This document discusses parallel and asynchronous programming. It begins by explaining how processors are getting smaller while networks are getting worse, requiring more efficient parallel programming approaches. It then covers different parallel programming models in .NET like data parallelism using PLINQ, task parallelism using TPL, asynchronous programming with async/await, and concurrent collections. It also discusses challenges like cancellation, progress reporting, and synchronization, and how modern .NET addresses these.

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

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

4. • 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
Processors are getting smaller

5. • 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 ...
What we used to have

6. • Asynchronous Pipes with APM
Why I stopped blogging

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

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

9. Example
Revani

10. • 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.
Synchronous Revani

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

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

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

15. • 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!
Data Parallelism – Recipe

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

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

18. • 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);
PLINQ

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

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

22. • 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
Task Parellelism – Recipe

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

24. • 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
Cancellation

25. • 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
Progress Reporting

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

27. • 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
Synchronization Context

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

29. • 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
Async/Await

30. 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--;
} } }
Asynchronous Retry

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

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

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

35. • 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
Concurrent Collections -
Gotchas

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

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

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

41. • 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
Pithos Client for Windows

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

43. • .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
Technologies

44. • 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
The challenges

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

46. • 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!
Merkle Hashing

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

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

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

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

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

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

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

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

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

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

58. • 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
Books

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