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1 Charm++ Tutorial Parallel Programming Laboratory, UIUC.

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1 1 Charm++ Tutorial Parallel Programming Laboratory, UIUC

2 2 Overview Introduction –Virtualization –Data Driven Execution in Charm++ –Object-based Parallelization Charm++ features with simple examples –Chares and Chare Arrays –Parameter Marshalling –Structured Dagger Construct –Load Balancing –Tools –Projections –LiveViz

3 3 Technical Approach Specialization Automation Decomposition done by programmer, everything else automated Seek optimal division of labor between “system” and programmer Scheduling Mapping Decomposition Charm++

4 4 Object - based Parallelization User View System implementation User is only concerned with interaction between objects

5 5 Virtualization: Object-based Decomposition Divide the computation into a large number of pieces –Independent of number of processors –Typically larger than number of processors Let the system map objects to processors

6 6 Chares – Concurrent Objects Can be dynamically created on any available processor Can be accessed from remote processors Send messages to each other asynchronously Contain “entry methods”

7 7.ci file mainmodule hello { mainchare main { entry main(); }; #include “hello.decl.h” class main : public CBase_main{ public: main(int argc, char **argv) { ckout <<“Hello World” <<endl; CkExit(); } }; #include “hello.def.h”.C file “Hello World!” Generates hello.decl.h hello.def.h

8 8 Compile and run the program Compiling charmc -o, -g, -language, -module, -tracemode pgm: pgm.ci pgm.h pgm.C charmc –c pgm.ci charmc –c pgm.c charmc –o pgm pgm.o –language charm++ To run a CHARM++ program named ``pgm'' on four processors, type: charmrun pgm +p4 Nodelist file (for network architecture) list of machines to run the program host

9 9 Data Driven Execution in Charm++ Scheduler Message Q Scheduler Message Q Objects x y CkExit() y->f() ??

10 10 Charm++ solution: proxy classes Proxy class generated for each chare class –For instance, CProxy_D is the proxy class generated for chare class D. –Proxy objects know where the real object is –Methods invoked on this object simply put the data in an “envelope” and send it out to the destination Given a proxy p, you can invoke methods –p.method(msg);

11 11 Ring program Array of Objects of the same kind Each one communicates with the next one Individual chares – cumbersome and not practical A collection of chares, –with a single global name for the collection –each member addressed by an index –Mapping of element objects to processors handled by the system

12 12 Chare Arrays A[1]A[0] System view A[1]A[0] A[1]A[2]A[3]A[..] User’s view

13 13 mainmodule m { readonly CProxy_main mainProxy; mainchare main{ …. } array [1D] Hello { entry Hello(void); entry void sayHi(int HiNo); }; (.ci) file int nElements=4; mainProxy = thisProxy; CProxy_Hello p = CProxy_Hello::ckNew(nElements); //Have element 0 say “hi” p[0].sayHi(12345); In main:: main() Array Hello p.SayHi(…) class Hello : public CBase_Hello { public: Hello(CkMigrateMessage *m){} Hello(); void sayHi(int hiNo); }; Class Declaration

14 14 void Hello::sayHi(int hiNo) { ckout << hiNo <<"from element" << thisIndex << endl; if (thisIndex < nElements-1) //Pass the hello on: thisProxy[thisIndex+1].sayHi(hiNo+1); else //We've been around once-- we're done. mainProxy.done(); } Array Hello Read-only Element index Array Proxy void main::done(void){ CkExit(); }

15 15 Basic Entities in Charm++ Programs Sequential Objects –ordinary sequential C++ code and objects Read-only variables –initialized in main::main() –used as “global” variables Chares (concurrent objects) Chare Arrays (an indexed collection of chares)

16 16 Illustrative example: Jacobi 1D Input: 2D array of values with boundary conditions In each iteration, each array element is computed as the average of itself and its neighbors Iterations are repeated till some threshold error value is reached

17 17 Jacobi 1D: Parallel Solution! Slice up the 2D array into sets of columns Chare = computations in one set At the end of each iteration –Chares exchange boundaries –Determine maximum change in computation Output result when threshold is reached

18 18 Arrays as Parameters Array cannot be passed as pointer specify the length of the array in the interface file – entry void bar(int n,double arr[n])

19 19 Jacobi Code void Ar1::doWork(int sendersID, int n, double arr[n]) { maxChange = 0.0; if (sendersID == thisIndex-1) { leftmsg = 1; // set boolean to indicate we received the right message } else if (sendersID == thisIndex+1) { rightmsg = 1; // set boolean to indicate we received the right message } // Rest of the code on the next slide … }

20 20 Reduction Like Barrier in MPI Apply a single operation (add, max, min,...) to data items scattered across many processors Collect the result in one place Reduce x across all elements –contribute(sizeof(x), &x, CkReduction::sum_int,processResult ); – Function “processResult()” All contribute calls from one array must name the same function

