This document discusses parallel and asynchronous programming using the Task Parallel Library (TPL) in .NET. It covers how processors are getting smaller so parallelism is important. It provides examples of using TPL for data parallelism by partitioning work over collections and task parallelism by breaking work into steps. It also discusses asynchronous programming with async/await and how TPL handles cancellation, progress reporting, and synchronization contexts.

2. Parallel and Asynchronous
Programming
Or how we built a Dropbox clone without a
PhD in Astrophysics
Panagiotis Kanavos
DotNetZone Moderator
pkanavos@gmail.com

3. Why
• Processors are getting smaller
• Networks are getting worse
• Operating Systems demand it
• Only a subset of the code can run in parallel

4. Processors are getting smaller
• Once, a single-thread process could use 100%
of the CPU
• 16% ΜΑΧ ona Quad core LAPTOP with
HyperThreading
• 8% ΜΑΧ on an 8 core server

5. What we used to have
• Hand-coded threads and synchronization
• BackgroundWorker
 Heavy, cumbersome, single threaded, inadequate progress reporting
• EAP: From event to event
 Complicated, loss of continuity
• APM: BeginXXX/EndXXX
 Cumbersome, imagine socket programming with Begin/End!
or rather ...

6. Why I stopped blogging
• Asynchronous Pipes with APM

7. The problem with threads
• Collisions
 Reduced throughput
 Deadlocks
• Solution: Limit the number of threads
 ThreadPools
 Extreme: Stackless Python
 Copy data instead of shared access
 Extreme: Immutable programming

8. Why should I care about
threads?
• How can I speed-up my algorithm?
• Which parts can run in parallel?
• How can I partition my data?

9. Example
Revani

10. Synchronous Revani
• Beat the yolks with 2/3 of sugar until fluffy
• Beat the whites with 1/3 of sugar to stiff meringue
• and add half the mixture to the yolk mixture.
• Mix semolina with flour and ground coconut ,
• add rest of meringue and mix
• Mix and pour in cake pan
• Bake in pre-heated oven at 170οC for 20-25 mins.
• Allow to cool
• Prepare syrup, boil water, sugar, lemon for 3 mins.
• Pour warm syrup over revani
• Sprinkle with ground coconut.

11. Parallel Revani
• Beat yolks • Beat Whites
• Add half mixture
• Mix semolina
• Add rest of meringue
• Mix
• Pour in cake pan
• Bake • Prepare syrup
• Pour syrup
• Sprinkle

12. What we have now
• Support for multiple concurrency scenarios
• Overall improvements in threading
• Highly Concurrent collections

13. Scenaria
• Faster processing of large data
• Number crunching
• Execute long operations
• Serve high volume of requests
• Social Sites, Web sites, Billing, Log aggregators
• Tasks with frequent blocking
• REST clients, IT management apps

14. Scenario Classification
• Data Parallelism
• Task Parallelism
• Asynchronous programming
• Agents/Actors
• Dataflows

15. Data Parallelism – Recipe
• Partition the data
• Implement the algorithm in a function
• TPL creates the necessary tasks
• The tasks are assigned to threads
• I DON’T’T have to define the number of
Tasks/Threads!

16. Data Parallelism - Tools
• Parallel.For / Parallel.ForEach
• PLINQ
• Partitioners

17. Parallel class Methods
• Parallel execution of lambdas
• Blocking calls!
• We specify
 Cancellation Token
 Maximum number of Threads
 Task Scheduler

18. PLINQ
• LINQ Queries
• Potentially multiple threads
• Parallel operators
• Unordered results
• Beware of races
List<int> list = new List<int>();
var q = src.AsParallel()
.Select(x => { list.Add(x); return x; })
.Where(x => true) .Take(100);

19. What it can’t do
• Doesn’t use SSE instructions
• Doesn’t use the GPU
• Isn’t using the CPU at 100%

21. Scenaria
• Data Parallelism
• Task Parallelism
• Asynchronous programming
• Agents/Actors
• Dataflows

22. Task Parellelism – Recipe
• Break the problem into steps
• Convert each step to a function
• Combine steps with Continuations
• TPL assigns tasks to threads as needed
• I DON’T have to define number of
Tasks/Threads!
• Cancellation of the entire task chain

23. The Improvements
• Tasks wherever code blocks
• Cancellation
• Lazy Initialization
• Progress Reporting
• Synchronization Contexts

24. Cancellation
• Problem: How do you cancel multiple tasks
without leaving trash behind?
• Solution: Everyone monitors a
CancellationToken
 TPL cancels subsequent Tasks or Parallel operations
 Created by a CancellationTokenSource
 Can execute code when Cancel is called

25. Progress Reporting
• Problem: How do you update the UI from inside
a task?
• Solution: Using an IProgress<T> object
 Out-of-the-Box Progress<T> updates the current Synch Context
 Any type can be a message
 Replace with our own implementation

26. Lazy Initialization
• Calculate a value only when needed
• Lazy<T>(Func<T> …)
• Synchronous or Asynchronous calculation
 Lazy.Value
 Lazy.GetValueAsync<T>()

27. Synchronization Context
• Since .NET 2.0!
• Hides Winforms, WPF, ASP.NET
 SynchronizationContext.Post/Send instead of Dispatcher.Invoke etc
 Synchronous and Asynchronous version
• Automatically created by the environment
 SynchronizationContext.Current
• Can create our own
 E.g. For a Command Line aplication

