1. A .NET Language for Safe and Scalable Concurrency
VTL02
Axum
A .NET Language for Safe and Scalable Concurrency
Niklas Gustafsson
Software Architect
Parallel Computing Platform

2. The Parallel Glass Ceiling
Practices not sustainable
Complexity
Performance
Scalability
Switching to new models
Will require rethink: programmers + platform vendors
Will take time
Strong tool support needed to rein in costs

3. Shared Memory Sharing Basic model: Threads / tasks
Common address space
Exchange data in memory cells
Synchronize via CAS in memory cells
Take locks to protect data

4. Unpartitioned State A Huge Mess Performance
Unstated, indirect, subtle data dependencies
Documentation quickly out of sync with reality
Requires enormous discipline
Process scale
Performance
Overlapping working sets + false sharing
Cache coherence traffic will dominate
Technology scale

5. Shared Memory
Implicit component dependencies
0x0001a3f0:

6. Shared Memory
As core count grows…

7. Shared Memory
… we have to add components

8. Shared Memory
… and more…

9. Shared Memory
… and more…

10. Shared Memory
… and more…

11. Shared Memory
… until all we see is a tangled mess…

12. Shared Memory
… and how it’s going to hurt us…

13. Shared Memory
… unless, of course, we’re blissfully unaware

14. Shared Memory
However, the reality is likely this…

15. Shared Memory … so we document it…
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16. Shared Memory … but a bug is fixed…
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17. The Answer? We all know of a system that…
… runs on millions and millions of processors
… is programmed by hundreds of thousands of developers in thousands of organizations often competing with each other
… is known to scale
… is remarkably robust
… may have something to teach us

18. The Web Loosely coupled Low trust between “components”
Partitioned state
Message-passing (HTTP Get/Put)

19. Partitioned State
Firm boundaries, explicit dependencies

20. Message-Passing Data exchange Synchronization
Information passed via messages
Data is sent by:
Copy
Reference to immutable data (in-process)
Ownership transfer (in-process)
Synchronization
Data exchange ≡ synchronization

21. Asynchrony Data exchange needs to be asynchronous
Removes inter-component dependencies in time
Frees up components to do work in parallel
Requires new ways of designing programs
IAsyncResult
Continuation-passing, e.g. CCR
Promises
Data-flow networks
Events

22. Language Support New practices require new languages
Libraries good at enabling, not constraining users
Formalism of state partitioning
Asynchrony

23. Axum
Special-purpose language in incubation Partition data into domains Agents use shared state within domains Agents use message-passing between domains Support for asynchrony and data-flow

24. Axum Concepts
Agent
Domain
Channel
Schema

25. Channels Conduit for messages between two agents
Support for in-process and distributed messaging
Define ports as communication vocabulary
channel PingPong {
input bool Ping;
output Signal Pong; }
Input port ‘Ping,’ sending data from a client to an agent
Output port ‘Pong,’ sending data from an agent to its client

26. Agents
Active components, similar to threads but has limited access to shared mutable state
Send and receive messages via channels
agent PingAgent : channel PingPong {
public PingAgent ()
while (receive(Ping))
Pong <-- Signal.Value; }

27. Connecting to an Agent Connections are made in one of two ways:
var tble = provider.Connect<TableAccess>(“
var tble = Table.TableAgent.CreateInNewDomain();
A factory for channels
A channel type
An agent address
An agent type
A factory method for in-process agents

28. Ping Pong channel PingPong { input bool Ping; output Signal Pong; }
agent PingAgent : channel PingPong
public PingAgent()
while (receive(Ping))
Pong <-- Signal.Value;
agent MainAgent : channel Microsoft.Axum.Application { public MainAgent() var pp = PingAgent.CreateInNewDomain(); for (int i = 0; i < 100; i++) pp::Ping <-- true; receive(pp::Pong); } pp::Ping <-- false; Done <-- Signal.Value;

29. Domains Define state shared by several agent instances
Isolate agents in different domains from each other
Foundation for seamless distribution of agents
Agents are “hosted” within domains
domain Table {
Dictionary<string, string> dict = new Dictionary<string,string>();
public Table()
Host<TableAgent>(“TableAgentAddress"); }
public agent TableAgent : channel TableAccess ...

