Concurrent Revisions: A Model for Deterministic Concurrency
This content discusses a deterministic concurrency model called Concurrent Revisions, focusing on interactive applications with large shared data structures. It covers the challenges of conflicting tasks, conventional concurrency control methods, and proposes a programming model based on revisions and isolation types for efficient parallel execution.
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Concurrent Revisions: A deterministic concurrency model. Daan Leijen, Alexandro Baldassin, and Sebastian Burckhardt Microsoft Research (OOPSLA 2010)
The concurrency elephant Task/Data Parallel: TPL, X10, Cilk, StreamIt, Cuda, OpenMP, etc. Concurrent: Thread, Locks, Promises, Transactions, etc. Our focus: Concurrent interactive applications with large shared data structures.
Application = Shared Data and Tasks Example: Office application Save the document React to keyboard input by the user Perform a spellcheck in the background Exchange updates with remote users Reader Mutator Reader Shared Data Mutator Reader
Beginning .NET Game Programming in C# Spacewars! About 15k lines of C# code, using DirectX. The original game is sequential
Examples from SpaceWars Game Example 1: read-write conflict Render task reads position of all game objects Physics task updates position of all game objects => Render task needs to see consistent snapshot Example 2: write-write conflict Physics task updates position of all game objects Network task updates position of some objects => Network has priority over physics updates
Conventional Concurrency Control Conflicting tasks can not efficiently execute in parallel. pessimistic concurrency control (i.e. locks) use locks to avoid parallelism where there are (real or potential) conflicts optimistic concurrency control (i.e. TM) speculate on absence of conflicts rollback if there are real conflicts either way: true conflicts kill parallelism.
Our Proposed Programming Model: Revisions and Isolation Types Revision A logical unit of work that is forked and joined Isolation Type A type which implements automatic copying/merging of versions on write-write conflict Deterministic Conflict Resolution, never roll-back No restrictions on tasks (can be long-running, do I/O) Full concurrent reading and writing of shared data Clean semantics (see technical report) Fast and space-efficient runtime implementation
No isolation: What s new We see either 0 or 1 depending on the schedule fork revision: forks off a private copy of the shared state Isolation types: declares shared data Concurrent Revisions Traditional Task int x = 0; Task t = fork { x = 1; } assert(x==0 || x==1); join t; assert(x==1); Versioned<int> x = 0; Revision r = rfork { x = 1; } assert(x==0); join r; assert(x==1); isolation: Isolation: side effects are only visible when the revision is joined. Deterministic execution! Concurrent modifications are not seen by others join revision: waits for the revision to terminate and writes back changes into the main revision
Puzzle time int x = 0; int y = 0; Task t = fork { if (x==0) y++; } if (y==0) x++; join t; Hard to read: let s use a diagram instead
Sequential consistency int x = 0 int y = 0 if (y==0) x++; if (x==0) y++; assert( (x==0 && y==1) || (x==1 && y==0) || (x==1 && y==1)); What are the possible values for x and y? ??
Transactional memory int x = 0 int y = 0 atomic { if (y==0) x++; } atomic { if (x==0) y++; } ?? assert( (x==0 && y==1) || (x==1 && y==0));
Concurrent revisions Versioned<int> x = 0 Versioned<int> y = 0 Isolation and x is always 0 Isolation y is always 0 if (y==0) x++; if (x==0) y++; Determinism only 1 possible result assert(x==1 && y==1); ??
Conflict resolution By default, on a write-write conflict (only), the modification in the child revision wins. Versioned<int> x; x = 0 x = 0 x = 0 x = 1 x = 2 x = 1 x = 0 x = 1 assert(x==2) assert(x==0) assert(x==1)
Custom conflict resolution Cumulative<int, (main,join,orig).main + join orig> x; x = 0 0 x += 1 x += 2 merge(1,2,0) 3 1 2 assert(x==3)
Demo class Sample { [Versioned] int i = 0; public void Run() { var r = CurrentRevision.Fork(() => { i += 1; }); i += 2; CurrentRevision.Join(r); Console.WriteLine("i = " + i); } }
Demo: Sandbox Fork a revision without forking an associated task/thread class Sandbox { [Versioned] int i = 0; public void Run() { var r = CurrentRevision.Branch("FlakyCode"); try { r.Run(() => { i = 1; throw new Exception("Oops"); }); CurrentRevision.Merge(r); } catch { CurrentRevision.Abandon(r); } Console.WriteLine("\n i = " + i); } } Run code in a certain revision Merge changes in a revision into the main one Abandon a revision and don t merge its changes.
A Software engineering perspective Transactional memory: Codecentric: put atomic in the code Granularity: too broad: too many conflicts and no parallel speedup too small: potential races and incorrect code Concurrent revisions: Data centric: put annotations on the data Granularity: group data that have mutual constraints together, i.e. if (x + y > 0) should hold, then x and y should be versioned together.
