ECE1747 Parallel Programming

1. ECE1747 Parallel Programming Shared Memory Multithreading Pthreads

2. Shared Memory • All threads access the same shared memory data space. Shared Memory Address Space proc1 proc2 proc3 procN

3. Shared Memory (continued) • Concretely, it means that a variable x, a pointer p, or an array a[] refer tothe same object, no matter what processor the reference originates from. • We have more or less implicitly assumed this to be the case in earlier examples.

4. Shared Memory a proc1 proc2 proc3 procN

5. Distributed Memory - Message Passing The alternative model to shared memory. mem1 mem2 mem3 memN a a a a proc1 proc2 proc3 procN network

6. Shared Memory vs. Message Passing • Same terminology is used in distinguishing hardware. • For us: distinguish programming models, not hardware.

7. Programming vs. Hardware • One can implement • a shared memory programming model • on shared or distributed memory hardware • (also in software or in hardware) • One can implement • a message passing programming model • on shared or distributed memory hardware

8. Portability of programming models shared memory programming message passing programming shared memory machine distr. memory machine

9. Shared Memory Programming: Important Point to Remember • No matter what the implementation, it conceptually looks like shared memory. • There may be some (important) performance differences.

10. Multithreading • User has explicit control over thread. • Good: control can be used to performance benefit. • Bad: user has to deal with it.

11. Pthreads • POSIX standard shared-memory multithreading interface. • Provides primitives for process management and synchronization.

12. What does the user have to do? • Decide how to decompose the computation into parallel parts. • Create (and destroy) processes to support that decomposition. • Add synchronization to make sure dependences are covered.

13. General Thread Structure • Typically, a thread is a concurrent execution of a function or a procedure. • So, your program needs to be restructured such that parallel parts form separate procedures or functions.

14. Example of Thread Creation (contd.) main() pthread_ create(func) func()

15. Thread Joining Example void *func(void *) { ….. } pthread_t id; int X; pthread_create(&id, NULL, func, &X); ….. pthread_join(id, NULL); …..

16. Example of Thread Creation (contd.) main() pthread_ create(func) func() pthread_ join(id) pthread_ exit()

17. Sequential SOR for some number of timesteps/iterations { for (i=0; i<n; i++ ) for( j=1, j<n, j++ ) temp[i][j] = 0.25 * ( grid[i-1][j] + grid[i+1][j] grid[i][j-1] + grid[i][j+1] ); for( i=0; i<n; i++ ) for( j=1; j<n; j++ ) grid[i][j] = temp[i][j]; }

18. Parallel SOR • First (i,j) loop nest can be parallelized. • Second (i,j) loop nest can be parallelized. • Must wait to start second loop nest until all processors have finished first. • Must wait to start first loop nest of next iteration until all processors have second loop nest of previous iteration. • Give n/p rows to each processor.

19. Pthreads SOR: Parallel parts (1) void* sor_1(void *s) { int slice = (int) s; int from = (slice*n)/p; int to = ((slice+1)*n)/p; for( i=from; i<to; i++) for( j=0; j<n; j++ ) temp[i][j] = 0.25*(grid[i-1][j] + grid[i+1][j] +grid[i][j-1] + grid[i][j+1]); }

20. Pthreads SOR: Parallel parts (2) void* sor_2(void *s) { int slice = (int) s; int from = (slice*n)/p; int to = ((slice+1)*n)/p; for( i=from; i<to; i++) for( j=0; j<n; j++ ) grid[i][j] = temp[i][j]; }

21. Pthreads SOR: main for some number of timesteps { for( i=0; i<p; i++ ) pthread_create(&thrd[i], NULL, sor_1, (void *)i); for( i=0; i<p; i++ ) pthread_join(thrd[i], NULL); for( i=0; i<p; i++ ) pthread_create(&thrd[i], NULL, sor_2, (void *)i); for( i=0; i<p; i++ ) pthread_join(thrd[i], NULL); }

22. Summary: Thread Management • pthread_create(): creates a parallel thread executing a given function (and arguments), returns thread identifier. • pthread_exit(): terminates thread. • pthread_join(): waits for thread with particular thread identifier to terminate.

23. Summary: Program Structure • Encapsulate parallel parts in functions. • Use function arguments to parameterize what a particular thread does. • Call pthread_create() with the function and arguments, save thread identifier returned. • Call pthread_join() with that thread identifier.

24. Pthreads Synchronization • Create/exit/join • provide some form of synchronization, • at a very coarse level, • requires thread creation/destruction. • Need for finer-grain synchronization • mutex locks, • condition variables.

25. Use of Mutex Locks • To implement critical sections. • Pthreads provides only exclusive locks. • Some other systems allow shared-read, exclusive-write locks.

