CHALLENGING SCHEDULING PROBLEM IN THE FIELD OF SYSTEM DESIGN Alessio Guerri Michele Lombardi * Michela Milano DEIS, University of Bologna

Talk outline OUTLINE 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 system MOTIVATION & CONTEXT Embedded system They are special purpose automotivedigital 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 systems MOTIVATION & CONTEXT General trend: A promising platform: MPSoCs H/S co-design:  special purpose application  “general purpose” platform IBM, CELL processor

Embedded system design MOTIVATION & 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 P1P2ram P1 P 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 RD Each task:  Reads data for each ingoing arc  Performs some computation  Writes data for each outgoing arc EXEC WR COMM. BUFFER LOC/REM PE PROG. DATA LOC/REM FREQ.

Application  Memory allocation  Remote memory is slower than the local ones  Execution frequency PROBLEM DESCRIPTION Task graph RDEXEC WR COMM. BUFFER LOC/REM PE PROG. DATA LOC/REM FREQ. Durations depend on: Different phases have different bus requirements

Application PROBLEM VARIANTS O. F. We focused on problem variants with different  Objective function  Graph features G.F. Bus traffic Energy consumption Makespan Pipelined Generic Generic with cond. branches DVS no DVS

Objective function PROBLEM 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 123 t 4 Time & energy cost Allocation dependent Schedule dependent

Graph structure PROBLEM VARIANTS 1234 Pipelined Typical of stream processing applications Generic Generic with cond. branches a !a b !b Stochastic problem Stochastic O.F. (exp. value)

Instances – an overview INSTANCES & 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 instances INSTANCES & INSTANCE GENERATOR Real world instances From application code… …extract a task graph… …and compute durations by repeated runs The extraction phase is tricky and slow

Synthetic instances INSTANCES & INSTANCE GENERATOR Synthetic instances 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 instances INSTANCES & 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

Random instances INSTANCES & INSTANCE GENERATOR..randomly add some nodes…..randomly add some arcs..cut arcs to turn some nodes into “tails”… Random Real like Many types of instances by balancing these two steps

Random instances INSTANCES & 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 language  node_attrdurrp(100, 200, LIN)  node_attrlong_dursum(dur, rp(50, 100, EXP)) Nodes and arcs are labeled with custom lists of attributes Crucial to have “real like” instances

Conclusions CONCLUSIONS  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 Guerri Michele Lombardi * Michela Milano DEIS, University of Bologna