1. Weekly Report Ph.D. Student: Leo Lee date: Oct. 9, 2009

2. Outline Courses Research Work plan

3. Outline Courses Research Work plan

4. Courses Data mining –Homework; –Hidden Markov Model –Read the most classical tutorial; Forward-backward procedure; Viterbi algorithm;

5. Courses Network security –Check the homework; –Modify the tutorial for next week; Learn C# ; Dev. an easy chat application.

6. Outline Courses Research Work plan

7. Research

8. Mars: A MapReduce Framework on Graphics Processors Introduction –For search engines and other web server applications, high performance is essential. –The MapReduce framework is a successful paradigm to support such data processing applications, which reduces the complexity of parallel programming. –Encouraged by the success of the CPU-based MapReduce frameworks, we develop Mars, a MapReduce framework on graphics processors, or GPUs.

9. Mars: A MapReduce Framework on Graphics Processors Introduction –Since GPUs are traditionally designed as special-purpose co- processors for gaming applications, their languages lack support for some basic programming constructs. variable-length data types; more complex functions such as recursion. –GPU architectural details are highly vendor-specific and programmers have limited access to these details. –All these factors make the GPU programming a difficult task in general and more so for complex tasks such as web data analysis. Therefore, we propose to develop a MapReduce framework on the GPU so that programmers can easily harness the GPU computation power for their data processing tasks.

10. Mars: A MapReduce Framework on Graphics Processors Introduction –First, the synchronization overhead must be low so that the system can scale to hundreds of processors. –Second, due to the lack of dynamic thread scheduling on current GPUs, it is essential to allocate work evenly across threads on the GPU to exploit its massive thread parallelism. –Third, the core tasks of MapReduce programs, including string processing, file manipulation and concurrent reads and writes, are unconventional to GPUs and must be handled efficiently.

11. Mars: A MapReduce Framework on Graphics Processors Preliminaries and overview –GPUs –GPGPU –MapReduce

12. Mars: A MapReduce Framework on Graphics Processors Design and implementation –Ease of programming. Ease of programming encourages developers to use the GPU for their tasks. –Performance. The overall performance of our GPU-based MapReduce should be comparable to or better than that of the state- of-the-art CPU counterparts.

13. Mars: A MapReduce Framework on Graphics Processors Design and implementation-APIs –User-implemented

14. Mars: A MapReduce Framework on Graphics Processors Design and implementation-APIs –System-provided

15. Mars: A MapReduce Framework on Graphics Processors System Workflow and Configuration

16. Mars: A MapReduce Framework on Graphics Processors Optimization Techniques –Coalesced accesses –Accesses using built-in vector types: char4 and int4?

17. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

18. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

19. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

20. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

21. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

22. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

23. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

24. Mars: A MapReduce Framework on Graphics Processors Experimental evaluation

25. Mars: A MapReduce Framework on Graphics Processors

26. Outline Courses Research Work plan

27. Work Plan Go on paper reading Learn more CUDA applications Work hard on data mining, try to implement some classical algorithm Learn C#

28. Thanks for your listening