Performance & Energy

Transcription

1 1 Performance & Energy Md Abdullah Shahneous Bari Abid M. Malik Millad Ghane Ahmad Qawasmeh Barbara M. Chapman 11/28/15

2 2 Layout of the talk Ø Overview Ø Motivation Ø Factors that affect the performance & Energy Optimization Ø Experimental Results Ø Conclusion & Future Work

3 3 OpenMP Ø De-facto standard for shared memory parallel programming Ø Thread based parallelism Ø Mainly two kinds of parallelism Ø Regular parallelism (work sharing constructs) Ø Irregular parallelism (task based constructs)

4 4 Main Barrier Towards Exascale Computing Ø Power, power and power Ø 20MW power limit for exascale machines (DOE) Ø Usually processor vendors concern Ø But to reach the exascale limit software stack have to chip in Ø Any solution????

5 5 Power Constrained Computing (Overprovisioning) Ø Usually not all application use maximum node power all the time Ø Capping the power at lower limit Ø Allows extra node to be added at the similar power budget Extra Node Extra Compute Power

6 6 Power Constrained Computing(Contd.) Ø More focus on overall system level performance Ø Some related work, Ø Sarood et al. [1] Ø Patki et al. [2] Ø Rountree et al. [3] 1. Sarood, Osman, et al. "Op?mizing power alloca?on to CPU and memory subsystems in overprovisioned HPC systems." Cluster Compu,ng (CLUSTER), 2013 IEEE Interna,onal Conference on. IEEE, Patki, Tapasya, et al. "Exploring hardware overprovisioning in power-constrained, high performance compu?ng." Proceedings of the 27th interna,onal ACM conference on Interna,onal conference on supercompu,ng. ACM, Rountree, Barry, et al. "Beyond DVFS: A first look at performance under a hardware-enforced power bound." Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th Interna,onal. IEEE, 2012.

7 7 Why OpenMP??? Ø Current Issue: Less focus on per-node performance Ø Challenge: To reach the peak throughput, per-node performance must be improved Ø OpenMP is the most popular language of choice for intra node parallelism

8 8 Factors That Impact Work Sharing Parallelism Ø How many workers are working? ~ Thread Ø How the work is scheduled? ~ Scheduling Policy Ø How much work they are given at one time? ~ Chunk Size Ø How the data is laid out for the workers? ~ Thread Affinity Ø What do the workers do during their break? ~ Wait Policy

9 9 Experimental Details Ø Selected parameters Ø No. Of threads (2, 4, 8, 16, 24, 32) Ø Scheduling policy (STATIC, DYNAMIC, GUIDED) Ø Chunk size(1, 8, 32, 64, 128, 256, 512) Ø Wait policy (active, passive) Ø Thread affinity (OMP_PLACES + OMP_PROC_BIND) Ø Power cap levels Ø (55, 70, 85, 100, 115)w Ø Used technology: Ø Intel RAPL (for power capping & energy measurement) Ø OMPT for kernel level measurement Ø Benchmark ~ NPB

10 10 Performance improvement using the best configuration compared to default across all kernels % Performance Improvement CG_conj_grad_1 CG_main_1 CG_main_2 CG_main_3 CG_main_4 CG_main_5 CG_main_6 EP_main_3 FT_c_s1_1 FT_c_s3_1 FT_c_ts2_1 *FT_c_i_1 **FT_c_i_c_1 FT_evolve_1 FT_init_ui_1 IS_alloc_key_bu IS_create_seq_1 LU_erhs_1 LU_setbv_1 LU_se?v_1 MG_zero3_1 MG_zran3_1 MG_zran3_2 MG_zran3_3 SP_add_1 SP_compute_rhs SP_error_norm_ SP_exact_rhs_1 SP_ini?alize_1 SP_ninvr_1 SP_pinvr_1 SP_rhs_norm_1 SP_txinvr_1 SP_tzetar_1 SP_x_solve_1 SP_y_solve_1 SP_z_solve_1 UA_geom1_2 UA_mortar_3 UA_move_1 Kernels 55W 70W 85W 100W 115W

11 11 Energy consumption improvement using the best configuration compared to default across all kernels %Energy Improvement CG_conj_grad_1 CG_main_1 CG_main_2 CG_main_3 CG_main_4 CG_main_5 CG_main_6 EP_main_3 FT_c_s1_1 FT_c_s3_1 FT_c_ts2_1 *FT_c_i_1 **FT_c_i_c_1 FT_evolve_1 FT_init_ui_1 IS_alloc_key_buff_1 IS_create_seq_1 LU_erhs_1 LU_setbv_1 LU_se?v_1 MG_zero3_1 MG_zran3_1 55W 70W 85W 100W 115W Kernels MG_zran3_2 MG_zran3_3 SP_add_1 SP_compute_rhs_1 SP_error_norm_1 SP_exact_rhs_1 SP_ini?alize_1 SP_ninvr_1 SP_pinvr_1 SP_rhs_norm_1 SP_txinvr_1 SP_tzetar_1 SP_x_solve_1 SP_y_solve_1 SP_z_solve_1 UA_geom1_2 UA_mortar_3 UA_move_1

12 Execution time comparison among different configurations (an LU kernel) Best Configura?on Default Configura?on Default Configura?on Without Power Cap Execu?on Time (Sec) W, 32, STATIC, 1 32, STATIC, 1 32, DYNAMIC, 8 115W, 32, STATIC, 1 32, STATIC, 1 24, GUIDED, 8 115W, 32, STATIC, 1 32, STATIC, 1 24, GUIDED, 8 115W, 32, STATIC, 1 32, STATIC, 1 24, DYNAMIC, 8 115W, 32, STATIC, 1 32, STATIC, 1 24, GUIDED, 8 55W 70W 85W 100W 115W Different Power Cap Levels

13 13 These results are based on STREAM benchmark. Data Size X means the array size for STREAM benchmark is 19,200,000*X. OpenMP ICVs on DRAM Power Ø Developing a model for power consumption of openmp applications Power (W) Power (W) Power (W) Data Size Courtesy: Millad Ghane Data Size Data Size

14 14 UTS FloorPlan Courtesy: Ahmad Qawasmeh Impact of threads & scheduling policy in task based parallelism

15 15 Ø Dynamic adaptation (APEX), Ø Active harmony Ø Modeling Ongoing Work Ø Across different software stack (OpenMP runtime), Ø Openuh Ø GCC Ø Intel Ø Across different hardware architecture Ø Intel sandybridge Ø IBM power8

16 16 Future Work Ø More concrete configuration selection Ø DRAM capping Ø Fine grain (core level) control Ø Other energy efficient techniques, Ø DVFS, frequency modulation etc. Ø Combining it with a inter-node (MPI) programming models for hybrid applications

17 17 Summary Ø Overview Ø Motivation Ø Factors that affect the performance & Energy Optimization Ø Experimental Results Ø Conclusion & Future Work