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Why use Parallel computing?

Background

Traditionally, software has been written for serial computation:

* To be run on a single computer having a single Central Processing Unit (CPU);

* A problem is broken into a discrete series of instructions.

* Instructions are executed one after another.

* Only one instruction may execute at any moment in time.

In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem:

* To be run using multiple CPUs

* A problem is broken into discrete parts that can be solved concurrently

* Each part is further broken down to a series of instructions

* Instructions from each part execute simultaneously on different CPUs

 

Parallel Computing

The compute resources can include:

* A single computer with multiple processors;

* An arbitrary number of computers connected by a network;

* A combination of both.

The computational problem usually demonstrates characteristics such as the ability to be:

* Broken apart into discrete pieces of work that can be solved simultaneously;

* Execute multiple program instructions at any moment in time;

* Solved in less time with multiple compute resources than with a single compute resource.

The Universe is Parallel:

Parallel computing is an evolution of serial computing that attempts to emulate what has always been the state of affairs in the natural world: many complex, interrelated events happening at the same time, yet within a sequence. For example:

* Galaxy formation

* Planetary movement

* Weather and ocean patterns

* Tectonic plate drift

* Rush hour traffic

* Automobile assembly line

* Building a space shuttle

* Ordering a hamburger at the drive through.

Uses for Parallel Computing:

Historically, parallel computing has been considered to be "the high end of computing", and has been used to model difficult scientific and engineering problems found in the real world. Some examples:

* Atmosphere, Earth, Environment

* 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

 

Different applications

Today, commercial applications provide an equal or greater driving force in the development of faster computers. These applications require the processing of large amounts of data in sophisticated ways. For example:

* Databases, data mining

* Oil exploration

* Web search engines, web based business services

* Medical imaging and diagnosis

* Pharmaceutical design

* Management of national and multi-national corporations

* Financial and economic modeling

* Advanced graphics and virtual reality, particularly in the entertainment industry

* Networked video and multi-media technologies



* Collaborative work environments

Why use Parallel computing?

Main Reasons:

a. - Save time and/or money: In theory, throwing more resources at a task will shorten its time to completion, with potential cost savings. Parallel clusters can be built from cheap, commodity components.

 

b. - 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. For example:

* "Grand Challenge" (en.wikipedia.org/wiki/Grand_Challenge) problems requiring PetaFLOPS and PetaBytes of computing resources.

* Web search engines/databases processing millions of transactions per second

c. - Provide concurrency: A single compute resource can only do one thing at a time. Multiple computing resources can be doing many things simultaneously. For example, the Access Grid (www.accessgrid.org) provides a global collaboration network where people from around the world can meet and conduct work "virtually".

d. - Use of non-local resources: Using compute resources on a wide area network, or even the Internet when local compute resources are scarce. For example:

* SETI@home (setiathome.berkeley.edu) uses over 330,000 computers for a compute power over 528 TeraFLOPS (as of August 04, 2008)

* Folding@home (folding.stanford.edu) uses over 340,000 computers for a compute power of 4.2 PetaFLOPS (as of November 4, 2008)

e. - Limits to serial computing: Both physical and practical reasons pose significant constraints to simply building ever faster serial computers:

* Transmission speeds - the speed of a serial computer is directly dependent upon how fast data can move through hardware. Absolute limits are the speed of light (30 cm/nanosecond) and the transmission limit of copper wire (9 cm/nanosecond). Increasing speeds necessitate increasing proximity of processing elements.

* Limits to miniaturization - processor technology is allowing an increasing number of transistors to be placed on a chip. However, even with molecular or atomic-level components, a limit will be reached on how small components can be.

* Economic limitations - it is increasingly expensive to make a single processor faster. Using a larger number of moderately fast commodity processors to achieve the same (or better) performance is less expensive.

Decision: Current computer architectures are increasingly relying upon hardware level parallelism to improve performance:

* Multiple execution units

* Pipelined instructions

* Multi-core


Date: 2016-03-03; view: 171


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