Parallel Computingby

Vikram Singh Slathia

Dept. Computer Science

Central University of Rajasthan

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

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.

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

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

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):

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

Conclusion

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

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

Thank you