Task-based Execution of Nested OpenMP Loops

Size: px

Start display at page:

Download "Task-based Execution of Nested OpenMP Loops"

David Grant
6 years ago
Views:

1 Task-based Execution of Nested OpenMP Loops Spiros N. Agathos Panagiotis E. Hadjidoukas Vassilios V. Dimakopoulos Department of Computer Science UNIVERSITY OF IOANNINA Ioannina, Greece

2 Presentation Layout Introduction Manual transformation of omp for Transformation Limitations Automatic Transformation in OMPi Evaluation #2

3 OpenMP Initially designed for Loop parallelism Nested parallelism important feature V3.0 Tasks Nested parallelism Introduction Difficult to handle efficiently o Possible processor oversubscription Ways of controlling overheads using: o Environmental variables (e.g. OMP_MAX_ACTIVE_LEVELS) o collapse clause (when applicable) #3

4 Introduction OpenMP parallel loops, structures with independent iterations Fortran 95 : FORALL Intel TBB : parallel_for Cilk++ : cilk_for TBB and Cilk++ use tasking mechanisms to perform the job Can an OpenMP implementation do the same? #4

5 Transforming Loop Code Manually #pragma omp parallel num_threads(m) #pragma omp parallel num_threads(m) { #pragma omp parallel for\ schedule(static) num_threads(n) for (i=lb; i<ub; i++) { <body> { for(t=0; t<n; t++) #pragma omp task { calculate(n, LB, UB, &lb, &ub); for (i=lb; i<ub; i++) <body> #pragma omp taskwait #N implicit tasks transformed to #N explicit tasks Instead of NxM, #M threads in system #5

6 Manually transforming Code Example Face Detection Application for each scale (< 14) { /* level 1 */ for i=1 to 4 { 16 core machine (2xAMD 6128 Opteron) <body1> for i=1 to 14 { <body2> for i=1 to 14 { <body3> GCC #6

7 Manually transforming Code Example Face Detection Application for each scale (< 14) { /* level 1 */ for i=1 to 4 { 16 core machine (2xAMD 6128 Opteron) <body1> for i=1 to 14 { <body2> for i=1 to 14 { <body3> ICC #7

8 Similar Transformation is possible for Dynamic Guided Manual Transformation Limitations Complicated user code Thread specific data whithin loops: Calls to omp_get_thread_num() utilizing thread s ID Accesses to threadprivate variables #8

9 Manual Transformation Limitations In general: A mini worksharing must be written What thread will execute what task? Impossible to handle thread specific data But within an OpenMP runtime system: All the worksharing functionality already there Access to all thread specific data Auto transformation in OMPi compiler #9

10 OMPi C Compiler OMPi (Univ. of Ioannina, V3.0 OpenMP C infrastructure Source-to-source compiler + Runtime Basic code Transformation: outlining for parallel and task regions pragma omp parallel { <parallel code body> pragma omp task { <task code body> thread_func0 { <parallel code body> task_func0 { <task code body> #10

11 OMPi s Runtime Organization Each OpenMP thread is associated with an EECB (Execution Entity Control Block) EECB All OpenMP thread info: 1) Thread ID 2) Parallel level 3) Pointer to parent EECB 4) #11

12 OMPi s Runtime Organization for(i=0; i<4; i++) #pragma omp task Thread ID = 0 Level = 0 Initial Implicit Task TASK_QUEUE #12

13 OMPi s Runtime Organization #pragma omp parallel num_threads(4) Thread ID = 0 Level = 0 Initial Implicit Task P0 Implicit Task P1 Implicit Task P2 Implicit Task P3 Implicit Task #13

14 OMPi s Runtime Organization A parallel team of 4 threads Thread ID = 0 Level = 0 Initial Implicit Task TASK_QUEUE table P0 Thread ID = 0 Thread ID = 1 P1 Thread 0 Thread 1 Thread 2 Thread 3 P2 Thread ID = 2 Thread ID = 3 P3 #14

15 Auto Transformation Implementation Compiler Side Almost no changes Extra flag to notify runtime for combined parallel for Runtime: New type of task called pfor_task Emulation of implicit tasks using explicit tasking #15

16 Nested Parallel for Execution P0 P1 S0 Thread ID = 0 Thread ID = 1 Thread 0 Thread 1 Thread 2 Thread 3 S3 S1 S2 P2 P3 Thread ID = 2 Thread ID = 3 #pragma omp parallel for num_threads(4) S0 pfor Task TASKWAIT S1 pfor Task S2 pfor Task S3 pfor Task #16

17 Nested Parallel for Execution P0 P1 S0 Thread ID = 0 Thread ID = 1 Thread 0 Thread 1 Thread 2 Thread 3 S3 S1 S2 P2 P3 Thread ID = 2 Thread ID = 3 Workstealing! S3 id=3 Thread ID = 3 Level = 2 Thread ID = 0 Level = 2 S0 id=0 #17