26. Condition variables (1 of 5) pthread_cond_init( pthread_cond_t *cond, pthread_cond_attr *attr) • Creates a new condition variable cond. • Attribute: ignore for now.

27. Condition Variables (2 of 5) pthread_cond_destroy( pthread_cond_t *cond) • Destroys the condition variable cond.

28. Condition Variables (3 of 5) pthread_cond_wait( pthread_cond_t *cond, pthread_mutex_t *mutex) • Blocks the calling thread, waiting on cond. • Unlocks the mutex.

29. Condition Variables (4 of 5) pthread_cond_signal( pthread_cond_t *cond) • Unblocksone thread waiting on cond. • Which one is determined by scheduler. • If no thread waiting, then signal is a no-op.

30. Condition Variables (5 of 5) pthread_cond_broadcast( pthread_cond_t *cond) • Unblocks all threads waiting on cond. • If no thread waiting, then broadcast is a no-op.

31. Use of Condition Variables • To implement signal-wait synchronization discussed in earlier examples. • Important note: a signal is “forgotten” if there is no corresponding wait that has already happened.

32. Barrier Synchronization • A wait at a barrier causes a thread to wait until all threads have performed a wait at the barrier. • At that point, they all proceed.

33. Implementing Barriers in Pthreads • Count the number of arrivals at the barrier. • Wait if this is not the last arrival. • Make everyone unblock if this is the last arrival. • Since the arrival count is a shared variable, enclose the whole operation in a mutex lock-unlock.

34. Implementing Barriers in Pthreads void barrier() { pthread_mutex_lock(&mutex_arr); arrived++; if (arrived<N) { pthread_cond_wait(&cond, &mutex_arr); } else { pthread_cond_broadcast(&cond); arrived=0; /* be prepared for next barrier */ } pthread_mutex_unlock(&mutex_arr); }

35. Parallel SOR with Barriers (1 of 2) void* sor (void* arg) { int slice = (int)arg; int from = (slice * (n-1))/p + 1; int to = ((slice+1) * (n-1))/p + 1; for some number of iterations { … } }

36. Parallel SOR with Barriers (2 of 2) for (i=from; i<to; i++) for (j=1; j<n; j++) temp[i][j] = 0.25 * (grid[i-1][j] + grid[i+1][j] + grid[i][j-1] + grid[i][j+1]); barrier(); for (i=from; i<to; i++) for (j=1; j<n; j++) grid[i][j]=temp[i][j]; barrier();

37. Parallel SOR with Barriers: main int main(int argc, char *argv[]) { pthread_t *thrd[p]; /* Initialize mutex and condition variables */ for (i=0; i<p; i++) pthread_create (&thrd[i], &attr, sor, (void*)i); for (i=0; i<p; i++) pthread_join (thrd[i], NULL); /* Destroy mutex and condition variables */ }

38. Note again • Many shared memory programming systems (other than Pthreads) have barriers as basic primitive. • If they do, you should use it, not construct it yourself. • Implementation may be more efficient than what you can do yourself.

39. Busy Waiting • Not an explicit part of the API. • Available in a general shared memory programming environment.

40. Busy Waiting initially: flag = 0; P1: produce data; flag = 1; P2: while( !flag ) ; consume data;

41. Use of Busy Waiting • On the surface, simple and efficient. • In general, not a recommended practice. • Often leads to messy and unreadable code (blurs data/synchronization distinction). • May be inefficient

42. Private Data in Pthreads • To make a variable private in Pthreads, you need to make an array out of it. • Index the array by thread identifier, which you should keep track of . • Not very elegant or efficient.

43. Other Primitives in Pthreads • Set the attributes of a thread. • Set the attributes of a mutex lock. • Set scheduling parameters.

44. ECE 1747 Parallel Programming Machine-independent Performance Optimization Techniques

45. Returning to Sequential vs. Parallel • Sequential execution time: t seconds. • Startup overhead of parallel execution: t_st seconds (depends on architecture) • (Ideal) parallel execution time: t/p + t_st. • If t/p + t_st > t, no gain.

46. General Idea • Parallelism limited by dependences. • Restructure code to eliminate or reduce dependences. • Sometimes possible by compiler, but good to know how to do it by hand.

47. Optimizations: Example 16 for (i = 0; i < 100000; i++) a[i + 1000] = a[i] + 1; Cannot be parallelized as is. May be parallelized by applying certain code transformations.

48. Example Transformation for (i=1; i < 100; i++){ int stride = i* 1000; for (j = 0; j < 1000; j++) a[stride+j] = a[j] + i; }

49. Code Transformations • Reorganize code such that • dependences are removed or reduced • large pieces of parallel work emerge • Code can become messy … there is a point of diminishing returns.

50. Flavors of Parallelism • Task parallelism: processors do different tasks. • Task queue • Pipelines • Data parallelism: all processors do the same thing on different data. • Regular • Irregular