Evaluating Orthogonalitybetween Application Auto tuning ...

Evaluating Orthogonality between Application Auto‐tuning and Run‐Time Resource Management for Adaptive OpenCL ApplicationsEdoardo Paone, Davide Gadioli, Gianluca Palermo, Vittorio Zaccaria, Cristina SilvanoPolitecnico di Milano

Computer Architecture Evolution

“The number of transistors incorporated in a chip will approximately double every two years” – Gordon Moore, Intel co-founder

Time

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80863um

3861.5um

Pentium 40.18um

Core2 Duo65nm

Nehalem45nm

80863um

3861.5um

Pentium 40.8um

Core2 Duo65nm

Nehalem45nm

“Moore’s Law” on Performance

1987 2003 2011 2020

Perf

orm

ance

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80863um

3861.5um

Pentium 40.8um

Core2 Duo65nm

Nehalem45nm


1987 2003 2011 2020

Perf

orm

ance

The Golden Era:- Single-processor

- 1st Power Wall

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80863um

3861.5um

Pentium 40.8um

Core2 Duo65nm

Nehalem45nm


1987 2003 2011 2020

Perf

orm

ance

The Multicore Era:- 2 to 16 cores- On-chip shared LL$

- Programmability challenge

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80863um

3861.5um

Pentium 40.8um

Core2 Duo65nm

Nehalem45nm


1987 2003 2011 2020

Perf

orm

ance

The Manycore Era:- Larger # of cores- Networks on-Chip

- Programmability challenge + Dynamic Resource Management

?

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Main Idea

In the context of resource consolidation, analyze the orthogonal effects of: Resource Management Application Auto‐Tuning

Approximate computing



…

Target Platforms

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Main Idea





…

Target Platforms

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Multicore PlatformMulticore Platform

Run‐Time Resource Management

Amit Kumar Singh, Muhammad Shafique, Akash Kumar, and Jörg Henkel. Mapping on multi/many‐core systems: survey of current and emerging trends. In Proceedings of the 50th Annual Design Automation Conference (DAC). 2013.

RTRM

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App1 App2 App3

Target Platform

RTRM ‐ Overview

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RTRM

App1 App2 App3

Accounting

Mapping

Target Platform

RTRM ‐ Overview

Resource accounting phase grants resources to critical workloads while optimize resource usage by best‐effort workloads

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RTRM

App1 App2 App3

Accounting

Mapping

44 44 66

Target Platform

RTRM ‐ Overview

Resource accounting phase grants resources to critical workloads while optimize resource usage by best‐effort workloads

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RTRM

App1 App2 App3

Accounting

Mapping

Mapping phase maps virtual resources on physical resources

to achieve optimal platform usage to handle run‐time variations

44 44 66

Target Platform

Application Auto‐TuningKey idea is that most of the applications are configurable thanks to a set of parameters

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Parameters:• Color

Parameters:• Color

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Parameters:• Color • Shape


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• Size


• Size

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Application Auto‐Tuning

Run-Time Knobs

Key idea is that most of the applications are configurable thanks to a set of parameters

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Why Run‐Time Knobs & Auto‐Tuning?In some applications internal knobs that can be used to trade‐off between application quality of results and performance

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QoR

Performance


Autonomous Video-surveillance

System

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Video Frame Rate

Video Resolution

Autonomous Video-surveillance

System

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Application Auto‐Tuning Framework

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Execution LoopExecution Loop

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MonitoringMonitoring

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Re-ConfigureRe-Configure

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Target HW Platform

Orthogonality Concept

Platform OS

Req

uests

Res

ourc

es

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Target HW Platform


Platform OS

Req

uests

Res

ourc

es

Exploitation of OpenCLDevice Fission to limit

resource requests


resource requests

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Run-Time Resource Manager


Platform OS

Target HW PlatformReq

uests

Res

ourc

es


resource requests


resource requests

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The Multi‐View Case Study

2 eyes = 3 dimensions

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Implementation 1

PR PL QR QL

P

Q

CAM LEFT

CAM RIGHT

PL

PR

QR

QL

DP

DQ

1 Ke Zhang, Jiangbo Lu, and Gauthier Lafruit, “Cross-Based Local Stereo Matching Using Orthogonal Integral Images”,IEEE Transactions On Circuits and Systems For Video Technology, Vol. 19, No. 7, July 2009

1 Ke Zhang, Jiangbo Lu, and Gauthier Lafruit, “Cross-Based Local Stereo Matching Using Orthogonal Integral Images”,IEEE Transactions On Circuits and Systems For Video Technology, Vol. 19, No. 7, July 2009

CAM LEFT CAM RIGHT

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Pixel disparity

Left camera Right camera

reference disparity

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Pixel disparity

Left camera Right camera

reference disparity

1

2

3

QoRDisparity

Error

QoRDisparity

Error

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5 Application Knobs

Experimental SetupTarget Platform

AMD NUMA Architecture: 4 nodes ‐ 4 cores • OpenCL 1.2 run‐time provided by AMD

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Workload Definition:Single application – multiple instancesDynamic workload in terms of start time,amount of data to process, frame‐rate goal

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Workload Definition:Single application – multiple instancesDynamic workload in terms of start time,amount of data to process, frame‐rate goal

Evaluation MetricsNormalized Actual Penalty (Performance/Quality metric)• User satisfaction in terms of Application Frame‐Rate

Normalized Application Error (Quality metric) • User satisfaction in terms of quality of the resulting image (1/QoR)

Difference w.r.t. off‐line profiling (Predictability metric)14

Application Auto‐Tuning Effects

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Comparative AnalysisApplication Auto-tuning

Run-T

ime

Res

ourc

e M

anag

emen

tOFF ON

OFF

ON

PLAIN-LINUX ADAPTIVE-LINUX

PLAIN-RTRM ADAPTIVE-RTRM

(No Device Fission)

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Run‐Time Results

Missed Deadlines

Missed Deadlines

Design-Time Vs Run-Time

Profiling


Profiling

QoRDisparity

Error

QoRDisparity

Error

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6 #Multi-View Apps#Multi-View Apps17

Run‐Time Results

Missed Deadlines

Missed Deadlines


Profiling


Profiling

QoRDisparity

Error

QoRDisparity

Error

- - ++

- + -+

- - ++

1 2 3 4 5 6

1 2 3 4 5 6


Run‐Time Results

Missed Deadlines

Missed Deadlines


Profiling


Profiling

QoRDisparity

Error

QoRDisparity

Error

- - + +

- + - +

- - + +

1 2 3 4 5 6

1 2 3 4 5 6


Run‐Time Results

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APPSAPPS

Resource‐Aware AS‐RTM

Target HW Platform

Platform OS

Req

uests

Res

ourc

esResource

Availability

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ConclusionsWe considered the problem of managing multiple OpenCL applications for server consolidation on multi‐core platforms

We implemented an approach exploiting run‐time management frameworks operating both at application level or at OS/resource level

Analysis of results:Auto‐tuning is necessary to modulate performance and QoRResource‐awareness is needed for predictability by means of resource isolation (RTRM) or simple monitor (RA‐AS‐RTM)

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Evaluating Orthogonalitybetween Application Auto tuning ...

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