Using Cowichan Problems to Inves6gate Programmability of X10 Programming System

Transcription

1 Using Cowichan Problems to Inves6gate Programmability of X1 Programming System Jeeva S. Paudel, J. Nelson Amaral Department of Computing Science University of Alberta, Edmonton Canada June 4, 211

2 X1: Design Goals Programmer Produc-vity An experiment to evaluate the programmability of three programming systems. (Sarkar et al., Workshop on Produc-vity & Performance in High- End Compu-ng 26) Code Performance s Performance evalua:on of MPI, UPC & OpenMP on mul:core architectures. (Mallon et al., EuroPVM/MPI 29) 2

3 Contribu-ons Performance assessment of X1 Implementa-on of a suite of parallel programs 3

4 Cowichan Suite Gregory Wilson Salishan - - Cowichan 4

5 Cowichan Suite Designed to assess the programmability of a programming system for wri-ng efficient parallel programs 5

6 Cowichan Problems

7 Example Implementa-ons Turing Ring r x,y : birth rates c x,y : death rates μ x,y : migra-on rates 7

8 Example Implementa-ons Turing Ring r x,y : birth rates c x,y : death rates μ x,y : migra-on rates Pred (X) Prey (Y) Sequen-al Implementa-on of Turing Ring Problem 8

9 Example Implementa-ons Turing Ring r x,y : birth rates c x,y : death rates μ x,y : migra-on rates - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent loca-ons Pred (X) Prey (Y) Sequen-al Implementa-on of Turing Ring Problem 9

10 Example Implementa-ons Turing Ring r x,y : birth rates c x,y : death rates μ x,y : migra-on rates Pred (X) Prey (Y) Place Place 3 Place 1 Place 4 Place 2 Place 5 CPU CPU 1 CPU 2 Paralleliza-on of Turing Ring Problem 1

11 Example Implementa-ons Turing Ring r x,y : birth rates c x,y : death rates μ x,y : migra-on rates - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent node - Wait for other process to finish this itera-on Pred (X) Prey (Y) Place Place 3 Place 1 Place 4 Place 2 Place 5 CPU CPU 1 CPU 2 Paralleliza-on of Turing Ring Problem 11

12 Example Implementa-ons Turing Ring - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent node - Wait for other process to finish this itera-on 12

13 Example Implementa-ons Turing Ring - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent node - Wait for other process to finish this itera-on 13

14 Example Implementa-ons Turing Ring - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent node - Wait for other process to finish this itera-on 14

15 Example Implementa-ons Turing Ring - Receive migra-on informa-on - Update local popula-on using birth and death rates - Update local popula-on using migra-on values - Send migra-on informa-on to adjacent node - Wait for other process to finish this itera-on 15

16 Example Implementa-ons Skyline Matrix N X N

17 Example Implementa-ons Skyline Matrix N X N

18 Example Implementa-ons Skyline Matrix Ax = b N X N

19 Example Implementa-ons Skyline Matrix Ax = b LU Decomposi-on

20 Example Implementa-ons Skyline Matrix Supra- diagonal Columns A Sub- diagonal Rows

21 Example Implementa-ons Skyline Matrix Supra- diagonal Columns A Sub- diagonal Rows

22 Example Implementa-ons Skyline Matrix Supra- diagonal Columns A Sub- diagonal Rows

23 Example Implementa-ons Skyline Matrix

24 Example Implementa-ons Skyline Matrix

25 Example Implementa-ons Skyline Matrix

26 Example Implementa-ons Skyline Matrix

27 Example Implementa-ons Skyline Matrix

28 Example Implementa-ons Skyline Matrix A Data Distribu-on for Parallel Solu-on

29 Example Implementa-ons Skyline Matrix A Data Distribu-on for Parallel Solu-on

30 Example Implementa-ons Skyline Matrix Need for Data Replica-on asynccopy( ) and copy( )

31 Experimental Setup Server with 16 nodes, each featuring two Quad- Core AMD Opteron processors X1c++ backend More mature and beher op-miza-ons pgas_sockets Supports mul-ple places pbs_resources To control number of cores, and processors used 31

32 Experimental Methodology Unix -me and system wall -me report real -me 15 account for variances 95% range of values that is likely to include differences from variable factors 32

33 Performance Reports Turing Ring 33

34 Performance Reports Turing Ring Sequen-al Execu-on Time (ms) Number of Loca-ons 34

35 Performance Reports Turing Ring Sequen-al Execu-on Time (ms) Number of Loca-ons 35

36 Performance Reports Turing Ring Sequen-al Execu-on Time (ms) Number of Loca-ons 36

37 Performance Reports Sequen-al Turing Ring Execu-on Time (ms) Parallel Number of Loca-ons Execu-on Time (ms) Number of Places 37

38 Performance Reports Sequen-al Turing Ring Execu-on Time (ms) Parallel Number of Loca-ons Execu-on Time (ms) Number of Places 38

39 Performance Reports Sequen-al Turing Ring Execu-on Time (ms) Parallel Number of Loca-ons Execu-on Time (ms) Number of Places 39

40 Performance Reports Sequen-al Turing Ring Execu-on Time (ms) Parallel Number of Loca-ons For 1 loca-ons & 4 places speedup = 3.6 Execu-on Time (ms) Number of Places 4

41 Performance Reports Turing Ring Sequen-al Execu-on Time (ms) Parallel Execu-on Time (ms) Number of Loca-ons Number of Places Op-mal number of places >= 256 & <

42 Performance Reports Sequen-al 3 Skyline Matrix Execu-on Time (ms) Matrix Chain Length 42

43 Performance Reports Sequen-al 3 Skyline Matrix Execu-on Time (ms) Matrix Chain Length Execu-on Time (ms) Number of Places 43

44 Performance Reports Sequen-al 3 Skyline Matrix Execu-on Time (ms) Matrix Chain Length For chain length 5 & 64 places, speedup = 11.2 Execu-on Time (ms) Number of Places 44

45 Performance Reports Sequen-al 3 Skyline Matrix Execu-on Time (ms) Execu-on Time (ms) Matrix Chain Length Op-mal number of places >= 64 & < Number of Places 45

46 Insights Expressiveness: Data representa-on & distribu-on Task and data parallelism Aggregate opera-ons Language Deficiencies: Fine- grained async - - poor performance Concurrent Input/Output - - useful Error Messages - - cryp-c 46

47 Comparison with Other Languages X1 and UPC: - both exploit data layout in memory (PGAS) - X1 richer through async ac-vi-es (APGAS) - expressive data and task parallelism stripped alloca-on of arrays collec-ve opera-ons - remote shared memory accesses - compiler technology 47

48 Comparison with Other Languages X1 and OpenMP: OpenMP restricted to shared memory systems lacks efficient data locality support scalability issue 48

49 Comparison with Other Languages X1 and Orca: Orca uses shared objects for communica-on shared objects encapsulated in abstract data types X1 relies on APGAS model and uses messages only immutable values are shared 49

50 Summary As an emerging programming model, performance analyses necessary Measured programmability of X1 language performance of X1 code Beher than UPC, OpenMP because of APGAS memory model Run-me performance improvements needed Addi-onal language constructs useful 5

51 Ques-ons? 51