CHALLENGING SCHEDULING PROBLEM IN THE FIELD OF SYSTEM DESIGN

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

2. OUTLINE Talk outline Talk topic • Present a mixed resource allocation and schedulingproblemarising in the design flow of embedded systems • Discuss some problem variants (including a stochastic one) • Discuss the instances we used… • …and the instance generator

3. MOTIVATION & CONTEXT Motivation Why is this stuff interesting? 1. It’s challenging • Mainly due to the resource allocation phase 2. It’s a real world problem • It’s important to test solvers on real problems 3. We considered many problem variants • Different objective function • Deterministic vs stochastic 4. The instance generator • Flexible, from random to “real like” instances

4. MOTIVATION & CONTEXT Embedded system Embedded system any information processing device embedded in another product They are finding widespread application: automotive digital convergence market share They are special purpose They often run a fixed set of applications Strong off-line optimization before the deployment

5. MOTIVATION & CONTEXT Platforms for embedded systems General trend: • special purpose application • “general purpose” platform H/S co-design: A promising platform: MPSoCs IBM, CELL processor

6. MOTIVATION & CONTEXT Embedded system design System design with MPSoCs Design flow 1 • Given a platform description • and an application abstraction • compute an allocation&schedule • verify results & perform changes Exploit application and platform parallelism to achieve real time performance 2 3 4 Crucial role of the allocation& scheduling algorithm 5 P1 P1 P2 ram 1 2 4 P2 3 5 1 2 4 t 3 5

7. PROBLEM DESCRIPTION MPSoC platform MPSoC platform • Identical processing elements (PE) • Local storage devices • Remote on-chip memory • Shared bus Resources: • PEs • Local memory devices • Shared BUS • PE frequencies DVS Constraints: • Local device capacity • Bus bandwidth (additive resource) • Architecture dependent constraints

8. PROBLEM DESCRIPTION Application Task graph • Nodes are tasks/processes • Arcs are data communication 1 Each task: 2 3 • Reads data for each ingoing arc • Performs some computation • Writes data for each outgoing arc 6 4 5 PROG. DATA LOC/REM FREQ. PE WR RD EXEC WR LOC/REM COMM. BUFFER

9. PROBLEM DESCRIPTION Application Task graph • Memory allocation • Remote memory is slower than the local ones • Execution frequency Durations depend on: 1 2 3 6 4 Different phases have different bus requirements 5 PROG. DATA LOC/REM FREQ. PE WR RD EXEC WR LOC/REM COMM. BUFFER

10. PROBLEM VARIANTS Application We focused on problem variants with different • Objective function • Graph features O. F. Bustraffic Energyconsumption Makespan G.F. Pipelined no DVS DVS no DVS Generic no DVS DVS no DVS Generic withcond. branches no DVS DVS no DVS

11. PROBLEM VARIANTS Objective function 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 Allocation dependent Schedule dependent Time & energy cost t 1 2 3 4

12. PROBLEM VARIANTS Graph structure Pipelined Typical of stream processing applications 1 2 3 4 Generic Generic with cond. branches 0.7 0.3 1 1 !a a 2 3 2 3 0.5 !b 0.5 b 6 6 4 4 5 5 Stochastic problem Stochastic O.F. (exp. value)

13. INSTANCES & INSTANCE GENERATOR Instances – an overview Different needs… • Solve real problems • Test the solvers and the models • Perform computation studies …different instances • Real world instances • “Synthetic” instances • Random instances Where do they come from?

14. INSTANCES & INSTANCE GENERATOR Real world instances …extract atask graph… Real world instances 1 From application code… 2 3 6 The extraction phase is tricky and slow 4 5 …and compute durations by repeated runs

15. INSTANCES & INSTANCE GENERATOR Synthetic instances Synthetic instances Randomly generate a graph… 1 2 3 Faster, but not enough for a statistical complexity study 6 4 5 …wrap it into a synthetic computer program… …again, compute durations by repeated runs

16. INSTANCES & INSTANCE GENERATOR Random instances Why random instances? • Quickly available “real like” instances • Completely random instances to test the solvers Instance generator* able to provide both Generation algorithm ..repeatedly replace an arc with series of nodes… Start from a bipartite graph… * Soon available for download at http://www-lia.deis.unibo.it/Staff/AlessioGuerri/Sched_LIB

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

18. INSTANCES & INSTANCE GENERATOR Random instances • The generation process is tuned by means of “random parameters” • rp(MIN, MAX, DISTRIBUTION) • mrp(MEAN, ST. DEV., DISTRIBUTION) Linear, quadratic, exponential Crucial to have “real like” instances Nodes and arcs are labeled with custom lists of attributes • Nodes and arc attributes can depend on each other and are specified via a simple functional language • node_attrdurrp(100, 200, LIN) • node_attrlong_dur sum(dur, rp(50, 100, EXP))

19. CONCLUSIONS Conclusions • It’s important for a competition to evaluatesolvers on real problems • Mixed allocation & scheduling problem • Complex allocation phase Questions? • Different O.F.s • Deterministic&Stochastic problems • Random instances • Realistic instances needed for real problems

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