21 21 void Ar1::doWork(int sendersID, int n, double arr[n]) { //Code on previous slide … if (((rightmsg == 1) && (leftmsg == 1)) || ((thisIndex == 0) && (rightmsg == 1)) || ((thisIndex ==K-1) && (leftmsg == 1))) { // Both messages have been received and we can now compute the new values of the matrix … // Use a reduction to find determine if all of the maximum errors on each processor had a maximum change that is below our threshold value. contribute(8, &maxChange, CkReduction::max_double, cb); } } Jacobi Code Continued

22 22 Structured Dagger What is it? –A coordination language built on top of Charm++ Motivation: –To reduce the complexity of program development without adding any overhead

23 23 Structured Dagger Constructs atomic {code} – Specifies that no structured dagger constructs appear inside of the code so it executes atomically. overlap {code} – Enables all of its component constructs concurrently and can execute these constructs in any order. when {code} – Specifies dependencies between computation and message arrival.

24 24 Structure Dagger Constructs Continued if / else/ while / for – These are the same as their C++ conterparts, except that they can contain when blocks in their respective code segments. Hence execution can be suspended while they wait for messages. forall – Functions like a for statement, but enables its component constructs for its entire iteration space at once. As a result it doesn’t need to execute its iteration space in strict sequence.

25 25 Jacobi Example Using Structured Dagger jacobi.ci array[1D] Ar1 { … entry void GetMessages (MyMsg *msg) { when rightmsgEntry(MyMsg *right), leftmsgEntry(MyMsg *left) { atomic { CkPrintf(“Got both left and right messages \n”); doWork(right, left); } } }; entry void rightmsgEntry(MyMsg *m); entry void leftmsgEntry(MyMsg *m); … }; In a 1D jacobi that doesn’t use structured dagger the code in the.C file for the doWork function is much more complex. The code needs to manually check if both messages have been received by using if/else statements. By using structured dagger, the doWork function will not be called until both messages have been received. The compiler will translate the structured dagger code into code that will do the appropriate checks, hence making the programmers job simpler.

26 26 Screen shots – Load imbalance Jacobi 2048 X 2048 Threshold 0.1 Chares 32 Processors 4

27 27 Timelines – load imbalance

28 28 Migration Array objects can migrate from one PE to another To migrate, must implement pack/unpack or pup method pup combines 3 functions into one –Data structure traversal : compute message size, in bytes –Pack : write object into message –Unpack : read object out of message Basic Contract : here are my fields (types, sizes and a pointer)

29 29 Pup – How to write it? Class foo { double a; int x; char y; unsigned long z; float q[3]; int *r; // heap allocated memory public: … other methods … void pup(PUP:er &p) { p(a); p(x); p(y); p(z); p(q,3); if(p.isUnpacking() ) r = new int[ARRAY_SIZE]; p(r,ARRAY_SIZE); } };

30 30 Load Balancing All you need is a working pup link a LB module –-module –CommLB, GreedyRefLB, HeapCentLB, MetisLB, NeighborLB runtime option –+balancer CommLB

31 31 When to Re-balance Load? AtSync method: enable load balancing at specific point –Object ready to migrate –Re-balance if needed –AtSync(), ResumeFromSync() Manual trigger: specify when to do load balancing –All objects ready to migrate –Re-balance now –TurnManualLBOn(), StartLB() Default: Load balancer will migrate when needed Programmer Control

32 32 Processor Utilization: After Load Balance

33 33 Timelines: Before and After Load Balancing

34 34 Other tools: LiveViz

35 35 LiveViz – What is it? Charm++ library Visualization tool Inspect your program’s current state Client runs on any machine (java) You code the image generation 2D and 3D modes

36 36 LiveViz – Monitoring Your Application LiveViz allows you to watch your application’s progress Can use it from work or home Doesn’t slow down computation when there is no client

37 37 LiveViz - Compilation Compile the LiveViz library itself –Must have built charm++ first! From the charm directory, run: –cd tmp/libs/ck-libs/liveViz –make

38 38 Running LiveViz Build and run the server –cd pgms/charm++/ccs/liveViz/serverpush –Make –./run_server Or in detail…

39 39 Running LiveViz Run the client –cd pgms/charm++/ccs/liveViz/client –./run_client [ [ ]] Should get a result window:

40 40 LiveViz Request Model LiveViz Server Code Client Parallel Application Get Image Poll for Request Poll Request Returns Work Image Chunk Passed to Server Server Combines Image Chunks Send Image to Client Buffer Request

41 41 Main: Setup worker array, pass data to them Workers: Start looping Send messages to all neighbors with ghost rows Wait for all neighbors to send ghost rows to me Once they arrive, do the regular Jacobi relaxation Calculate maximum error, do a reduction to compute global maximum error If timestep is a multiple of 64, load balance the computation. Then restart the loop. Jacobi 2D Example Structure Main: Setup worker array, pass data to them Workers: Start looping Send messages to all neighbors with ghost rows Wait for all neighbors to send ghost rows to me Once they arrive, do the regular Jacobi relaxation Calculate maximum error, do a reduction to compute global maximum error If timestep is a multiple of 64, load balance the computation. Then restart the loop. Main: Setup worker array, pass data to them Workers: Start looping Send messages to all neighbors with ghost rows Wait for all neighbors to send ghost rows to me Once they arrive, do the regular Jacobi relaxation Calculate maximum error, do a reduction to compute global maximum error If timestep is a multiple of 64, load balance the computation. Then restart the loop.

