FUTURE OF.NET PARALLEL PROGRAMMING Joseph Albahari SESSION CODE: DEV308 (c) 2011 Microsoft. All...

(c) 2011 Microsoft. All rights reserved.

FUTURE OF .NET PARALLEL PROGRAMMING

Joseph Albahari

SESSION CODE: DEV308

(c) 2011 Microsoft. All rights reserved.

JOE ALBAHARIwww.albahari.com

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Agenda

► Parallel Programming in Framework 4.0 recap

► TPL Dataflow in vNext

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CPU Clock Speeds over Past 25 Years

(logarithmic)

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Intel’s 80-core prototype

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Parallel APIs in Framework 4.0

Tasks

Parallel Loops

PLINQ

Concurrent Collections

CLR Thread Pool

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Fork/Join

Fork Join

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Parallel Programming APIs

API Fork JoinPLINQ

Parallel Loops

Task Parallelism

Functional

Imperative

Imperative

Task Parallel Library (TPL)

Task vs. Data Parallelism

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Tasks

Data

T1 T2 T3 T4

T1 T2 T3 T4

Parallel Loops

PLINQ

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PLINQ

► Highest-level parallel API► Data parallelism► Declarative► Transparently parallelizes LINQ queries

string[] names = { "Tom", "Dick", "Harry", "Mary", "Jay" };

var query =    from n in names    where n.Contains ("a")     orderby n.Length    select n.ToUpper();

string[] names = { "Tom", "Dick", "Harry", "Mary", "Jay" };

var query = names    .Where (n => n.Contains ("a"))    .OrderBy (n => n.Length)    .Select (n => n.ToUpper());

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PLINQ – gotchas

► Query needs ‘meat’ ► Original ordering lost (by default)► Functional impurity can break queries► No work-stealing with range partitioning

Math.Sqrt (numbers.Average (n => n * n))

Root-mean-square

double mean = numbers.Average();double sdev = Math.Sqrt (numbers.Average (n =>              {                 double dif = n - mean;                 return dif * dif;              }));

Standard deviation

var numbers = new[] { 2, 3, 4, 5, 6 } .AsParallel();

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PLINQ – Tricks & Tips

► Can do I/O bound queries– but there are better ways

► .ForAll() defeats data collation► Aggregations are parallelizable

albahari.com/threading

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Parallel Loops

Tasks

Parallel Loops

PLINQ

Concurrent Collections

CLR Thread Pool

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Parallel Loops

Parallel.ForParallel.ForEach

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Parallel.For (1, 1000, i => Foo (i));

for (int i = 1; i < 1000; i++) Foo (i);

Sequential

Parallel

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var toTest = new TypeToTest();

var methodsToTest = from m in toTest.GetType().GetMethods() where m.GetCustomAttributes (false)

.OfType<TestAttribute>().Any() select m;

Parallel.ForEach (methodsToTest, m =>{ try { m.Invoke (instanceToTest, null)); } catch (Exception ex) { ... }}

Parallel Unit Testing

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Tricks & Tips

► Best with moderately sized work items– 1µs to 100ms

► Avoid for I/O bound work

► Parallel.For = range partitioning + work stealing

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Task Parallelism

Tasks

Parallel Loops

PLINQ

Concurrent Collections

CLR Thread Pool

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Task Parallelism

►Parallel.Invoke(Action[] actions)

►Task / Task<T>

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Task and Task<TResult>

A task represents aconcurrent operation

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Tasks have two purposes

1. For multithreading– Including

Parallel programming– Gets you into the thread pool

2. A value-added signaling construct– I/O bound– Asynchronous

Pattern

Task

TaskScheduler

TaskCompletionSource

Task.Factory.StartNew()

new TaskCompletionSource<>()

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Tasks: Tricks and Tips

► Be careful with continuations– Consider async CTP if you need them– Conditional continuations are particularly nasty

► Exception-handle ‘set-and-forget’ tasks

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MANDELBROT DEMO

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TPL Dataflow CTP

Tasks

Parallel LoopsPLINQ

Concurrent Collections

CLR Thread Pool

Dataflow

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TPL Dataflow

► High throughput, low-latency scenarios► Typical applications:– Manufacturing– Imaging– Biology– Oil & Gas– Finance– Robotics

TPL Dataflow

TPL Dataflow

AAL (C++)

CCR(MS Robotics) Axum

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Dataflow Blocks

Buffering Execution Joining

BufferBlock

BroadcastBlock

WriteOnceBlock

ActionBlock

TransformBlock

TransformManyBlock

BatchBlock

JoinBlock

BatchedJoinBlock

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ActionBlock<TInput>

Action<TInput> delegate

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Action<TInput> delegate

ActionBlock<TInput>

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Multiple Blocks

Action<TInput>

Action<TInput>

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Action<TInput>

MaxDegreeOfParallelism

MaxDegreeOfParallelism = 2

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TransformBlock<TInput, TOutput>

Func<TInput,TOutput>

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TransformManyBlock<TInput, TOutput>

Func<TInput,TOutput>

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TransformBlock + ActionBlock

Func<,> Action<>

Action<>

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Mandelbrot Pipeline

Func<Rect,Frame>

Func<Frame,Stream>

Action<Stream>

Render

Encode

Save

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Dataflow Blocks

Buffering Execution Joining

BufferBlock

BroadcastBlock

WriteOnceBlock

ActionBlock

TransformBlock

TransformManyBlock

BatchBlock

JoinBlock

BatchedJoinBlock

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BatchBlock<>

JoinBlock<>

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Joining Blocks: Greedy by default

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Non-greedy

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DataFlow Blocks

Buffering Execution Joining

BufferBlock

BroadcastBlock

WriteOnceBlock

ActionBlock

TransformBlock

TransformManyBlock

BatchBlock

JoinBlock

BatchedJoinBlock

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BufferBlock<>

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BroadcastBlock<>

“overwrite buffer”

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WriteOnceBlock<>

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Dataflow Blocks

Buffering Execution Joining

BufferBlock

BroadcastBlock

WriteOnceBlock

ActionBlock

TransformBlock

TransformManyBlock

BatchBlock

JoinBlock

BatchedJoinBlock

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Extensibility & Interoperability

► Custom data blocks

► Rx:– AsObservable()– AsObserver()

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