1. Parallel Computing
by
Vikram Singh Slathia
Dept. Computer Science
Central University of Rajasthan

2. Parallel Processing is a term used to denote a
large class of techniques that are used to
provide simultaneous data processing tasks for
the purpose of
• Save time and/or money
• Solve larger problems
Parallel computing is the simultaneous
use of multiple compute resources to solve a
computational problem

3. The Universe is Parallel
• Galaxy formation
• Planetary movement
• Weather and ocean patterns
• Tectonic plate drift
• Rush hour traffic
• Automobile assembly line
• Building a jet
• Ordering a hamburger
at the drive through.

4. Areas of Parallel Computing
• Physics – applied, nuclear, particle, condensed matter, high
pressure, fusion, photonics
• Bioscience, Biotechnology, Genetics
• Chemistry, Molecular Sciences
• Geology, Seismology
• Mechanical Engineering - from prosthetics to spacecraft
• Electrical Engineering, Circuit Design, Microelectronics
• Computer Science, Mathematics

5. Why Use Parallel
Computing?
• Save time and/or money: In theory, throwing more resources at a task
will shorten its time to completion, with potential cost savings. Parallel
computers can be built from cheap, commodity components.
• Solve larger problems: Many problems are so large and/or complex
that it is impractical or impossible to solve them on a single computer, especially
given limited computer memory.
• Better response times: As the computing tasks are engaged by a group
of processors, the tasks are completed in a smaller amount of time

6. ways to classify parallel
computers.
• One of the more widely used classifications, in
use since 1966, is called Flynn's Taxonomy
The 4 possible classifications according to Flynn’s
are :
• Single Instruction, Single Data (SISD)
• Single Instruction, Multiple Data (SIMD)
• Multiple Instruction, Single Data (MISD)
• Multiple Instruction, Multiple Data (MIMD):

7. Some basic requirements for
achieving parallel execution
• Operating system capable of managing the
multiple processors.
• Computer system/servers with built in multiple
processors and better message facilitation
among processors.
• Clustered nodes with application software,
such as Oracle RAC

8. Conclusion
• Parallel computing is fast.
• Parallel computing is the future of computing.

9. References
Books
• The New Turing Omnibus, A. K. Dewdney, Henry Holt and Company, 1993
• Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw
Hill Higher Education, 2003
• Introduction to Parallel Computing 2nd Edition , Ananth Grama , Pearson
Links
• Parallel Processing,
http://www.dba-oracle.com/real_application_clusters_rac_grid/parallel.html
• Internet Parallel Computing Archive,
• wotug.ukc.ac.uk/parallel
• Introduction to Parallel Computing,
www.llnl.gov/computing/tutorials/parallel_comp/#Whatis

10. Thank you