A RTRM Proposal for Multi/Many-Core Platforms and Reconfigurable Applications

P. Bellasi, G. Massari and W. Fornaciari{bellasi, massari, fornacia}@elet.polimi.it

ReCoSoC 2012

Dipartimento di Elettronica e InformazionePolitecnico di Milano

Last revision Jul, 2 2012

The BarbequeRTRM Framework 2

IntroductionWhy Run-Time Resource Management?

Run-Time Resources Management (RTRM) is aboutfinding the optimal tradeoff between

QoS requirements and resources availability

Target scenarioShared HW resources

upcoming many-core devices are complex systemsprocess variations and run-time issues

Mixed SW workloadsresources sharing and competition

among applications with different and time-varying requirements

Simple solutions are requiredsupport for frequently changing use-casessuitable for both critical and best-effort applications

The BarbequeRTRM Framework 3

IntroductionPaper Contribution

Methodology to support system-wide run-time resource management

exploiting design-time informationhierarchical and distributed control

BarbequeRTRM Frameworkmulti-objective optimization strategyeasily portable and modular designrun-time tunable and scalable policiesopen source project

http://www.2parma.eu

http://bosp.dei.polimi.it

The BarbequeRTRM Framework 4

Multi-O

bjective

IntroductionHow we compare?

Heter. P

latforms

Hom

og. Platform

s

Reconf./A

dapt

Mult. R

esources

Clustered R

esourcesC

ontrol-Theory Model

Design-Tim

e Exploitation

Portability

ResourcesManagers Proposals

StarPU

Binotto et al.

Fu et al.

ACTORS

SEEC

DistRM

BarbequeRTRM

Desirable Properties

The BarbequeRTRM Framework 5

The BarbequeRTRMSystem-Wide RTRM: Overall Framework

System-Wide RTRMCoarse grained control on platform available resources:- resource accounting, partitioning and abstraction- high-level HW events handling e.g., critical conditions, faults...- manage applications priorities- power/thermal “coarse tuning”

Application-Specific RTMFine grained control on application allocated resources:- task ordering- virtual processor assignment- DVFS- application parameters monitoringDynamic Code

Generation

Task Mapping

DDM

Critical Apps Best-Effort Apps

RTLib

Res Accounting Res Partitioning

Res Abstraction

MRAPI

Platform DRVPlatform DRVPlatform Driver

Platform Proxy

supported platforms

kernel

user-space

H

Platform Firmware

C

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RTLib

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SW/HW Meta-dataY

Legend

BarbequeRTRM

The BarbequeRTRM Framework 6

The BarbequeRTRMSystem-Wide RTRM: Presentation Outline

System-Wide RTRMCoarse grained control on platform available resources:- resource accounting, partitioning and abstraction- high-level HW events handling e.g., critical conditions, faults...- manage applications priorities- power/thermal “coarse tuning”

Application-Specific RTMFine grained control on application allocated resources:- task ordering- virtual processor assignment- DVFS- application parameters monitoringDynamic Code

Generation

Task Mapping

DDM

Critical Apps Best-Effort Apps

RTLib

Res Accounting Res Partitioning

Res Abstraction

MRAPI

Platform DRVPlatform DRVPlatform Driver

Platform Proxy

supported platforms

kernel

user-space

H

Platform Firmware

C

F

G

I

ba

c

d

e

f

RTLib

C

ED

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SW/HW Meta-dataY

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BarbequeRTRM

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2

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The BarbequeRTRM Framework 7

The BarbequeRTRMSystem-Wide RTRM: Distributed Hierarchical Control

System-Wide RTRMCoarse grained control on platform available resources:- resource accounting, partitioning and abstraction- high-level HW events handling e.g., critical conditions, faults...- manage applications priorities- power/thermal “coarse tuning”

Application-Specific RTMFine grained control on application allocated resources:- task ordering- virtual processor assignment- DVFS- application parameters monitoringDynamic Code

Generation

Task Mapping

DDM

Critical Apps Best-Effort Apps

RTLib

Res Accounting Res Partitioning

Res Abstraction

MRAPI

Platform DRVPlatform DRVPlatform Driver

Platform Proxy

supported platforms

kernel

user-space

H

Platform Firmware

C

F

G

I

ba

c

d

e

f

RTLib

C

ED

A B

X SW Interface (API)

SW/HW Meta-dataY

Legend

BarbequeRTRM

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The BarbequeRTRM Framework 8

The Proposed Control SolutionDistributed Hierarchical Control

Different subsystems have their own control loop (CL)System-wide level (resources partitioning, system-wide optimization, ...)

Application specific (application tuning, dynamic memory management, ...)

Firmware/OS level (F/V control, thermal alarms, resource availability, ...)

