1. i MAGIS is a joint project of CNRS - INPG - INRIA - UJF iMAGIS-GRAVIR / IMAG Efficient Parallel Refinement for Hierarchical Radiosity on a DSM computer François X. Sillion, Jean-Marc Hasenfratz iMAGIS

2. iMAGIS-GRAVIR / IMAG Radiosity

3. iMAGIS-GRAVIR / IMAG Hierarchical Radiosity Hierarchical representation (mesh) Interactions computed at appropriate level

4. iMAGIS-GRAVIR / IMAG Strategies for Hierarchical Radiosity Gathering –memory consuming (store links) –Easier dynamic modifications Shooting –Memory efficient –Requires heuristic to decide shooting level –Links recomputed as needed

5. iMAGIS-GRAVIR / IMAG Parallel Approaches Two approaches: –data exchange via message-passing algorithms –Shared memory Partial solutions possible if “natural” partitioning exists (e.g. inside buildings) [Fun96,FY97] Virtual interfaces are harder to handle [RAPP97] Load balancing problem[Cav99]

6. iMAGIS-GRAVIR / IMAG Scheduler Force all link refinement operations through a scheduler object Natural place for –Parallel synchronization –Orientation and steering of calculation Advantages of using scheduler: –Global view of all pending task at any given time –Task extraction can be made according to various selection criteria

7. iMAGIS-GRAVIR / IMAG Example (sequential) schedulers Stack scheduler (depth first refinement) Priority scheduler –Use simple structure (heap) –Hierarchical level (breadth first) –Size, energy, error –Interactive user control Random scheduler...

8. iMAGIS-GRAVIR / IMAG Architecture Solver Main / GUI … Refiner

9. iMAGIS-GRAVIR / IMAG Synchronization 1.Scheduler –Single object talks to all refiners => Danger! –Use simple blocks of refinement jobs 2.Hierarchical data structure –Consistency of hierarchical scene structure 3.Interactions –Links or energy representations

10. iMAGIS-GRAVIR / IMAG Test scenes VRLab - 51 182 polygonsAircraft - 184 456 polygons Office - 5 285 polygons

11. iMAGIS-GRAVIR / IMAG Measurements Hardware architecture: –ccNUMA SGI 2000 computer with 64 microprocessors –Limit to 40 microprocessors R10000 at 195MHz Proc A Proc B IO Xbar IO Xbar Node 1 Node 1 Node 511 Node 511 Hub Chip Hub Chip Mem & Dir Mem & Dir Scalable Interconnect Network Node 0 IO Ctrls … … R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R

12. iMAGIS-GRAVIR / IMAG Measurements Time measurements: –Refinement: times system call which return clock ticks –Memory access, cache access…: perfex software tool which uses the 31 hard counters of R10,000

13. iMAGIS-GRAVIR / IMAG Results CPU Refinement time

14. iMAGIS-GRAVIR / IMAG Results Speed-up

15. iMAGIS-GRAVIR / IMAG Results Influence of the size of link blocks on overall CPU time

16. iMAGIS-GRAVIR / IMAG Results Memory used before and during the iterations

17. iMAGIS-GRAVIR / IMAG Conclusions Very simple atomic tasks Easily managed with a single scheduler structure Easily implemented on top of an existing radiosity simulation code –Thread setup –New link creation upon refinement decision

18. iMAGIS-GRAVIR / IMAG Future work Understanding the peculiar behaviour observed for the aircraft scene Dealing with graphics resources for “optimized” calculations using graphics hardware

19. iMAGIS-GRAVIR / IMAG Acknowledgements Peter Kipfer contributed to the design and early implementation of this work. Thanks to Centre Charles Hermite for providing access to its computational resources Laurent Alonso provided useful advice on performance questions. This work was supported in part by the European Union’s ESPRIT project #24944, ARCADE (“Making Radiosity Usable”).