Parallel and Asynchronous Programming - ITProDevConnections 2012 (Greek)
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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.
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Parallel and Asynchronous Programming - ITProDevConnections 2012 (Greek)
- 2. Parallel and Asynchronous Programming Or how we built a Dropbox clone without a PhD in Astrophysics Panagiotis Kanavos DotNetZone Moderator [email protected]
- 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