18 Nested Parallel for Execution P0 P1 Thread ID = 0 Thread ID = 1 Thread 0 Thread 1 Thread 2 Thread 3 S1 S2 P2 P3 Thread ID = 2 Thread ID = 3 S3 S2 id=3 id=2 Thread ID = 23 Level = 2 Thread ID = 10 Level = 2 S0 S1 id=0 id=1 #18

19 Nested Parallel for Execution P0 P1 Thread ID = 0 Thread ID = 1 Thread 0 Thread 1 Thread 2 Thread 3 Thread ID = 2 P2 Thread ID = 3 P3 S2 id=2 Thread ID = 2 Level = 2 Thread ID = 1 Level = 2 S1 id=1 #19

20 Auto Transformation Concerns All schedule types are supported Worksharing subsystem remains same Important : Less threads than num_threads(x) may execute parallel for Can this cause a problem? #20

21 ORDERED clause Auto Transformation Concerns Enforces ordering in the execution of iterations Can task execution order (scheduling) cause problems? Dynamic, Guided schedules No problem o If a thread (task) blocks at an ordered region, this means that previous iterations have already been given away o Hence, progress is guaranteed #21

22 Auto Transformation Concerns Static schedule + chunk size Each task is responsible to execute a precalculated number of iterations #pragma omp parallel for schedule(static, 10) num_threads(4) TASK 0 TASK 1 TASK 2 TASK 3 TASK 0 TASK 1 TASK 2 TASK 3 Iterations = 80 Scheduling depends on num_threads() #22

23 Auto Transformation Concerns Static + chunk size + ordered scheduling #pragma omp parallel for schedule(static, 10) ordered num_threads(4) TASK 0 TASK 1 TASK 2 TASK 3 TASK 0 TASK 1 TASK 2 TASK 3 Iterations = 80 In case of 2 threads Iterations 20 to 79 will never be executed! Deadlock! #23

24 Currently: Auto Transformation Concerns Engineering decision: for this special case we disable tasking transformation, and use threads OpenMP v3.1 taskyield may be able to guarantee progress (currently working on it) #24

25 Evaluation Environment 2X 8-core AMD Opteron GHz 16GB of main memory Debian Squeeze on the kernel GNU gcc (version ) [-O3 -fopenmp] Intel icc (version ) [-fast -openmp] Oracle suncc (version 12.2) [-fast -xopenmp=parallel] OMPi uses GNU gcc as a back-end compiler [-O3] Default Runtime Settings #25

26 Synthetic Benchmark main() { #pragma omp parallel for num_threads(16) for (i=0; i < 16;t++) testpfor(); testpfor() { for(i=0; i <= 100K; i++){ #pragma omp parallel for num_threads(n) for (j=0; j < N; j++) delay(task_load); #26

27 Synthetic Benchmark Results TASK_LOAD = Threads in 1 st Level #27

28 Synthetic Benchmark Results 16 Threads in 1 st Level N (L2 Threads) = 4 #28

29 Face Detection Application Takes as input an image Discovers the number of faces, their position in the image Varying #scales (depends of image usually < 14) Level 1 Unbalanced iterations : Dynamic Schedule Level 2 Static Schedule for each scale { /* level 1 */ for i=1 to 4 {/*level 2*/ <body1> for i=1 to 14 {/*level 2*/ <body2> for i=1 to 14 {/*level 2*/ <body3> #29

30 Face Detection Results 161 Images CMU test set Speedup for each compiler is calculated w.r.t. its own sequential execution #30

31 Face Detection Results Class 57 image from CMU test set Speedup for each compiler is calculated w.r.t. its sequential execution #31

32 Face Detection Results Compiler Best Configuration SpeedUp OMPi improvement GCC 6 X % ICC 12 X % SUNCC 4 X % OMPi 16 X Best configurations and comparison with OMPi when processing all images Speedup is calculated in comparison to the best sequential time overall #32

33 END #33

34 Parallel for Execution EECB Thread ID = 0 P0 Impl task EECB Thread ID = 1 P1 Impl task Thread 0 Thread 1 Thread 2 Thread 3 S1 S2 EECB Thread ID = 2 P2 Impl task EECB Thread ID = 3 P3 Impl task EECB Thread ID = 3 Level = 2 S3 EECB Thread ID = 0 Level = 2 S0 #34

Lecture 4: OpenMP Open Multi-Processing

CS 4230: Parallel Programming Lecture 4: OpenMP Open Multi-Processing January 23, 2017 01/23/2017 CS4230 1 Outline OpenMP another approach for thread parallel programming Fork-Join execution model OpenMP