and samples are available on Gian’s github repository.

Simple Streams

Gian started the session by live coding a simple implementation of streams in about 20 minutes:

type Stream<'T> = ('T -> unit) -> unit

let inline ofArray (source: 'T[]) : Stream<'T> =
   fun k ->
      let mutable i = 0
      while i < source.Length do
            k source.[i]
            i <- i + 1          

let inline filter (predicate: 'T -> bool) (stream: Stream<'T>) : Stream<'T> =
   fun k -> stream (fun value -> if predicate value then k value)

let inline map (mapF: 'T -> 'U) (stream: Stream<'T>) : Stream<'U> =
   fun k -> stream (fun v -> k (mapF v))

let inline iter (iterF: 'T -> unit) (stream: Stream<'T>) : unit =
   stream (fun v -> iterF v)

let inline toArray (stream: Stream<'T>) : 'T [] =
   let acc = new List<'T>()
   stream |> iter (fun v -> acc.Add(v))
   acc.ToArray()

let inline fold (foldF:'State->'T->'State) (state:'State) (stream:Stream<'T>) =
   let acc = ref state
   stream (fun v -> acc := foldF !acc v)
   !acc

let inline reduce (reducer: ^T -> ^T -> ^T) (stream: Stream< ^T >) : ^T
      when ^T : (static member Zero : ^T) =
   fold (fun s v -> reducer s v) LanguagePrimitives.GenericZero stream

let inline sum (stream : Stream< ^T>) : ^T
      when ^T : (static member Zero : ^T)
      and ^T : (static member (+) : ^T * ^T -> ^T) =
   fold (+) LanguagePrimitives.GenericZero stream

and as you can see only about 40 lines of code.

Sequential Performance

Just with this simple implementation, Gian was able to demonstrate a significant performance improvement over F#’s built-in Seq module for a simple pipeline:

#time // Turns on timing in F# Interactive

let data = [|1L..1000000L|]

let seqValue = 
   data
   |> Seq.filter (fun x -> x%2L = 0L)
   |> Seq.map (fun x -> x * x)
   |> Seq.sum
// Real: 00:00:00.252, CPU: 00:00:00.234, GC gen0: 0, gen1: 0, gen2: 0

let streamValue =
   data
   |> Stream.ofArray
   |> Stream.filter (fun x -> x%2L = 0L)
   |> Stream.map (fun x -> x * x)
   |> Stream.sum
// Real: 00:00:00.119, CPU: 00:00:00.125, GC gen0: 0, gen1: 0, gen2: 0

Note for operations over arrays, the F# Array module would be more appropriate choice and is slightly faster:

let arrayValue =
   data
   |> Array.filter (fun x -> x%2L = 0L)
   |> Array.map (fun x -> x * x)
   |> Array.sum
// Real: 00:00:00.094, CPU: 00:00:00.093, GC gen0: 0, gen1: 0, gen2: 0

Also LINQ does quite well here as it has a specialized overloads including one for summing over int64 values:

open System.Linq

let linqValue =   
   data
      .Where(fun x -> x%2L = 0L)
      .Select(fun x -> x * x)
      .Sum()
// Real: 00:00:00.058, CPU: 00:00:00.062, GC gen0: 0, gen1: 0, gen2: 0

However with F# Interactive running in 64-bit mode Streams take back the advantage (thanks to Nick Palladinos for the tip):

let streamValue =
   data
   |> Stream.ofArray
   |> Stream.filter (fun x -> x%2L = 0L)
   |> Stream.map (fun x -> x * x)
   |> Stream.sum
// Real: 00:00:00.033, CPU: 00:00:00.031, GC gen0: 0, gen1: 0, gen2: 0

Looks like the 64-bit JIT is doing some black magic there.

Parallel Performance

Switching to the full Nessos Streams library, there’s support for parallel streams via the ParStream module:

let parsStreamValue =
   data
   |> ParStream.ofArray
   |> ParStream.filter (fun x -> x%2L = 0L)
   |> ParStream.map (fun x -> x + 1L)
   |> ParStream.sum
// Real: 00:00:00.069, CPU: 00:00:00.187, GC gen0: 0, gen1: 0, gen2: 0

which demonstrates a good performance increase with little effort.

For larger computes Nessos Streams supports cloud based parallel operations against Azure.

Overall Nessos Streams looks like a good alternative to the Seq module for functional pipelines.

