Simple DSL for Power-Performance Modeling*with Segmented Linear Models
1213,4Yuan HE , Yasutaka WADA , Guanqin PAN , and Masaaki KONDO
1Shenyang University of Technology, Liaoning, China

2Meisei University, Tokyo, Japan

3The University of Tokyo, Tokyo, Japan

4RIKEN, Kobe, Japan

Power Management for Power-Constrained HPC Systems➢ Effective Utilization of Limited Power BudgetcappingPower✓ to Further Scale the Performance of HPC Systems• HW Overprovisioning and Power Management

Power constraint➢ Power-Performance Optimization requires

NWlarge gap✓ User Effort to Modify Apps for Power Capping✓ Good Understanding of both SW/HW✓ Consideration of Various Systems and AppsMem.
P-CAS・Hetero・Locality・Low-power
technology

CPUGPUBase→ A Common Power-Performance Optimization Frameworkapp. AMax
load✓ to Allow the Reuse of Existing Optimization✓ to Automate the Power Management Processes

Max app. A app. B
loadapp. A app. BMax
load

Exa-scale w/ conventional

Peta-scale

Exa-scale w/ PCAS

The PomPP Framework for Convenient/Effective Power Management➢ Simple DSL as the Front-end✓ All Features Mentioned on the Left✓ to Describe Optimization Algorithms✓ to Specify Power-Performance Models• Linear Models• Segmented Models• Look-Up Tables➢ Hardware Configuration and Calibration➢ Automatic Instrumentation✓ to Set/Modify System Settings✓ to Add Power-Knob Control APIs to✓ to Calibrate Hardware for Variations inUser Applications
Manufacturing• with TAU/PDT based Tools➢ Common I/F to Control Power Knobs➢ Profiling and Optimization✓ to Provide Simple APIs to Control Power✓ for Stats Collection
Knobs✓ for Optimized Production Run

Specify system configurations,
given power budget,
power-performance models,applications to be run, etc.

Hardware
Calibration
Scripts

DSL
Sources

User
Application
SourceDSL Interpreter

Auto/Manual
Instrumentation

Insert our unified API calls forpower knob control and powerperformance profiling

Application
Profiling
Scripts

Profiling
Run

Application
withPower-KnobControl

Provide a scriptfor hardware calibration
to handle manufacturing
variabilityCalibration
Data

Profiling
Data

Optimized
Execution
Scripts

Optimization
ModelsPossible to use
the same binaryfor both profiling run
and optimized run

PowerPerformance
Optimized Run

Case Study with Simple/Segmented Linear Models
BTCG

1.2
10.8
0.6
0.4
0.2
04060801001201404060801001201406080

406080LU

100

120

Segmented models
are exceedingly
more accurate thanlinear models inobtaining thepower caps for the2 presetperformance
demands

140

100

120

40

14060

80

100

1201404060

80

100120

140

40

0.4

0.3

0.3

0.2

0.2

0.1

0.1

00
FT

120

140

ISBenchmarks

LU

80

100

120

140

MG

Segmented Model

Errors with Estimated Power Caps for
Relative Performance of 80%

0.4

EP60

Linear Model

Errors with Estimated Power Caps for
Relative Performance of 50%

CG100

1.2
10.8
0.6
0.4
0.2
0Power Consumption

BT80

SP

1.2
10.8
0.6
0.40.204060

MG

1.2
10.8
0.6
0.4
0.2
040

1.2
10.8
0.6
0.4
0.2
01.2
10.8
0.6
0.4
0.2
0IS
1.2
10.8
0.6
0.4
0.2
0FTEP

1.2
10.8
0.6
0.4
0.2
0Errors

Relative Performance➢ Evaluated on a System with 16 Cores✓ Intel Xeon E5-2640 v2 per Socket✓ RAPL as the Power Knob
➢ Assume Linear and Segmented
Linear Relationships between Powerand Performance✓ Generated from Profiling Results✓ 2 Profiling Runs per Application
for the Simple Linear Models✓ 10 Profiling Runs per Application
for the Segmented Linear Models➢ 2 User-Set Performance Demands✓ 50% and 80%, respectively✓ Power Capping Values for These
Performance Demands are
Calculated with the Models✓ Evaluated with Applications fromthe NPB Suite✓ Segmented Models Works Betterin all 16 Cases

Performance/Power Models of NPB Applications

SPBTCGEP

Linear ModelFTISLU

MGSP

Segmented Model* This work was supported in part by JSPS KAKENHI under Grant Number 17K12665 and by the Natural Science Foundation of Liaoning Province under Grant Number 20180550194.