technische universiteit eindhoven

Department of Electrical EngineeringElectronic Systems

Platform-based Design 5KK70

MPSoCControlling the Parallel Resources

Bart Mesman & Henk Corporaal

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Contents

PicoChip Real-Time Scheduling basics Resource Management

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Pico Chip

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Pico Chip

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Pico Chip

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Fault-Tolerance

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Pico Chip

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Real-time systems (Reinder Bril)

Correct result at the right time: timeliness Many products contain embedded computers, e.g.

cars, planes, medical and consumer electronics equipment, industrial control.

In such systems, it’s important to deliver correct functionality on time.

Example: inflation of an air bag

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Example: Multimedia Consumer Terminals

DVD CDxfront end

YC interface

IEEE 1394interface

DVB Tuner

Cable modem

CVBSinterface

VGA

RF Tuner

(by courtesy of Maria Gabrani)

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Example: High quality video & real time

original

up-scaled

Rendered stream: 60 Hz (TV screen)

Input stream: 24 Hz (movie)

TV companies invest heavily in video enhancement,e.g. temporal up-scaling

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Example: High quality video & real time

original

up-scaled

Input stream: 24 Hz (movie)

TV companies invest heavily in video enhancement,e.g. temporal up-scaling

displayed

• Deadline miss leads to “wrong” picture.

• Deadline misses tend to come in bursts (heavy load).

• Valuable work may be lost.

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Real-time systems

Guaranteeing timeliness requirements: real-time tasks with timing constraints scheduling of tasks

Fixed-priority scheduling (FPS) is the de-facto standard for scheduling in real-time systems.

FPS: supported by commercially available RTOS; analytic and synthetic methods.

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Recap of FPS

Fixed Priority Pre-emptive Scheduling (FPPS) A basic scheduling model Analysis Example Optimality of RMS and DMS

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FPPS: A basic scheduling model

Single processor Set of n independent, periodic tasks 1, …, n

Tasks are assigned fixed priorities, and can be pre-empted instantaneously.

Scheduling: At any moment in time, the processor is used to execute the highest priority task that has work pending.

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FPPS: A basic scheduling model

Task characteristics: period T, (worst-case) computation time C, (relative) deadline D,

Assumptions: non-idling; context switching and scheduling overhead is ignored; execution of releases in order of arrival; deadlines are hard, and D T; 1 has highest and n has lowest priority. No data-dependencies between tasks

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FPPS: Example

Worst-case response time WR for task 3: First point in time that 1, 2, and 3 are finished

time0 10 20 30 40 50 60

Task 1

Task 3

Task 2

1 2

1

6543

2 3

WR3 = 56WR2 = 17

WR1 = 3

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FPPS: Analysis

Schedulable iff: WRi Di for 1 i n

Necessary condition:

Sufficient condition for RMS: U LL(n) = n (21/n – 1), i.e.i >j iff Ti < Tj;

Di = Ti.

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ni i

i

T

CU

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FPPS: Analysis

Otherwise, i.e. U 1 and not RMS, or n(21/n – 1) < U < 1 and RMS

exact condition: Critical instant: simultaneous release of i with all

higher priority tasks WRi is the smallest positive solution of

jij j

i CT

xCx

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FPPS: Example

Task set Γ consisting of 3 tasks:

Notes: RM priority assignment and Di = Ti (RMS);

U1 + U2 + U3 = 0.97 1, hence Γ could be schedulable;

Utilization bound: U(n) LL(n) = n (21/n – 1): U1+U2 = 0.88 > LL(2) 0.83,

therefore U(3) > LL(3), hence another test required.

Task PeriodT Computation time C

Utilization U

1 10 3 0.3

2 19 11 0.58

3 56 5 0.09

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FPPS: Example

Time line

time0 10 20 30 40 50 60

Task 1

Task 3

Task 2

1 2

1

6543

2 3

WR3 = 56WR2 = 17

WR1 = 3

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FPPS: Optimality of RMS and DMS

Priority assignment policies: Rate Monotonic (RM): i >j iff Ti < Tj

Deadline Monotonic (DM): i >j iff Di < Dj

Under arbitrary phasing: RMS is optimal among FPS when Di = Ti;

DMS is optimal among FPS when Di Ti,

where optimal means: if an FPS algorithm can schedule the task set, so can RMS/DMS.

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Non-Preemptive Systems (Akash Kumar)

State-space needed is smaller Lower implementation cost Less overhead at run-time Cache pollution, memory size

Task

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Why FPS doesn’t work for “future” high-performance platforms

Heavy-duty DSPs: Preemption not supported If it was: Context switching is significant

Data-dependencies not taken into account Multi-processor

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Related Research – Feasibility Analysis

Preemptive

Non-Preemptive

Homogeneous MPSoC

[Liu, Layland, 1973]

Heterogeneous MPSoC

[Jeffay, 1991]

[Baruah, 2006]

[ , 2020??]

A

B

C

D

P1 P2 P3

P4 P5 P6

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Unpredictability – Variation in Execution Time

A

B

t1t0 t2 t3SteadyState

P1

P2

P3

50 50

50

A 50 50

50

B

A

B

t1t0 t2 t3SteadyState

49 49

49

A 49 49

49

B

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Problem

No good techniques exist to analyze and schedule applications on non-

preemptive heterogeneous systems

Resource Manager proposed to schedule applications such that they meet their

performance requirements on non-preemptive heterogeneous systems

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Our Assumptions

Heterogeneous MPSoC Applications modeled as SDF

Non-preemptive system – tasks can not be stopped Jobs can be suspended

Lot of dynamism in the system Jobs arriving and leaving at run-time Variation in execution time

Very simple arbiter at coresA2B2

C2

D2

Job

Task

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Resource Manager

ResourceManager

Reconfigure to meet above qualitymilliseconds

Local Processor

Arbiter

Task level micro sec

A B

Core

Application QoS

Manager

Application level few sec

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Architecture Description

Computation resources available are described Each processor can have different arbiter

In this model First Come First Serve mechanism is used

Resource manager can configure/control the local arbiters: to regulate the progress of application if needed

P1 P2 P3

Resource Manager Local Arbiter

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Resource Manager

Responsible for two main things Admission control

Incoming application specifies throughput requirement Execution-time and mapping of each actor Repetition vector used to compute expected utilization RM checks if enough resources present Allocates resources to applications if admitted

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Admission Control

P1 P2 P3

Typing SmsVideo Conf Play MP3

Resource Reqmt

Exceeded!

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Resource Manager

Admission control Budget enforcement

When running, each application signals RM when it completes an iteration

RM keeps track of each application’s progress Operation modes

‘Polling’ mode ‘Interrupt’ mode

Suspends application if needed

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Budget Enforcement (Polling)

Performance goes down!

Resource

Manager

Better than required!

New job enters!

job suspended!

job resumed!

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Experiments

A high-level simulation model developed POOSL – a parallel simulation language used

A protocol for communication defined System verified with a number of application

SDF models Case study done with H263 and JPEG

application models Impact of varying ‘polling’ interval studied

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Performance without Resource Manager

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Performance with RM – I (2.5m cycles)

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Performance with RM – II (500k cycles)