Vol. 46, No. SIG 12(ACS 11), pp , August c MegaScript, MegaScript MegaScript MegaScript MegaScript Construction of Accurate Task
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1 Vol. 46, No. SIG 12(ACS 11), pp , August c MegaScript, MegaScript MegaScript MegaScript MegaScript Construction of Accurate Task Models for the MegaScript Task Parallel Language Hiroshi Yuyama,, Tomoaki Tsumura and Hiroshi Nakashima We are pursuing research works on our task-parallel script language named MegaScript for general purpose mega-scale computing. A feature of MegaScript is the capability to describe abstructed behavior of tasks in a program form named meta-program. The model derived from the meta-program is sufficiently accurate for sequential or well-balanced parallel programs. However, our experience showed that a large modeling error occured in ill-balanced and/or communication-bound parallel programs. This paper proposes a solution for the accuracy problem by introducing a profiling scheme into MegaScript meta-program. Our evaluation exhibited the profiling greatly improves model accuracy for the parallel programs with the accuracy problem. 1. Tera-FLOPS Ruby Mega- Peta-FLOPS Script MegaScript Peta-FLOPS 100 Toyohashi University of Technology Presently with Fujitsu System Solutions, Ltd.
2 2 1 MegaScript Fig. 1 Outline of MegaScript system. 1 behavior MegaScript 2. MegaScript MegaScript 2.1 SPMD l m n x l = Cost.new("x") m = Cost("x") MegaScript 1) n = C_.x MegaScript n MegaScript 2.3 MegaScript MegaScript MegaScript 1 Task compute() initialize behavior compute(cost) cost
3 MegaScript input() output() input(cost) / output(cost) cost Ruby FOR IF PARALLEL FOR IF PARALLEL 3. MegaScript msgsend() msgrecv() msgsend(cost) / msgrecv(cost) MegaScript cost 2.4 MegaScript 3.1 MegaScript MegaScript MegaScript behavior MegaScript Mega- Script 3
4 4 2 Fig. 2 An example of meta-program and the model generation script derived from it. 01: SIZE 16: FOR k IN 0..procs 02: $maxloop = : compute(2) 03: DEF mandel_color() 18: END 04: compute(2) 19: FOR s..e 20: FOR SIZE 05: FOR 0..$maxloop 21: mandel_color() 06: compute(4) 22: END 07: IF : END 08: BREAK 24: IF ID!=0 09: END 25: msgsend(size) 10: END 26: ELSE 11: END 27: FOR 1..procs 12: procs =16 28: msgrecv(size) 13: chunk = SIZE /procs 29: END 14: PARALLEL procs DO 30: END 15: s=0; e=chunk 31: END 2 MegaScript 3 Mandelbrot Fig. 3 Meta-program for Mandelbrot set problem n MegaScript comp_cost(n) N N init n 16 Mobile Intel Pentium III-M 866MHz 512MB GigaBit Ethernet PC n+1 FOR IF OS Red Hat Linux 7.2 IF 0.8 compute(50) 0.2 output(1) compute(n) n init + FOR n + (n + 2 1) ( (1 0.8) 0) output % N = Table 1 Methods for cost approximation. ( ) comp cost strmcomm cost strmin cost strmout cost strmcomm count msgcomm cost msgsend cost msgrecv cost msgcomm count (compute) 3.3 stream (input+output) stream (input) stream (output) stream message (msgsend+msgrecv) message (msgsend) message (msgrecv) message Mandelbrot N 3 1 MegaScript 71.9%
5 5 Table 2 2 Mandelbrot Modeling error of Mandelbrot set problem. N = 2048 N = 4096 N = 8192 N = [sec] [%] [sec] [%] [sec] [%] [sec] [%] API 4. Omni 3) Java tool kit 4.1 MegaScript API API 3 MegaScript gettime.how(func[(opt[,...])]) func how opt blockcount=b b getvalueofvariable.how(func[(opt[,...])]) func blockcount=b
6 6 3 API Table 3 Examples of API for instrumentation. API gettime.averageof(func(blockcount=2)) getvalueofvariable.maxof(func(variablename=i) getamountofmessage.of(func(blockcount=2, position=after(1))) b variablename=v v blockcount = 1 position=p b before(p) after(p) p getamountofmessage.how(func[(opt[,...])]) func how opt blockcount=b blockcount (bc) b position=p blockname (bn) p for while do blockcount position (pt) how position=n position=after(n) of variablename (vn) API averageof maxof API ( 1 ) API minof API ( 3 ) API headof ( 5 ) / tailof headof position=before(n) 4 ( 2 ) Omni ( 4 ) API 5 6 gettime
7 7 6 gettime Fig. 6 Example of instrumented task program with gettime. 4 Fig. 4 Flow of instrumentation. 01: x=gettime.of(main(blockcount=1)) 02: FOR x 03: compute(20) 04: END 05: FOR : y=getvalueofvariable.averageof( getvalueofvariable 07: func_2(blockcount=3) Fig. 7 Example of instrumented task program with 08: <-func_1(position=1) getvalueofvariable. 09: <-main(blockcount=1) 10: ) 11: compute(y) 12: IF getvalueof- 13: compute(x) 14: ELSE Variable getvalueofvariable 15: BREAK; 16: END averageof 17: END func_2 5 Fig. 5 Example of meta-program with instrumentation instructions. blockcount=3 7 getvalueofvariable 3 2 for <- func 2 func 1 func 1 main 1 5 gettime gettime main of 1 func 1 main func 2 func 2 blockcount=1 7 global 1 bit vector main main func 1 on MPI MPI Wtime func 2
