Orca A language for parallel programming of distributed systems

2 Orca Parallel language designed at VU Design and first implementation (‘88-’92): –Bal, Kaashoek, Tanenbaum Portable Orca system (‘93-’97): –Bal, Bhoedjang, Langendoen, Rühl, Jacobs, Hofman Used by ~30 M.Sc. Students

3 Overview Distributed shared memory Orca –Shared data-object model –Processes –Condition synchronization –Language aspects Examples: TSP and ASP Implementation

4 Orca’s Programming Model Explicit parallelism (processes) Communication model: –Shared memory: hard to build –Distributed memory: hard to program Idea: shared memory programming model on distributed memory machine Distributed shared memory (DSM)

5 Distributed Shared Memory(1) Hardware (CC-NUMA): –Cache-coherent Non Uniform Memory Access –Processor can copy remote cache line –Hardware keeps caches coherent –Examples: DASH, Alewife, SGI Origin CPU cache/ memory remote read local read

6 Distributed Shared Memory(2) Operating system: –Shared virtual memory –Processor can fetch remote pages –OS keeps copies of pages coherent User-level system –Treadmarks –Uses OS-like techniques –Implemented with mmap and signals

7 Distributed Shared Memory(3) Languages and libraries –Do not provide flat address space –Examples: Linda: tuple spaces CRL: shared regions Orca: shared data-objects

8 Shared Data-object Model Shared data encapsulated in objects Object = variable of abstract data type Shared data accessed by user-defined, high- level operations Object Local data Enqueue( ) Dequeue( )

9 Semantics Each operation is executed atomically –As if operations were executed 1 at a time –Mutual exclusion synchronization –Similar to monitors Each operation applies to single object –Allows efficient implementation –Atomic operations on multiple objects are seldom needed and hard to implement

10 Implementation System determines object distribution It may replicate objects (transparently) Single-copy object Network CPU 1CPU 2 Replicated object Network CPU 1CPU 2

11 Object Types Abstract data type Two parts: 1Specification part ADT operations 2Implementation part Local data Code for operations Optional initialization code

12 Example: Intobject Specification part object specification IntObject; operation Value(): integer; operation Assign(Val: integer); operation Min(Val: integer); end; object specification IntObject; operation Value(): integer; operation Assign(Val: integer); operation Min(Val: integer); end;

13 Intobject Implementation Part object implementation IntObject; X: integer; # internal data of the object operation Value(): integer; begin return X; end; operation Assign(Val: integer); begin X := Val; end operation Min(Val: integer); begin if Val < X then X := Val; fi; end; object implementation IntObject; X: integer; # internal data of the object operation Value(): integer; begin return X; end; operation Assign(Val: integer); begin X := Val; end operation Min(Val: integer); begin if Val < X then X := Val; fi; end;

14 Usage of Objects # declare (create) object MyInt: IntObject; # apply operations to the object MyInt$Assign(5); tmp := MyInt$Value(); # atomic operation MyInt$Min(4); # multiple operations (not atomic) if MyInt$Value() > 4 then MyInt$Assign(4); fi; # declare (create) object MyInt: IntObject; # apply operations to the object MyInt$Assign(5); tmp := MyInt$Value(); # atomic operation MyInt$Min(4); # multiple operations (not atomic) if MyInt$Value() > 4 then MyInt$Assign(4); fi;

15 Parallelism Expressed through processes –Process declaration: defines behavior –Fork statement: creates new process Object made accessible by passing it as shared parameter (call-by-reference) Any other data structure can be passed by value (copied)

16 Example (Processes) # declare a process type process worker(n: integer; x: shared IntObject); begin #do work... x$Assign(result); end; # declare an object min: IntObject; # create a process on CPU 2 fork worker(100, min) on (2); # declare a process type process worker(n: integer; x: shared IntObject); begin #do work... x$Assign(result); end; # declare an object min: IntObject; # create a process on CPU 2 fork worker(100, min) on (2);

17 Structure of Orca Programs Initially there is one process (OrcaMain) A process can create child processes and share objects with them Hierarchy of processes communicating through objects No lightweight treads

18 Condition Synchronization Operation is allowed to block initially –Using one or more guarded statements Semantics: –Block until 1 or more guards are true –Select a true guard, execute is statements operation name(parameters); guard expr-1 do statements-1; od;.... guard expr-N do statements-N od; end; operation name(parameters); guard expr-1 do statements-1; od;.... guard expr-N do statements-N od; end;

19 Example: Job Queue object implementation JobQueue; Q: “queue of jobs”; operation addjob(j: job); begin enqueue(Q,j); end; operation getjob(): job; begin guard NotEmpty(Q) do return dequeue(Q); od; end; object implementation JobQueue; Q: “queue of jobs”; operation addjob(j: job); begin enqueue(Q,j); end; operation getjob(): job; begin guard NotEmpty(Q) do return dequeue(Q); od; end;

20 Traveling Salesman Problem Structure of the Orca TSP program JobQueue and Minimum are objects Master Slave JobQueueMinimum

21 Language Aspects (1) Syntax somewhat similar to Modula-2 Standard types: –Scalar (integer, real) –Dynamic arrays –Records, unions, sets, bags –Graphs Generic types (as in Ada) User-defined abstract data types

22 Language Aspects (2) No global variables No pointers Type-secure –Every error against the language rules will be detected by the compiler or runtime system Not object-oriented –No inheritance, dynamic binding, polymorphism

23 Example Graph Type: Binary Tree type node = nodename of BinTree; type BinTree = graph root: node; # global field nodes # fields of each node data: integer; LeftSon, RightSon: node; end; type node = nodename of BinTree; type BinTree = graph root: node; # global field nodes # fields of each node data: integer; LeftSon, RightSon: node; end; t: BinTree; # create tree n: node; # nodename variable n := addnode(t); # add a node to t t.root := n; # access global field t[n].data := 12; # fill in data on node n t[n].LeftSon := addnode(t); # create left son deletenode(t,n); # delete node n t[n].data := 11; # runtime error t: BinTree; # create tree n: node; # nodename variable n := addnode(t); # add a node to t t.root := n; # access global field t[n].data := 12; # fill in data on node n t[n].LeftSon := addnode(t); # create left son deletenode(t,n); # delete node n t[n].data := 11; # runtime error

24 Performance Issues Orca provides high level of abstraction  easy to program  hard to understand performance behavior Example: X$foo() can either result in: –function call (if X is not shared) –monitor call (if X is shared and stored locally) –remote procedure call (if X is stored remotely) –broadcast (if X is replicated)

25 Performance model The Orca system will: –replicate objects with a high read/write-ratio –store nonreplicated object on ``best’’ location Communication is generated for: –writing replicated object (broadcast) –accessing remote nonreplicated object (RPC) Programmer must think about locality

26 Summary of Orca Object-based distributed shared memory –Hides the underlying network from the user –Applications can use shared data Language is especially designed for distributed systems User-defined, high-level operations