1. Weekly Report Start learning GPU Ph.D. Student: Leo Lee date: 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=Por tal&dl=GUIDE&CFID=52122012&CFTOKEN=42909759http://portal.acm.org/citation.cfm?id=1579193.1579654&coll=Por tal&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=Por tal&dl=GUIDE&CFID=53240258&CFTOKEN=63251930http://portal.acm.org/citation.cfm?id=1547557.1548166&coll=Por tal&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=Por tal&dl=GUIDE&CFID=53246314&CFTOKEN=25986930http://portal.acm.org/citation.cfm?id=1390156.1390170&coll=Por tal&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. 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. Accelerating K-Means on the Graphics Processor via CUDA Introduction –Massive data, K-Means, CUDA Related work

11. Accelerating K-Means on the Graphics Processor via CUDA Contribution –Parallel implementation; –Large scale; –Investigation of precision; –Evaluation of on chip memory throughput and floating point operation performance.

12. 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.

13. Fast SVM Training and Classification on GPU C-SVM –SVM Training –SMO

14. Fast SVM Training and Classification on GPU

15. SVM Classification Fast SVM Training and Classification on GPU

16. 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 Fast SVM Training and Classification on GPU

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

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

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

20. 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?

21. Outline References Courses study –Data mining, Security, CUDA Programming Development Work plan

22. CUDA Programming On-line class –Introduction –Basic –Memory –Threads –Application performance –Floating-point

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

24. Outline References Courses study Development Work plan

25. Continue read the papers. Read the code of k-means and k-nn in details. Data mining –SVM and C4.5

26. Thanks for you listening