Weekly Report Start learning GPU

1. Weekly ReportStart learning GPU Ph.D. Student: Leo Leedate: Sep. 18, 2009

2. Outline • References • Courses study • Development • Work plan

3. Outline • References • Courses study • Development • Work plan

4. References • K-Means on commodity GPUs with CUDA • http://portal.acm.org/citation.cfm?id=1579193.1579654&coll=Portal&dl=GUIDE&CFID=52122012&CFTOKEN=42909759 • Accelerating K-Means on the Graphics Processor via CUDA • http://portal.acm.org/citation.cfm?id=1547557.1548166&coll=Portal&dl=GUIDE&CFID=53240258&CFTOKEN=63251930 • Fast Support Vector Machine Training and Classification on Graphics Processors • http://portal.acm.org/citation.cfm?id=1390156.1390170&coll=Portal&dl=GUIDE&CFID=53246314&CFTOKEN=25986930

5. K-Means on commodity GPUs with CUDA • Introduction: • OpenMP has too much message communication overhead. • GPU is becoming common. • Compared with Shuai Che, puts new centroids recalculation step also onto GPU and algorithm performance thus becomes better. • GPGPU • The challenge in mapping a computing problem efficiently on a GPU through CUDA is to store frequently used data items in the fastest memory, while keeping as much of the data on the device as possible. • digital investigation, physics simulation, molecular dynamics.

6. K-Means on commodity GPUs with CUDA • K-Means algorithm on GPU • Data objects assignment, two strategies • Centroids-oriented-when the number of processors is small; • Data objects-oriented, adopted in this paper. • K centroids recalculation • Massive condition statements are not suitable to the stream processor model of GPUs • Host rearranges all data objects and counts the number of data objects contained by each cluster. • GPU based K means

7. K-Means on commodity GPUs with CUDA • Performance analysis

8. K-Means on commodity GPUs with CUDA

9. K-Means on commodity GPUs with CUDA • Pros and cons • Describe a GPU based k-Means algorithm and achieve a speed up of 10; • Does not have enough comparison, especially with other GPU base algorithms.

10. Fast SVM Training and Classification on GPU • Introduction • SVM could be adapted to parallel computers. • SVM is widely used. • Training and classification are computationally intensive.

11. Fast SVM Training and Classification on GPU • C-SVM • SVM Training • SMO

12. Fast SVM Training and Classification on GPU

13. Fast SVM Training and Classification on GPU • SVM Classification

14. Fast SVM Training and Classification on GPU • Graphics Processors • General purpose; • More aggressive memory subsystems; • Peak performance is usually impossible to achieve, but GPU still has significant speedups; • True round to nearest even rounding on IEEE single precision datatypes and promise double precision in the near future. • Nvidia GeForce 8800 GTX • CUDA

15. Fast SVM Training and Classification on GPU • SVM Training Implementation • Map reduce: computing f is the map, finding b and I is the reduction.

16. Fast SVM Training and Classification on GPU • Results, compared with LibSVM

17. Fast SVM Training and Classification on GPU • Results, compared with LibSVM

18. Summary • GPU related paper outline • ** algorithm is useful and computational intensive; • GPU and CUDA is powerful; • Implement the algorithm on GPU; • Results, compared with CPU-based algorithm and others’ GPU-based algorithm. • New algorithms or better speedup. • K-means is hot; • K-nn, SVM, Apriori appeared. • What is ours focus?

19. Outline • References • Courses study • Data mining, Security, CUDA Programming • Development • Work plan

20. CUDA Programming • On-line class • Introduction • Basic • Memory • Threads • Application performance • Floating-point

21. Outline • References • Courses study • Development • Matrix multiply, read k-means and k-nn. • Work plan

22. Outline • References • Courses study • Development • Work plan

23. Work plan • Continue read the papers. • Read the code of k-means and k-nn in details. • Data mining • SVM and C4.5

24. Thanks for you listening