Efficient Programming for Multicore Processor Heterogeneity: OpenMP Versus OmpSs

Transcription

1 Efficient Programming for Multicore Processor Heterogeneity: OpenMP Versus OmpSs Anastasiia Butko, Lawrence Berkeley National Laboratory F. Bruguier, A. Gamatié, G Sassatelli, LIRMM/CNRS/UM

2 2 Heterogeneity: a gateway to lower power and higher energy efficiency Homogeneous architecture Binary compatibility A different binary for each architecture Heterogeneous architecture Homogeneous multicore Single-ISA, heterogeneous multi-core [Kumar MICRO 03] Multiple-ISA, heterogeneous multi-core + accelerators (FPGA, GPUs, etc.) Higher energy efficiency Easier programmability Heterogeneity

3 3 Heterogeneity: a gateway to lower power and higher energy efficiency Homogeneous architecture Binary compatibility A different binary for each architecture Heterogeneous architecture Homogeneous multicore Single-ISA, heterogeneous multi-core [Kumar MICRO 03] Multiple-ISA, heterogeneous multi-core + accelerators (FPGA, GPUs, etc.) Higher energy efficiency Easier programmability Heterogeneity

4 4 Single-ISA heterogeneous architecture: big.little technology µelj%&foxvwhu A15 A15 µ/,77/(%&foxvwhu A7 A7 A15 A15 A7 A7 L2 L2 Interconnect Memory

5 5 Programming models: OpenMP versus OmpSs STATIC DYNAMIC big core LITTLE core Execution time big core LITTLE core AB CD E F Execution time GUIDED big core LITTLE core ABD E F C G D S Execution time

6 6 Programming models: OpenMP versus OmpSs STATIC Task Dependency Graph DYNAMIC big core LITTLE core Execution time big core LITTLE core AB CD E F Execution time Critical task queue GUIDED big core LITTLE core ABD E F C G D S Execution time priority Non-Critical task queue priority

7 7 Motivation Example!"#$%&'()*&+,-./'0& :*$;<(#=&<(-*&>/*$#=?' '<%(*)?,.*=&'&+'<)<($0,.*=&'&+?*=)-($0,.*=&'&+#;(?*?0,-./'&+$+,/0 8!2 "## $%;=%&'()*&+,.*=&'0

8 8 Focus areas Q1: OpenMP vs OmpSs? Q2: Chunk size & Block size? Q3: Asymmetry in heterogeneous architectures?

9 9 Approach Odroid XU3 board Gem5/McPAT + McPAT Robust results Fast Not flexible Acceptable accuracy (validated big.little mode) Flexible Low similation speed

10 10 Experimental scenarios: real board workload

11 11 Experimental scenarios: real board workload opemp ompss

12 12 Experimental scenarios: real board workload opemp ompss chunk size = 1 2 n block size = 1 2 n

13 13 Experimental scenarios: real board workload opemp ompss chunk size = 1 2 n block size = 1 2 n l/l l/l l/l l/l l/l l/l l/h l/h l/h l/h l/h l/h h/l h/l h/l h/l h/l h/l h/h h/h h/h h/h h/h h/h m/m m/m m/m m/m m/m m/m

14 14 Board Results Performance & Energy trade-offs Ompss/openmp, frequency asymmetry, chunk/block size

15 15 Board Results OmpSs outperforms OpenMP in many cases OmpSs provides better performance OpenMP provides better energy

16 16 Board Results OmpSs is more sensitive to granularity than OpenMP Best configuration stays the same across different scenarios (per workload) Block size 1

17 17 Board Results OmpSs is more sensitive to granularity than OpenMP Best configuration stays the same across different scenarios (per workload) Size-dependent behavior differs across different workloads Block size 1

18 18 Board Results Best scenarios with balanced asymmetry Extreme frequency asymmetry is inefficient

19 (e) fluidanimate (Exynos 5 Octa board) 19 (d) cholesky (gem5/mcpat simulation) (c) cholesky (Exynos 5 Octa board) (a) blackscholes (Exynos 5 Octa board) freqmine blackscholes Board Results (g) freqmine (Exynos 5simulation) Octa board) (f) fluidanimate (gem5/mcpat (a) blackscholes (Exynos 5 Octa board) (b) blackscholes (gem5/mcpat (c) cholesky (Exynos 5 simulation) Octa board) lud cholesky (e) fluidanimate (Exynos 5 Octa board) (g) freqmine (Exynos 5 Octa board) (i) (gem5/mcpat lud (Exynos 5 Octa board) (h) freqmine simulation)

20 20 Experimental scenarios: gem5/mcpat workload opemp ompss Optimal chunk size Optimal block size symmetric HMP asymmetric HMP SMP symmetric HMP asymmetric HMP SMP

21 21 gem5/mcpat Results Performance & Energy trade-offs ) (h) freqmine (gem5/mcpat simulation) Ompss/openmp, cluster asymmetry

