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Shared Memory and Distributed Memory Parallel Ray Tracer

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Presentation on theme: "Shared Memory and Distributed Memory Parallel Ray Tracer"— Presentation transcript:

1 Shared Memory and Distributed Memory Parallel Ray Tracer
Dmitriy Bespalov

2 Overview What is Ray Tracer Implemented Features of Ray Tracer
Parallel Implementation Experimental Results Conclusions

3 What is Ray Tracer Ray Tracer – generate realistic images

4 Trivial Ray Tracer

5 Hierarchical Modeling

6 Phong’s shading model and supersampling

7 Acceleration Technique
Prune rays earlier Use axis-aligned bounding boxes Divide bounding boxes into buckets

8 Shadows and Reflection
Phong’s shading model and supersampling

9 Parallel Implementation
Shared Memory Create job list Threads remove jobs from the list Distributed Memory Send scene to every work node Work nodes request jobs from supervisor

10 Experimental Results Render same scene using both ray tracers
4 reflective spheres 3 meshes (~20000 faces)

11 Running Times Shared Memory Distributed Memory 1 thread: 682.3 seconds
2 threads: seconds Distributed Memory 1 thread on ws79: seconds

12 Running Times

13 Speed-Up

14 Efficiency

15 Conclusions Good parallel performance Shared Memory
Almost twice as fast with 2 threads Distributed Memory: Linear speed-up Constant efficiency Run time is independent of the job size

16 Thank You! Questions?


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