Timothy Ng is the F# Lead at Microsoft Corporation. The document summarizes F#'s approach to parallelism and concurrency through tools like Visual F#, libraries like Parallel LINQ and Rx, and language features in F# like immutability and asynchronous workflows. It discusses challenges of shared state, code locality, I/O parallelism, and scaling to multiple machines that F# addresses through techniques like immutability, asynchronous workflows using async {...}, and agent-based programming. The summary concludes that F# with .NET 4.0 makes parallelism and asynchrony simple, powerful, and productive for both current and future use.

1. Timothy Ng
F# Lead
Microsoft Corporation

3. Tools Managed Languages
Visual Studio Visual F# Axum
2010
Parallel
Managed Libraries Native Libraries
Debugger
Windows
DryadLINQ Parallel Async
Profiler
Pattern Agents
Concurrency Parallel LINQ Rx
Data Structures
Data Structures
Analysis Library Library
Microsoft
Task Parallel Library
Native Concurrency Runtime
Research
Race Task Scheduler
Detection Managed Concurrency Runtime
Fuzzing ThreadPool Resource Manager
Operating
HPC Server Threads UMS Threads System
Operating Key: Research / Incubation Visual Studio 2010 / .NET 4 Windows 7 / Server 2008 R2
System

4. Shared State
Code Locality
I/O Parallelism
Scaling to Multi-Machine

5. Shared State
o Difficult to maintain and test
o Very difficult to parallelize!
o Locking is fundamentally error prone:
o Must guess where parallelism will be needed
o All consumers need to participate
Code Locality
I/O Parallelism
Scaling to Multi-Machine

7.  Immutable Lists  Immutable Tuples
 Immutable Records  Immutable Dictionaries
 Immutable Sets  Immutable Unions
 Immutable Objects  Lots of language features
to encourage immutability

8. Shared State
Code Locality
o We’re used to expressing algorithms linearly
o Async requires logical division of algorithms
o Very difficult to
o Combine multiple asynchronous operations
o Deal with exceptions and cancellation
I/O Parallelism
Scaling to Multi-Machine

10. Shared State
Code Locality
I/O Parallelism
o Software is often I/O-bound
o Leveraging web services
o Working with data on disk
o Network and disk speeds increasing slower
o I/O resources are inherently parallel
o Huge opportunity for performance
Scaling to Multi-Machine

12. Shared State
Code Locality
I/O Parallelism
Scaling to Multi-Machine
o To scale up, must to go beyond a single machine
o Multi-machine resources becoming common
o Roll-you-own clusters with cheap hardware
o On-demand cloud compute with Azure
o But
o Shared memory doesn’t scale

13. Shared State immutability
Code Locality async { … }
I/O Parallelism async { … }
Scaling to Multi-Machine agents

14. Simple, powerful, Ready for production
and productive use with VS2010
F#
Parallelism and
F# + .NET 4.0 radically
asynchrony – for today
simplify parallelism
and tomorrow

15. http://fsharp.net

16. © 2007 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only.
MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY.