weekly report ph.d. student: leo lee date: oct. 9, 2009
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Weekly Report
Ph.D. Student: Leo Leedate: Oct. 9, 2009
Outline
• Courses
• Research
• Work plan
Outline
• Courses
• Research
• Work plan
Courses
• Data mining– Homework;
– Hidden Markov Model
– Read the most classical tutorial;• Forward-backward procedure;• Viterbi algorithm;
Courses
• Network security– Check the homework;
– Modify the tutorial for next week;• Learn C# ;• Dev. an easy chat application.
Outline
• Courses
• Research
• Work plan
Research
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.
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.
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.
Mars: A MapReduce Framework on Graphics Processors
• Preliminaries and overview– GPUs
– GPGPU
– MapReduce
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.
Mars: A MapReduce Framework on Graphics Processors
• Design and implementation-APIs– User-implemented
Mars: A MapReduce Framework on Graphics Processors
• Design and implementation-APIs– System-provided
Mars: A MapReduce Framework on Graphics Processors
• System Workflow and Configuration
Mars: A MapReduce Framework on Graphics Processors
• Optimization Techniques– Coalesced accesses
– Accesses using built-in vector types: char4 and int4?
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
• Experimental evaluation
Mars: A MapReduce Framework on Graphics Processors
Outline
• Courses
• Research
• Work plan
Work Plan
• Go on paper reading
• Learn more CUDA applications
• Work hard on data mining, try to implement some classical algorithm
• Learn C#
• Thanks for your listening