22 22 RNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 gem5/mcpat Results 4 ()&*"#%+,-#%.&-/0&*+,123 (,&'456&::%*%&,# pss environment is built on top of Mercurium compiler, 0<9 =<9 ch translates the OmpSs annotation clauses to source!"#$%&' =<9 1&;$ e and Nanos++ runtime system that manages task exe(,&'45-!&$;5-9'+1"*# (,&'45-!&$;5-9'+1"*# on. Nanos++ supports several task scheduling policies, Outliers: asymmetric HMP 7A7/1A &#'%* d) freqmine (gem5/mcpat simulation)!"#$%&' ch play a crucial role in efficient(h) program execution. It 825..&#'%* 05..&#'%* nes execution order and resource allocation for readyxecute tasks, i.e. tasks whose dependencies have been!"#$%&' 9&':+'.;,*&6&::%*%&,# sfied. The Criticality-Aware Task Scheduler (CATS) [35] (,&'456#+60+$"#%+,-/7+"$&23 amically detects the longest path of the task dependency ph using bottom-level longest-past priorities. The tasks, ch belong to the longest path, are determined as critical. Fig. 1: Execution time, Energy-to-Solution and Energy Delay re are two queues for ready tasks: (i) critical task queue Product projection. is intended to big cores and (ii) non-critical task queue is intended to LITTLE cores. Study of these programming alternatives is out of the kernel configuration and are related to the Adaptive Supply pe of this work. Here we use common OpenMP dynamic Voltage (ASV) technique used in Samsung SoCs. The operating temperature strongly depends on the eduling. Our work advances state-of-the-art by exploring erogeneous multicore architectures based on validated cluster architecture and application nature. For the Cortexformance and power models of ARM big.little ar- A7 cluster the temperature always remains below 323K ectures. Models are shown to have sufficient accuracy and the board fan stays off. For the Cortex-A15 cluster the mpared to an actual SoC and are further made freely temperature rises above 323K and the board fan is quickly triggered so as to ensure proper cooling. ilable. >?

23 23 RNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 gem5/mcpat Results ()&*"#%+,-#%.&-/0&*+,123 (,&'456&::%*%&,# mpss environment is built on top of Mercurium compiler, 0<9 =<9 ich translates the OmpSs annotation clauses to source!"#$%&' =<9 1&;$ de and Nanos++ runtime system that manages task exe(,&'45-!&$;5-9'+1"*# (,&'45-!&$;5-9'+1"*# ion. Nanos++ supports task scheduling policies, Need betterseveral heterogeneity management 825..&#'%*!"#$%&' ich play a crucial role in efficient program execution. It 825..&#'%* 05..&#'%* fines execution order and resource allocation for readyexecute tasks, i.e. tasks whose dependencies have been!"#$%&' 9&':+'.;,*&6&::%*%&,# isfied. The Criticality-Aware Task Scheduler (CATS) [35] (,&'456#+60+$"#%+,-/7+"$&23 namically detects the longest path of the task dependency ph using bottom-level longest-past priorities. The tasks, ich belong to the longest path, are determined as critical. Fig. 1: Execution time, Energy-to-Solution and Energy Dela ere are two queues for ready tasks: (i) critical task queue Product projection. t is intended to big cores and (ii) non-critical task queue t is intended to LITTLE cores. Study of these programming alternatives is out of the kernel configuration and are related to the Adaptive Suppl pe of this work. Here we use common OpenMP dynamic Voltage (ASV) technique used in Samsung SoCs. The operating temperature strongly depends on th eduling. Our work advances state-of-the-art by exploring erogeneous multicore architectures based on validated cluster architecture and application nature. For the Cortex formance and power models of ARM big.little ar- A7 cluster the temperature always remains below 323K tectures. Models are shown to have sufficient accuracy and the board fan stays off. For the Cortex-A15 cluster th mpared to an actual SoC and are further made freely temperature rises above 323K and the board fan is quickl triggered so as to ensure proper cooling. ailable. >?

24 24 (f) fluidanimate (gem5/mcpat simulation) (e) fluidanimate (Exynos 5 Octa board) gem5/mcpat Results (f) fluidanimate (gem5/mcpat simulation) freqmine blackscholes (e) fluidanimate (Exynos 5 Octa board) (h) freqmine (gem5/mcpat simulation) (b) blackscholes (gem5/mcpat simulation) (g) freqmine (Exynos 5 Octa board) (h) freqmine (gem5/mcpat simulation) lud cholesky (b) blackscholes (gem5/mcpat simulation) (g) freqmine (Exynos 5 Octa board) (i) lud (Exynos 5 Octa board) (d) cholesky (gem5/mcpat simulation) (j) lud (gem5/mcpat simulation)

25 25 Open Source architecture model and workloads: big.little model (ex5) available in gem5 source tree OmpSs/OpenMP precompiled executables & runtime environment