Green: A Framework for Supporting Energy-Conscious Programming using Controlled Approximation

Woongki Baek Stanford UniversityTrishul M. Chilimbi Microsoft Research PLDI 2010

General Problem

Tradeoff between Quality of Service (QoS) and Performance + Energy Consumption

Why is it critical? Datacenters: Amazon, Google, Microsoft Existence of acceptable domains: Machine Learning,

Image/Video Processing Programmers do this anyway, but in an ad-hoc manner

and no QoS guarantees Green Framework was built to address these issues

Green Framework Overview

Static and dynamic calibration QoS Model Language Extension

Functionality Input provided by programmer:

QoS loss (maximum acceptable) i.e. 2% QoS computation function through language extension Optionally:

approximate version(s) of a function or a loop calibration mechanism

Output by Green system: New version of a program that is tuned to be more

efficient (both performance and power) QoS guarantees to be in the acceptable range Dynamic re-calibration (adaptation) if needed at run-time

Overview

General Problem and Functionality System Design Green Implementation Benchmarks Evaluation Discussion

Green Framework Design

QoS Service Level Agreement (SLA) QoS_Compute QoS_Approx and QoS-ReCalibrate

Green Mechanisms

Loop Approximation

To be replaced

QoS_Compute Used in the Calibration

Phase

QoS_Lp_Approx

QoS Model for Loops

Calibration data goes to MATLAB program Automatically selects appropriate

approximation level Provides 2 interfaces:

Function Approximation

Approximation Modeling

For each function and loop individually Then extensive search space exploration to

combine them and still fulfill QoS SLA Global recalibration based on

QoS_Loss/Performance_Gain Exponential backoff scheme to avoid non-

linear effects if any

Experiments

Benchmarks: Bing Search Graphics: 252.eon (SPEC2000) Machine Learning: Cluster GA Signal Processing: Discrete Fourier Transform Finance: Blackscholes (PARSEC)

Results with Bing Search - Performance

Results with Bing Search - QoS

Results with 252.eon - Performance

Results with 252.eon - QoS

Results with 252.eon – Model Sensitivity

Results with DFT – Performance And Energy

Results with DFT – QoS

Discussion

Advantages: Performance and power efficiency

improvements Design flexibility Automatic QoS modeling Fine granularity: function and loop based

Discussion

Disadvantages: Limited scope of application: sin, cos, log, exp Complexity for programmer: QoS_Compute,

approx_loop extensions Semi-automatical Limited QoS approach Only numerical data as input, no structures in

QoS modelling