Introduction:

The goal of research at the Parallel and Distributed Computing Laboratory (PaDiC Lab) is to develop a computing system and architecture which can adapt to large-scale dynamic computing environments, facilitate  application development, and improve runtime performance as well.  The members of the PADIC Lab are also interested in recent technology advances in computer science related fields, such as e-science and e-social-science. The current research direction in the PaDiC Lab focuses on three topics:

• Large-scale distributed programming technology to facilitate worldwide-scale application development

• Grid/Cloud Computing

• Generic distributed scientific computing framework

• Peer-to-Peer systems

Projects:

Worldwide Computing Environment (sponsored by NSC, Taiwan, NSC97-2221-E-008-046- and NSC98-2221-E008-080-):
The purpose of this research is to turn the Internet into a unified, worldwide computing environment, which harnesses under-loaded computing resources by the technique of dynamic system reconfiguration in a peer-to-peer manner. Our worldwide computing environment research uses the following toolkits: (1) Condor, (2) Ganglia, (3) Message Passing Interface, (4) Java Programming Language, and (5) the Simple Actor Language, System, and Architecture (SALSA) The Worldwide Computing Laboratory at Rensselaer Polytechnic Institute in the US is our major collaboration partner in this project.  

Large-Scale Scientific Computing Application, System, and Architecture in Astronomy:
PANoramic Survey Telescope And Rapid Response System (Pan-STARRS) is a planned astronomical survey that will conduct astrometry and photometry of 3/4 of the entire sky every night. The major goal of Pan-STARRS is to detect potentially hazardous objects, in particular the near-earth objects in the Solar System. PS1, essentially one quarter of Pan-STARRS, is the prototype of the Pan-STARRS, and is also expected to have significant contribution in many areas of astronomy research. Pan-STARRS is currently operated by the University of Hawaii, which has set up an international consortium comprised of research groups in Germany, the USA, the United Kingdom, and Taiwan. The observed data from PS1 will be about 2-3 Terabytes per night which takes a long time to finish data analysis. Therefore advanced computing technology,  especially grid/p2p computing technology, is required to improve performance. Furthermore, many astronomy applications may also utilize the data from PS1 to do their own analysis, which means there exist many research potentials for grid computing in this area. The PaDiC Lab is collaborating with the Graduate Institute of Astronomy at National Central University, Taiwan, on PS1 related research.

Courses:

• Fall 2008:
  • (CE1001A) Introduction to Computer Science I, (CE1003A) Introduction to Computer Science Lab I, (CE5049) Introduction to Grid Computing.
• Spring 2008:
  • (CE1002A) Introduction to Computer Science II, (CE1004A) Introduction to Computer Science Lab II, (CE3002-A) Operating System.
• Fall 2008:
  • (CE1001A) Introduction to Computer Science I, (CE1003A) Introduction to Computer Science Lab I, (CE5049) Introduction to Grid Computing.
• Spring 2008:
  • (CE1002A) Introduction to Computer Science II, (CE1004A) Introduction to Computer Science Lab II, (CE6132) Advanced Distributed Computing Models.
• Fall 2007:
  • (CE1001A) Introduction to Computer Science I, (CE1003A) Introduction to Computer Science Lab I, (CE5049) Introduction to Grid Computing.

Publication List:

The information can be obtained at Wei-Jen Wang's publication list.

Alumni:

Chien-Hao Chiu (Master Student, 98G)
Ssu-Liang Shen (Master Student, 98G)
Ting-Yuan Song (Master Student, 98G)
Cheng-Hui Wu (Master Student, 98G)