weekly report ph.d. student: leo lee date: oct. 9, 2009

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