1. Scalable Data Clustering with GPUs Andrew D. Pangborn Thesis Defense Rochester Institute of Technology Computer Engineering Department Friday, May 14 th 2010

2. Data Clustering

3. Data Clustering Cont.

4. Example

5. Flow Cytometry

6. Flow Cytometry Cont.

7. Flow Cytometry Data Sets Size of the data, motivation for GPUs / parallel processing

8. Parallel Computing

9. Trend toward multi-core, many-core architectures

10. GPU Architecture Trends

11. Tesla GPU Architecture

12. GPGPU

13. CUDA Software Stack

14. CUDA Programming Model

15. CUDA Kernel Grids / Blocks /Threads

16. CUDA Memory

17. CUDA Program Flow

18. C-means

19. C-means Parallel Implementation

21. EM with a Gaussian mixture model

22. EM Parallel Implementation

24. Performance Tuning Global Memory Coalescing – 1.0/1.1 vs 1.2/1.3 devices

25. Performance Tuning Partition Camping

26. Performance Tuning CUBLAS

27. Multi-GPU Strategy 3 Tier Parallel hierarchy – MPI, OpenMP, CUDA

28. Multi-GPU Strategy MapReduce-style data distribution and reduction

29. Multi-GPU Implementation Very little impact on GPU kernel implementations, just their inputs / grid dimensions Discuss host-code changes

30. Data Distribution Asynchronous MPI sends from host instead of each node reading input file from data store

31. Results - Kernels Speedup figures

32. Results - Kernels Speedup figures

33. Results – Overhead Time-breakdown for I/O, GPU memcpy, etc

34. Multi-GPU Results Amdahl’s Law vs. Gustafson’s Law – i.e. Strong vs. Weak Scaling – i.e. Fixed Problem Size vs. Fixed-Time – i.e. True Speedup vs. Scaled Speedup

35. Fixed Problem Size Analysis

36. Time-Constrained Analysis

37. Conclusions

39. Future Work

40. Questions?

41. References