42 42 #include void main::main(...) { // Do misc initilization stuff // Now create the (empty) jacobi 2D array work = CProxy_matrix::ckNew(0); // Distribute work to the array, filling it as you do } #include void main::main(...) { // Do misc initilization stuff // Create the workers and register with liveviz CkArrayOptions opts(0);// By default allocate 0 // array elements. liveVizConfig cfg(true, true);// color image = true and // animate image = true liveVizPollInit(cfg, opts);// Initialize the library // Now create the jacobi 2D array work = CProxy_matrix::ckNew(opts); // Distribute work to the array, filling it as you do } LiveViz Setup

43 43 Adding LiveViz To Your Code void matrix::serviceLiveViz() { liveVizPollRequestMsg *m; while ( (m = liveVizPoll((ArrayElement *)this, timestep)) != NULL ) { requestNextFrame(m); } void matrix::startTimeSlice() { // Send ghost row north, south, east, west,... sendMsg(dims.x-2, NORTH, dims.x+1, 1, +0, -1); } void matrix::startTimeSlice() { // Send ghost row north, south, east, west,... sendMsg(dims.x-2, NORTH, dims.x+1, 1, +0, -1); // Now having sent all our ghosts, service liveViz // while waiting for neighbor’s ghosts to arrive. serviceLiveViz(); }

44 44 Generate an Image For a Request void matrix::requestNextFrame(liveVizPollRequestMsg *m) { // Compute the dimensions of the image bit we’ll send // Compute the image data of the chunk we’ll send – // image data is just a linear array of bytes in row-major // order. For greyscale it’s 1 byte, for color it’s 3 // bytes (rgb). // The liveViz library routine colorScale(value, min, max, // *array) will rainbow-color your data automatically. // Finally, return the image data to the library liveVizPollDeposit((ArrayElement *)this, timestep, m, loc_x, loc_y, width, height, imageBits); }

45 45 OPTS=-g CHARMC=charmc $(OPTS) LB=-module RefineLB OBJS = jacobi2d.o all: jacobi2d jacobi2d: $(OBJS) $(CHARMC) -language charm++ \ -o jacobi2d $(OBJS) $(LB) –lm jacobi2d.o: jacobi2d.C jacobi2d.decl.h $(CHARMC) -c jacobi2d.C OPTS=-g CHARMC=charmc $(OPTS) LB=-module RefineLB OBJS = jacobi2d.o all: jacobi2d jacobi2d: $(OBJS) $(CHARMC) -language charm++ \ -o jacobi2d $(OBJS) $(LB) -lm \ -module liveViz jacobi2d.o: jacobi2d.C jacobi2d.decl.h $(CHARMC) -c jacobi2d.C Link With The LiveViz Library

46 46 LiveViz Summary Easy to use visualization library Simple code handles any number of clients Doesn’t slow computation when there are no clients connected Works in parallel, with load balancing, etc.

47 47 Advanced Features Priorities –Each message(method invocation) can have a priority –Integer or bit vector Complex index types for arrays –2D, 3D …6D –User defined indexes, eg: Oct-tree Object groups –Similar to arrays but with exactly one element on each processor –Find local member via function call : Synchronous Method invocation

48 48 Entry Method Attributes entry [ attribute1,..., attributeN ] void EntryMethod ( parameters ); Attributes: threaded –entry methods which are run in their own non- preemptible threads sync –methods return message as a result

49 49 Advanced features: Reductions Callbacks –transfer control back to a client after a library has finished –Various pre-defined callbacks, eg: CkExit the program Callbacks in reductions –contribute(sizeof(x), &x, CkReduction::sum_int,processResult ); –myProxy.ckSetReductionClient(new CkCallback(...)); User defined reductions –performing a user-defined operation on user-defined data

50 50 Benefits of Virtualization Better Software Engineering –Logical Units decoupled from “Number of processors” Message Driven Execution –Adaptive overlap between computation and communication –Predictability of execution Flexible and dynamic mapping to processors –Flexible mapping on clusters –Change the set of processors for a given job –Automatic Checkpointing Principle of Persistence

51 51 More Information http://charm.cs.uiuc.edu –Manuals –Papers –Download files –FAQs ppl@cs.uiuc.edu


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