CHALLENGING SCHEDULING PROBLEM IN THE FIELD OF SYSTEM DESIGN

Alessio GuerriMichele Lombardi *Michela MilanoDEIS, University of Bologna

Talk outlineOUTLINE

Talk topic

Present a mixed resource allocation and scheduling problem arising in the design flow of embedded systems

Discuss some problem variants (including a stochastic one)

Discuss the instances we used… …and the instance generator

Motivation MOTIVATION & CONTEXT

Mainly due to the resource allocation phase

Why is this stuff interesting?

1. It’s challenging

2. It’s a real world problem

3. We considered many problem variants

It’s important to test solvers on real problems

Different objective function Deterministic vs stochastic

4. The instance generator Flexible, from random to “real like” instances

Embedded systemMOTIVATION & CONTEXT

Embedded system

They are special purpose

automotive digital convergence

market share

any information processing device embedded in another product

They are finding widespread application:

They often run a fixed set of applications

Strong off-line optimization before the deployment

Platforms for embedded systemsMOTIVATION & CONTEXT

General trend:

A promising platform: MPSoCs

H/S co-design: special purpose application “general purpose” platform

IBM

, C

ELL

pro

cess

or

Embedded system designMOTIVATION & CONTEXT

System design with MPSoCs

Exploit application and platform parallelism to

achieve real time performance

Given a platform description

and an application abstraction

compute an allocation & schedule

verify results & perform changes

Design flow

1

2 3

4

5

P1 P2 ram

P1

P2

1 2

3

4

5

1 2

3

4

5 t

Crucial role of the allocation& scheduling algorithm

MPSoC platform

Identical processing elements (PE)

Local storage devices Remote on-chip

memory Shared bus

PROBLEM DESCRIPTION

MPSoC platform

Resources:

Constraints:

PEs Local memory devices Shared BUS PE frequencies Local device capacity Bus bandwidth (additive resource) Architecture dependent constraints

DVS

Application

Nodes are tasks/processes Arcs are data communication

PROBLEM DESCRIPTION

Task graph

1

2 3

4

5

6

RD

Each task:

Reads data for each ingoing arc

Performs some computation Writes data for each outgoing

arc

EXEC

WR

WR

COMM. BUFFER

LOC/REM

PEPROG. DATA

LOC/REMFREQ

.

Application

Memory allocation Remote memory is slower than the local ones

Execution frequency

PROBLEM DESCRIPTION

Task graph

1

2 3

4

5

6

RD EXEC

WR

WR

COMM. BUFFER

LOC/REM

PEPROG. DATA

LOC/REMFREQ

.

Durations depend on:

Different phases have different bus requirements

ApplicationPROBLEM VARIANTS

O. F.

We focused on problem variants with different Objective function Graph features

G.F. Bustraffic

Energyconsumptio

nMakespan

Pipelined

Generic

Generic withcond.

branches

DVS

DVS

DVS

no DVS

no DVS

no DVS

no DVS

no DVS

no DVS

Objective functionPROBLEM VARIANTS

Bus traffic

Tasks produce traffic when they access the bus Completely depends on memory allocation

Makespan

Completely depends on the computed schedule

Energy (DVS)

The higher the frequency, the higher the energy consumption

Cost for frequency switching

1 2 3 t4

Time & energy

cost

Allocation dependentSchedule dependent

Graph structurePROBLEM VARIANTS

1 2 3 4

Pipelined

Typical of stream processing applications

Generic

1

2 3

4

5

6

Generic with cond. branches

1

2 3

4

5

6

a !a

b!b

Stochastic problem

0.3

0.7

0.5

0.5

Stochastic O.F. (exp. value)

Instances – an overviewINSTANCES & INSTANCE GENERATOR

Real world instances “Synthetic” instances Random instances

…different instances

Different needs…

Solve real problems Test the solvers and the models Perform computation studies

Where do they come from?

Real world instancesINSTANCES & INSTANCE GENERATOR

Real world instances1

2 3

4

5

6

From application code…

…extract atask graph…

…and compute durations by repeated

runs

The extraction phase is tricky and slow

Synthetic instancesINSTANCES & INSTANCE GENERATOR

Synthetic instances

1

2 3

4

5

6

Randomly generate a graph…

…wrap it into a synthetic computer

program…

…again, compute

durations by repeated runs

Faster, but not enough for a statistical complexity study

Random instancesINSTANCES & INSTANCE GENERATOR

Why random instances? Quickly available “real like” instances Completely random instances to test the solvers

Instance generator* able to provide both

Generation algorithm

Start from a bipartite graph…

..repeatedly replace an arc with series of nodes…

* Soon available for download at http://www-lia.deis.unibo.it/Staff/AlessioGuerri/Sched_LIB

Random instancesINSTANCES & INSTANCE GENERATOR

..randomly add some nodes…

..randomly add some arcs

..cut arcs to turn some nodes into “tails”…

Random

Real like

Many types of instancesby balancing these two steps

Random instancesINSTANCES & INSTANCE GENERATOR

The generation process is tuned by means of “random parameters”rp(MIN, MAX, DISTRIBUTION)mrp(MEAN, ST. DEV., DISTRIBUTION)

Linear, quadratic,

exponential

Nodes and arc attributes can depend on each other and are specified via a simple functional languagenode_attr dur rp(100, 200, LIN)node_attr long_dur sum(dur, rp(50, 100, EXP))

Nodes and arcs are labeled with custom lists of attributes

Crucial to have “real like” instances

ConclusionsCONCLUSIONS

Mixed allocation & scheduling problem

Complex allocation phase

It’s important for a competition to evaluate solvers on real problems

Different O.F.s Deterministic &

Stochastic problems

Random instances

Realistic instances needed for real problems

Questions?

CHALLENGING SCHEDULING PROBLEM IN THE FIELD OF SYSTEM DESIGN

Alessio GuerriMichele Lombardi *Michela MilanoDEIS, University of Bologna