Models for Deterministic Execution of Real-time Multiprocessor Applications Peter Poplavko, Dario Socci, Paraskevas Bourgos, Saddek Bensalem, Marius Bozga

Models for Deterministic Execution of Real-timeMultiprocessor Applications

Peter Poplavko, Dario Socci, Paraskevas Bourgos, Saddek Bensalem, Marius Bozga

VERIMAG lab (CNRS, University of Grenoble)

Research supported by the European ICT Collaborative Project no. 288175(CERTAINTY) and no. 332987 (ARROWHEAD).

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I. Fixed Priority Process Networks - FPPN • Motivation • Model of computation

• KPN + synchronous events + priorities

II. Approximately static subclass of FPPN• Static scheduling• Online scheduling policy

Implementation and experimentsConclusions

Models for Multiprocessor Applications

Petro Poplavko / VERIMAG Grenoble11-Mar-15

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• Many industrial designs use fixed priority• schedulability analysis tools, programming language

support (ADA) and… deterministic communication

Fixed Priority Process Networks

Petro Poplavko / VERIMAG Grenoble11-Mar-15

• ????• … under the condition of short enough execution times

(not safe, but often holds for control apps)

• … valid restrictions on task set, e.g. Audsley’s approach for static task graphs [RTNS12]

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• Functionally deterministic models:• Kahn process networks and dataflow

streaming applications• Synchronous languages

reactive applications


11-Mar-15 Petro Poplavko / VERIMAG Grenoble

streaming + reactive?• Reactive Process Networks (RPN) deterministic and timed behavior ?

=> add priorities and synchronous events

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• Functionally deterministic models:• Kahn process networks and dataflow

streaming applications• Synchronous languages

reactive applications



FPPN is a combinationstreaming + reactive?• Reactive Process Networks (RPN) deterministic and timed behavior ?

=> add priorities and synchronous events

Petro Poplavko / VERIMAG Grenoble 6

Representative Example

11-Mar-15

InputA1 per 200ms

FilterB200ms

CoefB≤ 2 per 700ms

functional priority (at each channel)FIFOsporadic process

periodic process

Input Channel

FilterA100ms

OutputA200ms

NormA200ms

blackboard (shared variable)

OutputB100ms

Output Channel 1

Output Channel 2

burst size and inter-arrival

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FPPN: unexpected facts


• Channels are non-blocking

• Non-FIFO channels are allowed• FIFO, blackboard, user-defined…

• Processes = RTS tasks• sporadic / periodic / others…• deadline , inter-arrival time • any “classical” independent task set can be seen as FPPN

without channels

• Processes are synchronous• global time• simultaneously arriving / non-arriving

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Relative order of execution


precedence between communicating jobs:• follow the arrival times• simultaneous arrival => apply priority

dynamically constructed precedence graph

sporadic A sporadic B

time

A[1](0,2000)

A[2](10,2010)

B[1](20,3020)

B[3](70,3070)

A[3](70,2070)

B[2](50,3050)

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II. Approximately static FPPN


FilterB[1](0,200)

OutputB[1](0,100)

InputA[1](0,200)

FilterA[1](0,100)

OutputA[1](0,200)

CoefB[1](0,200)

CoefB[2](0,200)

FilterA[2](100,200)

NormA[1](0,200)

OutputB[2](100,200)

p i [k i](A i , D i )

Restriction and static approximation

11-Mar-15 Petro Poplavko / VERIMAG Grenoble 10

FilterB200ms

CoefB≤ 2 per700ms

every sporadic process

periodic server user period

unique periodic “user” process

OutputB[1]

M1 InputA[1] FilterA[1] OutputA[1]

COEFB[1]

FilterA[2]

OutputB[2]

100 ms -arrival0 arrival

M2 FilterB[1]


…can be scheduled by List Scheduling

• easy, fast but sub-optimal (work conserving)

• better heuristics:

• topological permutation [PRORISC2000]

• force-directed scheduling

200 ms cycle

COEFB[2]

NormA[2]


Online policy illustrated by Petri Net

wait 0+cyc wait 100+cyc cyc:= cyc+200

OutputB[1]

M1 InputA[1] FilterA[1] OutputA[1]

COEFB[1]

FilterA[2]

OutputB[2]

100 ms -arrival0 arrival

M2 FilterB[1]

200 ms cycle

COEFB[2]

NormA[2]


wait 0+cyc wait 100+cyc

Online policy and sporadic jobs

cyc:= cyc+200

wait 0+cyc sporadic jobs

up to 2 jobs/ period

-200+cyc 0+cyc

periodic server:𝕋 and 𝔽 tokens

𝔽 ; 𝔽𝕋 ; 𝔽𝕋 ; 𝕋

Utilization metric?

(0,200 , 25) (0,100 , 25)

(0,200 , 25) (0,100 , 25) (0,200 , 25)

(0,200, 5) (0,200 , 5)

(100,200 , 25) (0,200 , 5)

(100,200 , 25)


p i [k i](A i , D i , C i ) Load:

This example: 95%

p i [k i](A* i , D*i , C i ) Load*:

This example: 110%5

10 35

100

200

175

200

0

Implementation (public domain)


Application (FPPN+C)

Compile Application Compile Scheduling

StaticSchedule

BIP Compiler

Executable

Application + Scheduler (BIP)

BIP RTE

FFT2_0_1

generator

FFT2_0_2

FFT2_0_3

FFT2_0_0

FFT2_1_1

FFT2_1_2

FFT2_1_3

FFT2_1_0

FFT2_2_1

FFT2_2_2

FFT2_2_3

FFT2_2_0

consumer


Case Study1: FFT Transform

Kalray MPPA 256 Platform

Load* = 93% 120 % (with RT overhead)


Case Study1: FFT Transform (on 2 cores)

RT overhead

SensorInput200ms

AnemoConfig2 per200ms

GPSConfig2 per200ms

IRSConfig2 per200ms

DopplerConfig2 per200ms

HighFreqBCP200ms

LowFreqBCP5000ms

MagnDeclin1600ms

BCPConfig2 per200ms

Performance1000ms

MagnDeclinConfig5 per1600ms

PerformanceConfig5 per1000ms

AnemoDataGPSDataIRSData

DopplerData

PerformanceData

BCP Data

RM relative priority


Flight Management System (Thales)


Flight Management System (Thales)

Linux Intel i7 3.6 GHz platform

Static task graph: 812 nodes, 1977 edges

Load* = 23%


Related work

FPPN combines certain concepts of synchronous andstreaming languages - PRELUDE-SCHEDMCORE

synchronous language and multi-core scheduling - OIL – streaming language with timing constraints - Algorithms for scheduling task graph for synchronous systems

- Reactive Process Networks


Conclusions

• FPPN – model of computation for reactive + streaming applications

• Generalizing the determinism of uniprocessor fixed-priority systems to multi-processors

• Approximately static subclass:

• task graph derivation

• scheduling algorithm

• scheduling policy and utilization metric

• two use cases

Petro Poplavko / VERIMAG Grenoble 22

Thank you!

11-Mar-15

InputA200ms

FilterB200ms

CoefB2 per700ms OutputB

100ms

functional priority (at each channel)FIFOspradic process

periodic process

Input Channel

Output Channel 1

Load*:

This example: 110%

Documents

Models for Deterministic Execution of Real-time Multiprocessor Applications Peter Poplavko, Dario Socci, Paraskevas Bourgos, Saddek Bensalem, Marius Bozga