Current Implementation: C# library For each versioned object, maintain multiple copies Map revision ids to versions `mostly lock-free array Revision Value 1 0 40 2 45 7 New copies are allocated lazily Don t copy on fork copy on first write after fork Old copies are released on join No space leak
SpaceWars Game Parallel Collision Detection Parallel Collision Detection Parallel Collision Detection Render Screen Graphics Card Simulate Physics Sequential Game Loop: Play Sounds Shared State Send Process Inputs Receive Autosave Key- board Network Connection Disk
Revision Diagram for Parallelized Game Loop Coll. Det. 4 Coll. Det. 1 Coll. Det. 2 Coll. Det. 3 network Render Physics (long running) autosave
Problem Example 1 is solved Render task reads position of all game objects Physics task updates position of all game objects No interference! Coll. Det. 4 Coll. Det. 1 Coll. Det. 2 Coll. Det. 3 network Render Physics (long running) autosave
Problem Example 2 is solved. Physics task updates position of all game objects Network task updates position of some objects Network updates have priority over physics updates Order of joins establishes precedence! Coll. Det. 4 Coll. Det. 1 Coll. Det. 2 Coll. Det. 3 network Render Physics (long running) autosave
Results Physics task Render Collision detection Autosave now perfectly unnoticeable in background Overall Speed-Up: 3.03x on four-core (almost completely limited by graphics card)
Only a 5% slowdown in the sequential case Overhead: How much does all the copying and the indirection cost? Some individual tasks slow down much more (i.e. physics simulation)
Conclusion Revisions and Isolation Types simplify the parallelization of applications with tasks that Exhibit conflicting accesses to shared data Have unpredictable latency Have unpredictable data access pattern May perform I/O that can not be rolled back Revisions and Isolation Types are easy to reason about (determinism, isolation) have low-enough overhead for many applications
Questions? daan@microsoft.com sburckha@microsoft.com External download available soon
Sequential Consistency int x = 0; int y = 0; task t = fork { if (x==0) y++; } if (y==0) x++; join t; assert( (x==0 && y==1) || (x==1 && y==0) || (x==1 && y==1)); Concurrent Revisions Transactional Memory int x = 0; int y = 0; task t = fork { atomic { if (x==0) y++; } } atomic { if (y==0) x++; } join t; versioned<int> x = 0; versioned<int> y = 0; revision r = rfork { if (x==0) y++; } if (y==0) x++; join r; assert( (x==0 && y==1) || (x==1 && y==0)); assert(x==1 && y==1);
x = 0 0 x += 1 x += 2 2 x += 3 merge(1,2,0) 3 1 2 3 5 merge(3,5,2) 6 assert( x==6 )
By construction, there is no global state: just local state for each revision State is simply a (partial) function from a location to a value
Operational Semantics For some revision r, with snapshot and local modifications and an expression context with hole( x.e) v the state is a composition of the root snapshot and local modifications On a join, the writes of the joinee r take priority over the writes of the current revision: :: On a fork, the snapshot of the new revision r is the current state: ::
Custom merge: per location (type) No conflict if a location was not written in the joinee On a join, using a merge function. No conflict if a location was unmodified in the current revision, use the value of the joinee Conflict otherwise, use a location/type specific merge function Standard merges:
What is a conflict? Merge is only called if: (1) write in child, and (2) modification in main revision: Cumulative<int> x = 0 0 x += 2 2 No conflict (merge function is not called) x += 3 0 2 2 5 No conflict (merge function is not called) assert( x = 5 )
Merging with failure On fail, we just ignore any writes in the joinee
Snapshot isolation Widely used in databases, for example Oracle and Microsoft SQL In essence, in snapshot isolation a concurrent transaction can only complete in the absence of write-write conflicts. Our calculus generalizes snapshot isolation: We support arbitrary nesting We allow custom merge functions to resolve write-write conflicts deterministically
Snapshot isolation We can succinctly model snapshot isolation as: Disallow nesting Use the default merge: Some versions of snapshot isolation do not treat silent writes in a transaction as a conflict:
Sequential merges We can view each location as an abstract data types (i.e. object) with certain operations (i.e. methods). If a merge function always behaves as if concurrent operations for those objects are sequential, we call it a sequential merge. Such objects always behave as if the operations in the joinee are all done sequentially at the join point.
Sequential merges x = o A merge is sequential if: u merge(uw1(o), uw2(o), u(o)) = uw1w2(o) w1 w2 Anduw1w2(o) merge(uw1(o), uw2(o), u(o))
Abelian merges For any abstract data type that forms an abelian group (associative, commutative, with inverses) with neutral element 0 and an operation , the following merge is sequential: merge(v,v ,v0) = v v v0 This holds for example for additive integers and additive sets.