FF closed CLusing OP and AWM

Optimaluser defined goal functionsincluding overheads

Robust Adaptive

The BarbequeRTRM Framework 9

The BarbequeRTRMSystem-Wide RTRM: Resource Partitioning Strategy

System-Wide RTRMCoarse grained control on platform available resources:- resource accounting, partitioning and abstraction- high-level HW events handling e.g., critical conditions, faults...- manage applications priorities- power/thermal “coarse tuning”

Application-Specific RTRMFine grained control on application allocated resources:- task ordering- virtual processor assignment- DVFS- application parameters monitoringDynamic Code

Generation

Task Mapping

DDM

Critical Apps Best-Effort Apps

RTLib

Res Accounting Res Partitioning

Res Abstraction

MRAPI

Platform DRVPlatform DRVPlatform Driver

Platform Proxy

supported platforms

kernel

user-space

H

Platform Firmware

C

F

G

I

ba

c

d

e

f

RTLib

C

ED

A B

X SW Interface (API)

SW/HW Meta-dataY

Legend

BarbequeRTRM

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The BarbequeRTRM Framework 10

Scheduling PolicyYaMS - A modular multi-objective scheduler

Introduction of a new modular policy (YaMS)partition available resources (R) on applications (A)

considering A priorities and R “residual” availabilities

multi-objective optimizationsupport a set of tunable goals

DONE: performances, overheads,congestion, fairness

WIP: stability, robustness,thermal and power

increase overall system valueconsidering discrete and tunableimprovements

LP theory, MMKP heuristicpromote scheduling of some AWMs

which improve optimization goals

demote scheduling of others AWMswhich degrade solution metrics

e.g. stability and robustness

The BarbequeRTRM Framework 11

Scheduling PolicyYaMS - Scalability

Speedup

+36%

+54%

The BarbequeRTRM Framework 12

Scheduling PolicySystem-Wide Controller – Overall View

BBQ Validation Policy- enforce certain control properties

energy budget, stability and robustness- authorize resources synchronization

The BarbequeRTRM Framework 13

Scheduling PolicySystem-Wide Controller – Inner-Loop “Scheduling”

BBQ Validation Policy- enforce certain control properties

energy budget, stability and robustness- authorize resources synchronization

The BarbequeRTRM Framework 14

Scheduling PolicySystem-Wide Controller – Inner-Loop Overheads

+

+ +

Apps with 3 AWM, 3 Clusters => 9 configuration per applicationBBQ running on NSJ, 4 CPUs @ 2.5GHz (max)

The BarbequeRTRM Framework 15

The BarbequeRTRMSystem-Wide RTRM: Platform Integration

System-Wide RTRMCoarse grained control on platform available resources:- resource accounting, partitioning and abstraction- high-level HW events handling e.g., critical conditions, faults...- manage applications priorities- power/thermal “coarse tuning”

Application-Specific RTRMFine grained control on application allocated resources:- task ordering- virtual processor assignment- DVFS- application parameters monitoringDynamic Code

Generation

Task Mapping

DDM

Critical Apps Best-Effort Apps

RTLib

Res Accounting Res Partitioning

Res Abstraction

MRAPI

Platform DRVPlatform DRVPlatform Driver

Platform Proxy

supported platforms

kernel

user-space

H

Platform Firmware

C

F

G

I

ba

c

d

e

f

RTLib

C

ED

A B

X SW Interface (API)

SW/HW Meta-dataY

Legend

BarbequeRTRM

3

The BarbequeRTRM Framework 16

The BarbequeRTRMPlatform Integration Layer

Support for generic Linux SMP/NUMA machinesportable solution, based on Linux Control Group

CPUs and Memory nodes assignmentsMemory amount assignmentCPU bandwidth quota assignment (requires kernel 3.2)

Support both resources monitoring and controlpre-configured CGroup to define BBQ controlled resources

at Barbeque start, by parsing a pre-configured cgroups… than Barbeque takes control over these resources

tun-time generation of new CGroup to control applicationsrequires RTLib integration (of course)

Working on P2012 integrationnew genration many-core platform from STMicroelectronics

first version: 4 cluster with 16 Processing Element each one

The BarbequeRTRM Framework 17

Synchronization PolicySystem-Wide Controller – Outer-Loop “Synchronization”

BBQ Validation Policy- enforce certain control properties

energy budget, stability and robustness- authorize resources synchronization

The BarbequeRTRM Framework 18

Synchronization PolicySystem-Wide Controller – Outer-Loop Overheads

CGroupsPIL

+ +

+

+

+

min AWM 25% CPU Time, 3 Clusters x 4CPUs => max 48 syncsBBQ running on NSJ, 4 CPUs @ 2.5GHz (max)

Linux kernel 3.2Creation overheads: ~500msUpdate overheads: ~100ms

(1/3 on quadcore i7)

Application dependent

The BarbequeRTRM Framework 19

The BarbequeRTRM FrameworkPower Optimizations

X86_64 NUMA machine: 3 Clusters x 4CPUsBBQ running on NSJ, 4 CPUs @ 800MHz

Initial experiments on congested workloadsincreasing running instances of Bodytrack (PARSEC)

3AWM: [1,2,4] Threads

system-wide power measurementsvia the standard IPMI interface

Power Gains2,3-3,7%

Time Gains338-625%

The BarbequeRTRM Framework 20

The BarbequeRTRM FrameworkConclusions

Framework for System-Wide RTRMflexibility and scalability of the RTRM strategy

thanks to its hierarchical and distributed control structure

acceptable overheads for real usage scenariosincluding those with variable workload

tunable multi-objective optimization policiesto cope with several design constraints and goals

e.g., performance, power, thermal and reliability, ...

promising results in terms of performance improvingand power consumption reduction

for a highly parallel workload, on a NUMA multi-core architecture

availability of a simple API interfacemaking straightforward for the programmers to take full advantages from framework services

The BarbequeRTRM Framework 21

The BarbequeRTRM FrameworkFuture Works

Optimization policiesrobustness and stability assessmentimproving power/energy optimizationsthermal and reliability management

Platform integrationextended support for Android targetscomplete the integration with the P2012 many-core platform

Thanks for your attention!

If you are interested, please checkthe project website for further information

and keep update with the developments

http://bosp.dei.polimi.it