28. Scenaria
• Data Parallelism
• Task Parallelism
• Asynchronous programming
• Agents/Actors
• Dataflows

29. Async/Await
• Support at the language leve
• Debugging support
• Exception Handling
• After await return to original “thread”
 Beware of servers and libraries
• Dos NOT always execute asynchronously
 Only when a task is encountered or the thread yields
 Task.Yield

30. Asynchronous Retry
private static async Task<T>
Retry<T>(Func<T> func, int retryCount) {
while (true) {
try {
var result = await Task.Run(func);
return result;
}
catch {
If (retryCount == 0)
throw;
retryCount--;
} } }

32. More Goodies - Collections
• Highly concurrent
• Thread-safe
• Not only for TPL/PLINQ
• Producer/Consumer scenaria

33. Concurrent Collections - 2
• ConcurrentQueue
• ConcurrentStack
• ConcurrentDictionary

34. The Odd one - ConcurrentBag
• Duplicates allowed
• List per Thread
• Reduced collisions for each tread’s Add/Take
• BAD for Producer/Consumer

35. Concurrent Collections -
Gotchas
• NOT faster than plain collections in low
concurrency scenarios
• DO NOT consume less memory
• DO NOT provide thread safe enumeration
• DO NOT ensure atomic operations on content
• DO NOT fix unsafe code

36. Also in .NET 4
• Visual Studio 2012
• Async Targeting package
• System.Net.HttpClient package

37. Other Technologies
• F# async
• C++ Parallel Patterns Library
• C++ Concurrency Runtime
• C++ Agents
• C++ AMP

38. • Object storage similar to Amazon S3/Azure Blob
storage
• A Service of Synnefo – IaaS by GRNet
• Written in Python
• Clients for Web, Windows, iOS, Android, Linux
• Versioning, Permissions, Sharing

39. Synnefo

40. Pithos API
• REST API base on CloudFiles by Rackspace
 Compatible with CyberDuck etc
• Block storage
• Uploads only using blocks
• Uses Merkle Hashing

41. Pithos Client for Windows
• Multiple accounts per machine
• Synchronize local folder to a Pithos account
• Detect local changes and upload
• Detect server changes and download
• Calculate Merkle Hash for each file

42. The Architecture
UI Core Networking Storage
File Agent
WPF
CloudFiles SQLite
Poll Agent
MVVM
Network
Agent
SQL Server
Caliburn HttpClient
Compact
Micro Status Agent

43. Technologies
• .ΝΕΤ 4, due to Windows XP compatibility
• Visual Studio 2012 + Async Targeting Pack
• UI - Caliburn.Micro
• Concurrency - TPL, Parallel, Dataflow
• Network – HttpClient
• Hashing - OpenSSL – Faster than native provider for hashing
• Storage - NHibernate, SQLite/SQL Server Compact
• Logging - log4net

44. The challenges
• Handle potentially hundrends of file events
• Hashing of many/large files
• Multiple slow calls to the server
• Unreliable network
• And yet it shouldn’t hang
• Update the UI with enough information

45. Events Handling
• Use producer/consumer pattern
• Store events in ConcurrentQueue
• Process ONLY after idle timeout

46. Merkle Hashing
• Why I hate Game of Thrones
• Asynchronous reading of blocks
• Parallel Hashing of each block
• Use of OpenSSL for its SSE support
• Concurrency Throttling
• Beware of memory consumption!

47. Multiple slow calls
• Each call a task
• Concurrent REST calls per account and share
• Task.WhenAll to process results

48. Unreliable network
• Use System.Net.Http.HttpClient
• Store blocks in a cache folder
• Check and reuse orphans
• Asynchronous Retry of calls

49. Resilience to crashes
• Use Transactional NTFS if available
 Thanks MS for killing it!
• Update a copy and File.Replace otherwise

50. Should not hang
• Use of independent agents
• Asynchronous operations wherever possible

51. Provide Sufficient user feedback
• Use WPF, MVVM
• Use Progress to update the UI

52. Next Steps
• Create Windows 8 Dekstop and WinRT client
• Use Reactive Framework
ΖΗΤΟΥΝΤΑΘ ΕΘΕΛΟΝΤΕΣ

54. Clever Tricks
• Avoid Side Effects
• Use Functional Style
• Clean Coding
• THE BIG SECRET:
 Use existing, tested algorithms
• IEEE, ACM Journals and libraries

55. YES TPL
• Simplify asynchronous or parallel code
• Use out-of-the-box libraries
• Scenarios that SUIT Task or Data Parallelism

56. NO TPL
• To accelerate “bad” algorithms
• To “accelerate” database access
 Use proper SQL and Indexes!
 Avoid Cursors
• Reporting DBs, Data Warehouse, OLAP Cubes

57. When TPL is not enough
• Functional languages like F#, Scala
• Distributed Frameworks like Hadoop, {m}brace

58. Books
• C# 5 in a Nutshell, O’Riley
• Parallel Programming with .NET, Microsoft
• Pro Parallel Programming with C#, Wiley
• Concurrent Programming on Windows, Pearson
• The Art of Concurrency, O’Reilly

59. Useful Links
• Parallel FX Team:
http://blogs.msdn.com/b/pfxteam/
• ΙΕΕΕ Computer Society
http://www.computer.org
• ACM http://www.acm.org