30. Domains + Agents = ♥ ♥ ♥ Agents declared as
Writer – allowed to modify state
Reader – allowed to read mutable state
N/A – no access to mutable state
Agent code is scheduled in parallel based on classification
Agents in different domains run in parallel
“One writer, many readers” within each domain
No-access agents run completely in parallel

31. Schema Payload definition Guaranteed to serialize
Efficient in-process copying
schema TableEntry {
required String Key;
required String Value;
rules { require !String.IsNullOrEmpty(Key);
require !String.IsNullOrEmpty(Value); } }

32. Protocols Define legal order of messages
Place constraints on payload values
channel TableAccess {
input KeyValuePair <String,String> Put;
input String Get : String;
input Signal Done;
Start: { Put $ (!String.IsNullOrEmpty(value.Key)) -> Start;
Get $ (!String.IsNullOrEmpty(value)) -> Start;
Done -> End; } }

33. Dataflow Networks Propagate data between Operators Operands
Computations (methods)
Buffers
Channel ports
Operators
forward, forward once, broadcast, alternate, multiplex, join
Operands
buffer, single-assignment, transforms, sinks
chan::Request ==> TranformString ==> chan::Reply;
buffer -<< { TransformString ==> sink, PrintString };

34. Ping Pong agent PingAgent : channel PingPong { public PingAgent()
Ping ==> (b => Signal.Value) ==> Pong; }

35. Ping Pong agent PingAgent : channel PingPong { public PingAgent()
Ping ==> Trace ==> Pong; }
private Signal Trace(bool inpt)
Console.WriteLine("{0}",inpt);
return Signal.Value;

36. Asynchrony Blocking a thread is costly
1MB (or more) stack space
Thread context
Context switching via ‘syscall’
Asynchronous APIs (APM, EAP, CCR, etc.)
Runtime efficient, state in heap
Complex to use in program code
Axum: let the compiler do the work!

37. Other Languages with Agents
Functional
Mostly pure message-passing
Dedicated virtual machine
Isolation through immutability
Transparent support for distribution
Support for asynchrony (receive)
Scala
Runs on JVM
No isolation, but some immutable types
Distribution a separate model
Basic support for asynchrony (react) F#
Functional
Runs on .NET
No isolation, but some immutable types
Distribution a separate model
Support for asynchrony (async + let!)

38. F# and Axum Axum model a closer fit with F# than C++/C#
Immutable collection types
Support for asynchrony
In-process agent API available
Add channels, isolation, schema and you have Axum
Check out Luke Hoban’s F# session next

39. Summary Shared Memory Pure Message Passing Axum
Complexity hard to rein in
Performance suffers as cache coherence dominates memory system
Pure Message Passing
Separation of components
High overhead
Does not leverage existing know-how
Axum
Balances shared memory and message-passing
Reduce complexity
Leverage existing patterns and libraries

40. Try it out! http://msdn.microsoft.com/devlabs

41. Parallel Computing at PDC09
Overview
FT07: The State of Parallel Programming
WKSP08: Patterns of Parallel Programming
Managed code in Visual Studio 2010
FT03: Manycore and .NET 4: A Match Made in Visual Studio 2010
FT21: PLINQ: LINQ, but Faster!
FT20: F# for Parallel and Asynchronous Programming
Native code in Visual Studio 2010
SVR18: Developing Applications for Scale-Up Servers Running Windows Server 2008 R2
SVR10: Lighting up Windows Server 2008 R2 Using the ConcRT on UMS
FT19: C++ Forever: Interactive Applications in the Age of Manycore
HPC Server
SVR01: Accelerating Applications Using Windows HPC Server 2008
Research and Incubation
VTL02: Axum: A .NET Language for Safe and Scalable Concurrency
VTL32: Concurrency Fuzzing & Data Races
SVR17: Data-Intensive Computing on Windows HPC Server with DryadLINQ
VTL04: Rx: Reactive Extensions for .NET
FT36: Future of Garbage Collection

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