Nessos LinqOptimzer

For further optimization Gian recommended the Nessos LinqOptimizer:

An automatic query optimizer-compiler for Sequential and Parallel LINQ. LinqOptimizer compiles declarative LINQ queries into fast loop-based imperative code. The compiled code has fewer virtual calls and heap allocations, better data locality and speedups of up to 15x

The benchmarks are impressive:

cs-ssq

Reactive Extensions (Rx)

One of the questions in the talk and on twitter later was, given Rx is also a push model, how does the performance compare:


Clearly the Nessos Streams library and Rx have different goals (data processing vs event processing), but I thought it would be interesting to compare them all the same:

open System.Reactive.Linq

let rxValue =
   data
      .ToObservable()
      .Where(fun x -> x%2L = 0L)
      .Select(fun x -> x * x)
      .Sum()      
      .ToEnumerable()
      |> Seq.head
// Real: 00:00:02.895, CPU: 00:00:02.843, GC gen0: 120, gen1: 0, gen2: 0

let streamValue =
   data
   |> Stream.ofArray
   |> Stream.filter (fun x -> x%2L = 0L)
   |> Stream.map (fun x -> x * x)
   |> Stream.sum
// Real: 00:00:00.130, CPU: 00:00:00.109, GC gen0: 0, gen1: 0, gen2: 0

In this naive comparison you can see Nessos Streams is roughly 20 times faster than Rx.

Observable module

F# also has a built-in Observable module for operations over IObservable<T> (support for operations over events was added to F# back in 2006). Based on the claims on Rx performance made by Matt Podwysocki I was curious to see how it stacked up:

let obsValue =
   data
   |> Observable.ofSeq
   |> Observable.filter (fun x -> x%2L = 0L)
   |> Observable.map (fun x -> x * x)
   |> Observable.sum
   |> Observable.first
// Real: 00:00:00.479, CPU: 00:00:00.468, GC gen0: 18, gen1: 0, gen2: 0

As you can see Observable module comes off roughly 5 times faster.

Note: I had to add some simple combinators to make this work, you can see the full snippet here: http://fssnip.net/ow

Summary

Nessos Streams look like a promising direction for performance of functional pipelines, and for gaining raw imperative performance the Nessos LINQOptimizer is impressive.


Tags:
Categories: F# | C# | .Net
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The Last Assignment

October 10, 2014 00:53 by phil

Back in November last year, my eldest son and I popped over to the Insomnia Gaming Festival in Telford to take part in a game jam organised by Global GameCraft. (Today I  bumped into the source again on a USB stick).

The theme for the day was “The Last Assignment”. We decided to go with a text based adventure game loosely based on the Dirty Harry movie.

With just 7 hours on the clock we managed to put together quite a fun adventure game with ambient sound and graphics:

The Last Assignment - Start Screen

and picked up the prize for best storyline!

The Last Assignment - Insomnia Telford

Given the time constraints I decided to build the dialogue as a simple state machine using coroutines. In this scenario C# was my go to language as it provides basic iterator block support and a first class goto statement.

By building the game dialogue as a simple state machine I was able test it from the start as a console app and later easily integrate it into a graphical environment.

Here’s the state machine for the rookie scene:

public static IEnumerable<State> Rookie()
{
   yield return new State(
         "One way or another this will be your last assignment.\r\n" +
         "Just 2 weeks left on the force before you retire.\r\n" +
         "Back at the police station",
         "You get a black coffee and a donut",
         "A chai latte and a cup cake") { Theme="70s reflective;bullpen"};
   if (Choice.Taken == 2) goto imposter;
   yield return new State(
         "Your new partner introduces himself.",
         "You give him a stern look",
         "Ignore him") { Theme = "70s reflective;bullpen" };
   yield return new State(
         "\"Why do they call ya 'Dirty Harry'?\"",
         "Make up your own mind kid",
         "Turn up your eye brow"
         ) { Theme = "70s reflective;bullpen" };
   yield break;
imposter:
   Game.Ended = true;
   yield return new State(
         "You have been exposed as an imposter.\r\n" +
         "Cops don't chai latte, keep it real!")
         { Theme = "end game mp3;bullpen" };
}

 

which looked like this:

Rookie Scene

If you fancy having a play, the source for the game as a console app is available here:

Have fun!


Tags:
Categories: Basic | C#
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Oops

October 7, 2014 14:27 by phil

Are you working in the enterprise?

Do you find yourself, day-in-day-out, up to the eyeballs in unmaintainable code?

Does the once beautiful architecture now more closely resemble a big ball of mud, that no amount of tooling will dig you out of?

What can you do?

1) Bury your head in the sand

head-in-the-sand

A very popular option, you just need to keep practising denial.

2) Turn to drink

gazza

Another popular option, although unfortunately this strategy is only likely to last as long as your liver.

3) Become a scrum master

scrum master

This is an easy way out, scrum certification is just a 2 day course away, but there’s probably no looking back.

4) Admit there’s a problem

AA

This is one of the hardest and least popular options, but possibly the most rewarding.

Start by saying out loud: “Object Oriented Programming is an expensive disaster which must end” and then take each new day as it comes.


Tags:
Categories: C# | C++ | Java
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