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

SESSION CODE: DEV308
Joseph Albahari
FUTURE OF .NET PARALLEL
PROGRAMMING
(c) 2011 Microsoft. All rights reserved.
JOE ALBAHARI
www.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
PLINQ
Parallel
Loops
Tasks
CLR Thread Pool
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Concurrent
Collections
Fork/Join
Join
Fork
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Parallel Programming APIs
API
PLINQ
Parallel Loops
Fork Join
Functional
Imperative
Task Parallelism
Imperative
Task Parallel Library (TPL)
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Task vs. Data Parallelism
PLINQ
T1
T2
T3
T4
T1
T2
T3
T4
Parallel
Loops
Data
Tasks
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PLINQ
► Highest-level parallel API
► Data parallelism
► Declarative
► Transparently parallelizes LINQ queries
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string[] names = { "Tom", "Dick", "Harry", "Mary", "Jay" };
var query =
from
where
orderby
select
n in names
n.Contains ("a")
n.Length
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());
PLINQ – gotchas
► Query needs ‘meat’
► Original ordering lost (by default)
► Functional impurity can break queries
► No work-stealing with range partitioning
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var numbers = new[] { 2, 3, 4, 5, 6 }
.AsParallel();
Root-mean-square
Math.Sqrt (numbers.Average (n => n * n))
Standard deviation
double mean = numbers.Average();
double sdev = Math.Sqrt (numbers.Average (n =>
{
double dif = n - mean;
return dif * dif;
}));
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
PLINQ
Parallel
Loops
Tasks
CLR Thread Pool
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Concurrent
Collections
Parallel Loops
Parallel.For
Parallel.ForEach
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Sequential
for (int i = 1; i < 1000; i++)
Foo (i);
Parallel
Parallel.For (1, 1000, i =>
Foo (i)
);
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Parallel Unit Testing
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) { ... }
}
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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
PLINQ
Parallel
Loops
Tasks
CLR Thread Pool
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Concurrent
Collections
Task Parallelism
►Parallel.Invoke(Action[] actions)
►Task / Task<T>
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Task and Task<TResult>
A task represents a
concurrent operation
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Tasks have two purposes
1. For multithreading
–
–
Task.Factory.StartNew()
TaskScheduler
Including
Parallel programming
Gets you into the thread pool
Task
2. A value-added signaling construct
–
–
I/O bound
Asynchronous
Pattern
TaskCompletionSource
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
PLINQ
Parallel
Loops
Dataflow
Tasks
CLR Thread Pool
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Concurrent
Collections
TPL Dataflow
► High throughput, low-latency scenarios
► Typical applications:
–
–
–
–
–
–
Manufacturing
Imaging
Biology
Oil & Gas
Finance
Robotics
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TPL Dataflow
AAL
(C++)
CCR
(MS Robotics)
TPL
Dataflow
Axum
Dataflow Blocks
Buffering
Execution
Joining
BufferBlock
ActionBlock
BatchBlock
BroadcastBlock
TransformBlock
JoinBlock
WriteOnceBlock TransformManyBlock BatchedJoinBlock
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ActionBlock<TInput>
Action<TInput> delegate
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ActionBlock<TInput>
Action<TInput> delegate
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Multiple Blocks
Action<TInput>
Action<TInput>
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MaxDegreeOfParallelism
Action<TInput>
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
Action<>
Func<,>
Action<>
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Mandelbrot Pipeline
Func<Rect,Frame>
Render
Func<Frame,Stream>
Encode
Action<Stream>
Save
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Dataflow Blocks
Buffering
Execution
Joining
BufferBlock
ActionBlock
BatchBlock
BroadcastBlock
TransformBlock
JoinBlock
WriteOnceBlock TransformManyBlock BatchedJoinBlock
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BatchBlock<>
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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
ActionBlock
BatchBlock
BroadcastBlock
TransformBlock
JoinBlock
WriteOnceBlock TransformManyBlock 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
ActionBlock
BatchBlock
BroadcastBlock
TransformBlock
JoinBlock
WriteOnceBlock TransformManyBlock BatchedJoinBlock
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Extensibility & Interoperability
► Custom data blocks
► Rx:
– AsObservable()
– AsObserver()
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