A unified Energy Footprint for Simulation Software
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1 A unified Energy Footprint for Simulation Software Hartwig Anzt, Armen Beglarian, Suren Chilingaryan, Andrew Ferrone, Vincent Heuveline, Andreas Kopmann Hartwig Anzt September 12, 212 ENGINEERING MATHEMATICS AND COMPUTING LAB (EMCL) KIT University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association
2 Green Computing Hardware developers aim for low power devices Integration of low-consuming coprocessors (GPUs, Cell, MIC, FPGAs... ) Green5 as counterpart to performance-oriented TOP5 September 12, 212 2/2
3 Green Computing 25 TOP 1 TOP 1 Sequoia - BlueGene/Q Power BQC 16C 2 MFlops/Watt 15 1 K computer SPARC64 VIIIfx China Tianhe-1A X567, NVIDIA GPU 5 Roadrunner BladeCenter QS22/LS21 PowerXCell 8i Jaguar - Cray XT5-HE Opteron September 12, 212 3/2
4 Green Computing 25 TOP 1 TOP 1 GREEN 1 GREEN 1 2 Sequoia - BlueGene/Q Power BQC 16C MFlops/Watt 15 1 K computer SPARC64 VIIIfx China Tianhe-1A X567, NVIDIA GPU 5 Roadrunner BladeCenter QS22/LS21 PowerXCell 8i Jaguar - Cray XT5-HE Opteron September 12, 212 4/2
5 Green Computing 25 TOP 1 TOP 1 GREEN 1 GREEN 1 2 accelerated (%) Sequoia - BlueGene/Q Power BQC 16C MFlops/Watt K computer SPARC64 VIIIfx China Tianhe-1A X567, NVIDIA GPU Roadrunner BladeCenter QS22/LS21 PowerXCell 8i Jaguar - Cray XT5-HE Opteron September 12, 212 5/2
6 Green Computing Increasing focus on power & energy (e.g. Flops/Watt in Green5) Theoretical ratio usually irrelevant Challenge to transfer the power efficiency into the simulation Energy need in scientific computing depends on software implementation Compare energy & power for different simulation software September 12, 212 6/2
7 Green Computing Unified Energy Footprint Introduce unified energy-footprint for simulation software Information about hardware configuration, typical application, power & energy draft and scalability Compress information like on Autoquartett card Easy comparison for different software packages September 12, 212 7/2
8 Power measurement Supermicro X8DTG-QF GPU-workstation 2 Intel XEON (QPI-connected), 192 GB memory 4 Fermi C27 (PCI-Express 16 ) Independent, embedded measurement setup Powermeters monitor voltages and currents in lines powering chipset, the hard- disks and the GPUs (including PCI) 25 k Samples/second Motivation Power Measurement Energy Footprint COMSO Energy Footprint PyHST-CPU Energy Footprint PyHST-GPU September 12, 212 Conclusion 8/2
9 Hardware evaluation: CPU coreburn coreburn linear slope 1-6 cores linear slope 6-12 cores linear slope 1-12 cores 17 power [W] Number of active Cores Evaluate power dissipation of active CPU cores almost linear increase ( 6.5 Watts/core) Pattern when activating cores in 2nd CPU September 12, 212 9/2
10 Energy Footprint: COSMO COSMO Model (version 4.21) Numerical weather simulation In operational use by DWD, MeteoSwiss gridpoints resolution of.12 and 4 s timestep Configuration: Energy summary: System: CPU: Accelerator: Application: Resources: Supermicro X8DTG-QF 2 Intel XEON E554@2.53GHz 4 Nvidia C27 24 h forecast 6 cores total runtime : avg. power chipset : avg. power HDD : avg. power GPUs : total Energy : no GPUs used s W W. W Wh September 12, 212 1/2
11 Energy Footprint: COSMO Model 2 chipset HDD1 HDD total energy consumption net energy consumption power [W] 15 1 energy [Wh] time [s] (a) Power profile 2 chipset HDD1 HDD2 GPU1* GPU2* GPU3* GPU4* (b) Energy consumption Characteristic power draft due to communication Very compute-intensive September 12, /2
12 Energy Footprint: COSMO Model energy [Wh] application energy energy idle energy coreburn energy model runtime [1 s] / energy [Wh] runtime total energy # cores (c) Scaling wrt. hardware simulated forecast [h] (d) Scaling wrt. simulation parameters Excellent scaling wrt. core-numbers GPU implementations necessary for higher energy efficiency September 12, /2
13 COSMO s scorecard compress information on scorecard Hardware & software configuration Compact summary of power & energy analysis for easy comparison September 12, /2
14 Energy Footprint: PyHST CPU-version PyHST CPU-version X-ray tomography reconstruction (3D) Filtered Back Projection (FBP) Using CPU for reconstruction Configuration: Energy summary: System: CPU: Accelerator: Application: Resources: Supermicro X8DTG-QF 2 Intel XEON E554@2.53GHz 4 Nvidia C27 4 frames (1776x177) 6 cores total runtime : avg. power chipset : avg. power HDD : avg. power GPUs : total Energy : no GPUs used s W W. W Wh September 12, /2
15 Energy Footprint: PyHST CPU-version 2 chipset HDD1 HDD2 25 total energy consumption net energy consumption 2 15 power [W] 1 energy [Wh] time [s] (e) Power profile chipset HDD1 HDD2 GPU1* GPU2* GPU3* GPU4* (f) Energy consumption I/O should be handled asynchronously to computation September 12, /2
16 Energy Footprint: PyHST CPU-version energy [Wh] application energy energy idle energy coreburn energy model # cores (g) Scaling wrt. hardware runtime [1 s] / energy [Wh] runtime total energy # processed frames (h) Scaling wrt. simulation parameters Poor core-scaling due to sequential I/O Linear runtime & energy increase for higher frame counts September 12, /2
17 Energy Footprint: PyHST GPU-version PyHST GPU-version X-ray tomography reconstruction (3D) Filtered Back Projection (FBP) Using GPU / multiple GPUs for reconstruction Configuration: Energy summary: System: CPU: Accelerator: Application: Resources: Supermicro X8DTG-QF 2 Intel XEON E554@2.53GHz 4 Nvidia C27 4 frames (1776x177) 2 cores + 2 GPUs total runtime : avg. power chipset : avg. power HDD : avg. power GPUs : total Energy : 2 GPUs used s W W W 5.9 Wh September 12, /2
18 Energy Footprint: PyHST GPU-version 2 15 chipset HDD1 HDD2 GPU1 GPU total energy consumption net energy consumption power [W] 1 energy [Wh] time [s] (i) Power profile -.5 chipset HDD1 HDD2 GPU1 GPU2 GPU3* GPU4* (j) Energy consumption runtime for I/O phase exceeds reconstruction phase September 12, /2
19 Energy Footprint: PyHST GPU-version energy [Wh] application energy energy idle energy coreburn energy model runtime [1 s] / energy [Wh] runtime total energy # GPUs (k) Scaling wrt. hardware # processed frames (l) Scaling wrt. simulation parameters No gain for multiple GPUs (CPU I/O, additional power dissipation) Initialization overhead for small frame counts September 12, /2
20 Summary & Outlook Information about power & energy draft and scaling simplifies comparison of software implementations identification of energy bottlenecks & optimization decisions when acquiring hardware Unified Energy Footprint Standard for creating a comprehensive database for software implementations. September 12, 212 2/2
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