Private:progress-gao

Spring 2011 (TA)

• Courses:
  • None.

Feb 28

• Report: here
• Hadoop's two important design features, which have great influence over the experiment's performance are examined: data placement policy and task scheduling policy.
• It is noted that, for map tasks, the scheduler uses a locality optimization technique. After selecting a job, the scheduler picks the map task in the job with data closest to the slave, on the same node if possible, otherwise on the same rack, or finally on a remote rack. For reduce tasks, the jobtracker just takes the next in the reduce tasks' list and assign it to the tasktracker.

Feb 14

• Formalized the proposed design. Analyzing why a specific family of function are used, the speedup attained, the evaluation measurement.

Feb 7

• Studied LSH and explored different LSH families according to the distance measurement they use.
• Based on how LSH methods work, analysed how these LSH methods can be parallelized in distributed environment.

Jan 31

• Examined how the main class clustering algorithms can be parallelized, respectively.
• Explained spectral clustering from theoretical aspect, from graph cut viewpoint.
• Based on the understanding of main clustering algorithms, proposed optimizing method for spectral clustering to deal with large data set.
• The proposed method makes use of LSH to do pre-precessing.

Jan 17

• Understanding Spectral clustering and distributed implementation.
• Mahout experimenting.

Jan 10

• Survey on main clustering algorithms and the distributed map-reduce method of these algorithms.
• Mahout experimenting.

Fall 2010 (FELLOWSHIP)

• Courses:
  • CMPT 771: Internet Architecture and Protcols
  • CMPT 741: Data Mining

• Worked on efficient approximation of gram matrix using map-reduce framework, focusing on LSH performance evaluation and network communication measurement.

Summer 2010 (RA)

• Courses:
  • None

• Worked on Approximation of gram matrices using Locality Sensitive Hashing on Cluster.

Spring 2010 (TA+RA)

• Courses:
  • CMPT 886: Special Topics in Operating Systems and Computer Architecture

• Worked on Band approximation of gram matrices (large high-dimensional dataset) using Hilbert curve on multicore.

Fall 2009 (TA)

• Courses:
  • CMPT 705: Design and Analysis of Algorithms
  • CMPT 726: Machine Learning

• Worked on Band approximation of gram matrices (large high-dimensional dataset) using Hilbert curve on multicore.