Have you ever had to endure the pain of manually creating threads? This talk introduces enhancements in Visual Studio and .NET that make developing high-throughput, asynchronous, and low-latency applications attainable.

1. Parallel Computing in .NET
James Rapp

2. TOPICS
• Task Parallel Library
• Parallel Debugging
• Parallel Profiling

3. TYPES OF PARALLELISM
Data Parallelism
Occurs when you carry out the same operation on multiple subsets of the
data simultaneously and independently.
Examples:
• Matrix multiplication
• Jacobi Relaxation
• Ray Tracing

4. TYPES OF PARALLELISM
Task Parallelism
One or more independent tasks running concurrently
Examples:
• Sorting
• Dataflow Networks
• Asynchrony

5. WHY IS PARALLELISM RELEVANT?
• Moore’s law: Transistor count doubles every two years
• We’ve reached the physical limits of clock speed
• Solution: Scale horizontally, not vertically
• The free lunch is over: We now must right parallel code if we want to
benefit from better hardware

6. TASK PARALLEL LIBRARY
Implementing Thread-Based Parallelism
• Tedious and error-prone
• Difficult to read
• Threads are heavyweight
• Wrong abstraction level
TPL
• Simplifies parallel programming
• Raises the level of abstraction
• Encapsulates common patterns
• Enables fine-grained control

7. Tasks (System.Threading.Tasks)
Task – A lightweight schedulable unit of work.
• Represents an asynchronous operation
• Higher level of abstraction than a ThreadPool work item
Purpose of Tasks
• Simplifies low-level details such as cancellation or exception handling.
• More control – rich APIs for continuations, custom scheduling, etc.

8. .NET PARALLELISM OVERVIEW
Operating System
Threads
Concurrency Runtime
ThreadPool
Task Scheduler
Resource Manager
Developer Tools
Parallel Debugger
Concurrency
Visualizer
Programming Models
PLINQ
Task Parallel Library

9. A NOTE ON CHILD TASKS
Behavior Detached Attached
Parent waits for child to
complete
No Yes
Parent propagates exceptions
thrown by child
No Yes
Status of parent depends on
status of child
No Yes

10. CONCURRENT COLLECTIONS
(System.Collections.Concurrent)
Class Description
ConcurrentDictionary Collection of key/value pairs that can be accessed by safely by
multiple threads
ConcurrentQueue Thread-safe FIFO collection
ConcurrentStack Thread-safe LIFO collection
Partitioner Provides common partitioning strategies for arrays, lists, and
enumerables
ConcurrentBag Thread-safe, unordered collection of objects
BlockingCollection Provides blocking and bounding capabilities for thread-safe
collections that implement IProducerConsumerCollection<T>
Etc.
• Thread-safe collection classes – Optimized for performance
– Should be used instead of System.Collections and System.Collections.Generic

11. TPA DATAFLOW
(System.Threading.Tasks.Dataflow)
• Meant for course-grained dataflow or pipeline tasks
• Useful for processing data as it becomes available (e.g. red eye reduction
on a web cam)

12. .NET ASYNC
• Symplifies asynchronous programming
• Improves UI responsiveness and performance
async Task<int> AccessTheWebAsync()
{
HttpClient client = new HttpClient();
Task<string> getStringTask = client.GetStringAsync(“www.geneca.com");
//Work that doesn’t rely on getStringTask
DoIndependentWork();
string urlContents = await getStringTask;
return urlContents.Length;
}

13. NOT COVERED (Suggestions for
Further Research)
• Other Parallel Programming Models in .NET
– PLINQ
• Native Concurrency
– Parallel Patterns Library
• Data Parallelism on the GPU
– C++ AMP