8 8 Table 4 4 Evaluation environment. 16 nodes CPU Mobile Intel Pentium III-M 866MHz Memory 512 MB RAM Network Gigabit Ethernet 1000-BASE, 13port SW:Fujitsu PG-SW101 2, 28port SW:CISCO catalyst OS Red Hat Linux release : procs =16 08: compute(3) 16: compute(5) 02: FOR : FOR : IF : compute(1) 10: compute(3) 18: compute(10) 04: END 11: IF : END 05: compute(4) 12: compute(10) 20: END 13: END 21: END 06: np=4096/procs 14: END 22: compute(30) 07: FOR k IN 0..np 15: FOR EP Fig. 8 Meta-program for EP without profiling. averageof 05: blockcount=2)) 21: blockcount=5, : compute(3) 9 EP Fig. 9 Meta-program for EP with profiling. NAS Parallel Benchmarks EP Mandelbrot 5.3 NAS Parallel Benchmarks EP NAS Parallel Benchmarks EP EP EP EP gcc version MPICH with p4 library Ruby Omni Compiler version : procs =16 02: x=getvalueofvariable.of( 15: 16: IF 0.5 compute(10) 03: main(blockname=for, 17: END 04: variablename=i, 18: END 19: x=getvalueofvariable.of( 06: FOR 0..x 20: main(blockname=for, 07: compute(1) 22: variablename=i 1 08: END 23: ) 09: compute(4) 24: ) 10: np=4096/procs 25: FOR 0..x 11: FOR k IN 0..np 26: compute(5) 12: compute(3) 27: IF : FOR : compute(10) 29: END 30: END 31: END 32: compute(30) % %
9 Table 6 6 Mandelbrot (profile) Modeling error of Mandelbrot set problem with profile. N = 2048 N = 4096 N = 8192 N = [sec] [%] [sec] [%] [sec] [%] [sec] [%] EP Table 5 Modeling error of EP. no profile profile [sec] [%] [sec] [%] : $procs=16 02: $local_n 03: DEF step(myid) 04: IF myid!= 0 05: msgsend(1) 06: msgrecv(1) 07: END 08: IF myid!= $procs-1 09: msgsend(1) 10: msgrecv(1) 11: END 12: END 13: PARALLEL $procs DO 14: 15: IF ID==0 16: compute(1) 17: END 18: IF ID== $procs-1 19: compute(1) 20: END 21: step(id) 22: FOR $local_n +2 23: compute(1) 24: END 25: END 26: IF ID==0 27: output(1) 28: END 29: END 11 Fig. 11 Meta-program for wave equation problem without profiling. 10 Mandelbrot 16 N = 2048 Fig. 10 Meta-program for Mandelbrot set problem with profiling. 8.47% Mandelbrot Mandelbrot 3.3 Mandelbrot % T N = T = N N N 11 compute() N = N 12
10 10 01: $procs=16 15: PARALLEL $procs DO 02: $local_n 16: 03: DEF step(myid) 17: IF ID==0 04: IF myid!= 0 18: compute(1) 05: x=getamountofmessage.of( 19: END step(position=1)) 20: IF ID== $procs-1 06: msgsend(x) 21: compute(1) 22: END 07: msgrecv(1) 23: step(id) 08: END 24: FOR $local_n +2 09: IF myid!= $procs-1 25: compute(1) 10: y=getamountmessage.of( 26: END step(position=2)) 27: END 11: msgsend(y) 28: IF ID==0 12: msgrecv(1) 29: output(1) 13: END 30: END 14: END 31: END 12 Fig. 12 Meta-program for wave equation problem with profiling. 13 EP Fig. 13 Simplified meta-program for EP. Table 7 7 Modeling error of wave equation problem. no profile profile [sec] [%] [sec] [%] Table 8 Profiling overhead. EP Mandelbrot (N = 4096) (1.16) 0.02 (0.04) 0.01 (0.09) (0.35) 0.01 (0.04) 0.01 (0.10) (0.26) 0.01 (0.04) 0.01 (0.13) 13 EP (0.24) 0.02 (0.13) (0.07) 14 Mandelbrot (0.08) (0.04) (0.10) 13 9 % 1.3% C M N = 4096 αc + βm α β Fig. 14 Mandelbrot Simplified meta-program for Mandelbrot set problem. 0.13% % % 1.0% MegaScript
11 9 Table 9 Execution time and modeling error in heterogeneous environment. [sec] [%] EP Mandelbrot Mandelbrot 4 4 Pentium III 1GHz 256MB 4 Gi- gabit Ethernet 8 EP Mandel- brot N = EP 2 Mandelbrot 6% /2 1/3 16 MegaScript 1 23% EP Mandelbrot 1 16 N = % EP OS /14
12 12 6. Man- delbrot 2 SCALEA 5) SCALEA Mandelbrot FortranMPI OpenMP 2 Hybrid HPF++ MegaScript 2 SCALEA GUI NAS Parallel Benchmarks EP Mandelbrot MegaScript EP 2.8% 1.5% Mandelbrot 71.9% 8.5% 71.9% 22.9% ( 1 ) ( 2 ) 7.2 ( 3 ) MegaScript
13 2004 1) MegaScript SACSIS2003, pp (2003). 2) Ruby ASCII (1999). 3) Omni OpenMP 2004 Vol.42, No.4, pp (2001). 4) MegaScript SACSIS 2004, pp (2004). 5) Truong, H-T., Fahringer, T., Madsen, G., Malony, A. D., Moritsch, H., and Shende, S.: On Using SCALEA for Performance Analysis of Distributed and Parallel Programs. Supercomputing 2001 (2001) ( ) ( ) IEEE-CS ACM ALP TUG
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