1. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science X10: IBM’s bid into parallel languages Paul B Kohler Kevin S Grimaldi University of Massachusetts Amherst

2. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 2 introduction A new language based of Java IBM’s entry to the DARPA’s PERCS project (Productive Easy-to-use Reliable Computer Systems) Built for NUCCs(Non-Uniform Computing Clusters) where different memory locations incur different cost.

3. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 3 intro continued Will eventually be combined with new tools for Eclipse Goals Safe Analyzable Scalable Flexible

4. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 4 PGAS Past attempts at parallel languages have used the illusion of a single shared memory This does not represent the situation in NUCC. Problems occur when we try divide memory among processors. X10 uses PGAS to reveal the non- uniformity and make the language scalable.

5. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 5 PGAS(co nt) PGAS=Partitioned Global Address Space Memory partitioned into places. Data is associated with a place and can only be read/changed locally. Provided in X10 through the abstractions of places and activities.

6. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 6 Places Contain a collection of resident mutable data objects and associated activities Places represent locality boundaries Very efficient access to resident data Set of places remains fixed at runtime Places are virtual Mapped to physical processors by runtime Runtime may transparently migrate places

7. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 7 Using Places Accessible via place.places First activity runs at place.FIRST_PLACE Iterate over places with next() and prev() here represents current place

8. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 8 Activities Similar to java threads. Activities are associated with a place. Activities never migrate places. Activities may only read/modify mutable data that is local to its place. However immutable data (i.e.final or value) maybe accessed by any activity.

9. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 9 Activities (cont) Activities are GALS(Globally Asynchronous Locally Synchronous) Local data accesses are synchronized Global data accesses are not by default. Synchronization can be explicitly forced.

10. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 10 Activities:Syntax It is very simple to spawn new activities: async(place)statement This runs the specified statement at the specified place. Example: final int result; async(here.next()){result=a+b} This would add two numbers at the adjacent place and store the result(since result is final it can be accessed by other places)

11. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 11 Type System X10 is strongly typed Unified type system Everything is an object; no primitive types Library supplies boolean, byte, short, char, int, long, float, double, complex, String classes Borrows Java’s single inheritance combined with interfaces

12. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 12 Reference vs Value Types Two types of objects Value types are immutable and can be freely copied Reference types can contain mutable fields but cannot be migrated Value classes are declared value keyword instead of class Value classes can still contain fields that are of reference types Allows them to refer to mutable data Copying ‘bottoms out’ on reference fields

13. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 13 Type System (cont) Objects are either scalar or aggregate Each of value and reference types can be either scalar or aggregate Types consist of two parts Data type – The set of values it can take Place type – The place at which it resides No generics (yet)

14. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 14 Variables Variables must be initialized (can never be observed without a value) final variables cannot be changed after initialization Declared by using the final keyword and/or using a variable name that starts with a capital letter

15. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 15 Nullable Types Designers view ability to hold null value as orthogonal to value vs reference type Either reference or value types can be preceded by nullable Adds a null value to the type Multiple nullable s are collapsed (i.e. nullable nullable T = nullable T ) Can cast between T and nullable T (nullable T) v always succeeds (T) null throws an exception if T is not nullable

16. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 16 Rooted exceptions What should happen when a thread/activity terminates abnormally? In java it’s unclear since the spawning thread may have already terminated. X10 uses a rooted exception model. All uncaught exceptions get passed to the calling activity. A new blocking command finish s is introduced. This command waits for all activities in s to terminate before proceeding.

17. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 17 Exceptions (cont) Finish allows exceptions to travel back towards the root activity and possibly be caught and handled along the way. Example: try{ finish async(here.next()){ throw new Exception(); } catch(Exception e){ }

18. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 18 Arrays X10 features an array sub-language similar to ZPL. Arrays have: Regions Distributions Arrays are operated on by: for foreach ateach And more!

19. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 19 Even more arrays Arrays may be value(immutable) or reference(mutable) Keyword unsafe allows arrays that will play nice with java code. Arrays can run code as an initialization step.

20. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 20 Arrays:Regions Regions:As in ZPL a region is a set of indexed data points. Regions and distributions are first class constructs. Regions can be specified like this: [0:128,0:256] creates a region 128x256

21. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 21 Regions(cont.) Regions can be modified by operation such as union(||), intersection(&&) and set difference(-). Predefined regions types can be constructed using factories. region R2 = region.factory.upperTriangular(25) In the future users may be able to define there own regions.

22. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 22 Arrays:Distributions Every array has a distribution. A distribution is mapping of array elements to places. Distributions are over a particular region. Arrays are typed by their distribution.

23. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 23 Distributions cont. Currently must use pre-defined distributions(unique,block,cyclic…etc.) Have set operations like regions. Can be used as functions so for a point p and distribution d: d[p]=place which point p maps to(i.e. where the p’th element “lives”).

24. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 24 Subarrays Use various boolean operations on distributions to create subdistributions To get the portion of a block distribution that is located here: block([1:100]) && [1:100]->here a | D1 is the portion of array a corresponding to the subdistribution D1

25. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 25 Array construction Here is an example of array initialization: float [.] data= new[factory.cyclic([0:200,50:250])] (point [i, j]){return i+j};

26. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 26 Array construction Here is an example of array initialization: float [.] data= new[factory.cyclic([0:200,50:250])] (point [i, j]){return i+j}; This specifies a 200x200 region

27. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 27 Array construction Here is an example of array initialization: float [.] data= new[factory.cyclic([0:200,50:250])] (point [i, j]){return i+j}; This specifies a 200x200 region. This specifies a cyclic distribution over the region.

28. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 28 Array construction Here is an example of array initialization: float [.] data= new[factory.cyclic([0:200,50:250])] (point [i, j]){return i+j}; This specifies a 200x200 region. This specifies a cyclic distribution over the region. This code initialize each element to the some of its i,j coordinates

29. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 29 Array iteration Once you have an array what can you do with it? Array iterators: for, foreach, ateach for: Sequentially iterates over a supplied region. At each point it binds the point to a variable and executes the accompanying statement. foreach: As with for but operations are done in parallel. That is it spawns a new activity for each point. ateach: takes a distribution instead of a region. Performs operations in parallel at the place specified by the distribution.

30. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 30 Iteration example Example: for(point p : A){ A[p]=A[p]*A[p] }

31. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 31 More array ops lift: Takes a binary function and two arrays of the same distribution. Produces a new array formed by a pointwise application of the function to the two arrays. reduce: As in MPI applies a binary function to every element to produce a single value. scan: Creates a new array where the i’th element is the result of reduction on the first i elements.

32. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 32 Atomic Blocks X10 allows you to define atomic blocks The contents of a block is guaranteed to execute as a single atomic event. This is only in regards to other activities in the same place. While this is guaranteed to be atomic the details are implementation specific. Syntax: atomic S

33. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 33 Conditional Atmc Blck Also provides: when(Cond) S This blocks until cond is true and then executes S atomically. This allows the creation of a number of synchronization mechanisms. Dangerous! If cond is never true or if there is a cycle deadlock occurs.

34. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 34 Future and Force As discussed before futures allow the asynchronous computation of a value that may be used in the future. Futures return a object of type Future Force is a blocking call that waits for a particular future to be finished

35. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 35 Futures(cont.) Can only access final variables. This prevents side effects. Syntax: future(p)e Example: Future blah = future(here.next){sqrt(a^2+b^2)};

36. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 36 Clocks Act as barriers Much more flexible Guarantee no deadlock Dynamically associated with different sets of activities

37. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 37 Clock Semantics Activities register with zero or more clocks Can register/unregister at any time Clocks are always in some phase Do not advance until all currently registered activities quiesce Activities quiesce with next operation Indicates they are ready for all their clocks to advance Suspends until all clocks have advanced This makes deadlock impossible

38. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 38 Status IBM has supposedly built a single VM reference implementation Language still under heavy revision GPL’ed X10-XTC compiler available Doesn’t conform to current language spec Uses what will possibly be version 0.5 Speculatively contains support for operator overloading and generics Currently very poor performance

39. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 39 conclusion So is X10 the answer to all our parallel programming woes?

40. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 40 conclusion So is X10 the answer to all our parallel programming woes? In my opinion probably not.

41. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 41 conclusion So is X10 the answer to all our parallel programming woes? In my opinion probably not. Parallelism still very explicit. Still opportunities for deadlock, race conditions etc.

42. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 42 conclusion So is X10 the answer to all our parallel programming woes? In my opinion probably not. Parallelism still very explicit. Still opportunities for deadlock, race conditions etc. Takes a “…and the kitchen sink” approach which makes learning the syntax a chore.

43. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 43 conclusion So is X10 the answer to all our parallel programming woes? In my opinion probably not. Parallelism still very explicit. Still opportunities for deadlock, race conditions etc. Takes a “…and the kitchen sink” approach which makes learning the syntax a chore. It’s not FORTRAN. Will people bother to use it?