Parallel Computing :: Overview  

 

 


What is Parallel Computing?

  1. Traditionally, software has been written for serial computation:
    • To be executed by a single computer having a single Central Processing Unit (CPU);
    • Problems are solved by a series of instructions, executed one after the other by the CPU. Only one instruction may be executed at any moment in time.


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

  3. The compute resources can include:
    • A single computer with multiple processors;
    • An arbitrary number of computers connected by a network;
    • A combination of both.

  4. 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.

  5. 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. Some examples:
    • Planetary and galactic orbits
    • Weather and ocean patterns
    • Tectonic plate drift
    • Rush hour traffic in LA
    • Automobile assembly line
    • Daily operations within a business
    • Building a shopping mall
    • Ordering a hamburger at the drive through.

  6. Traditionally, parallel computing has been considered to be "the high end of computing" and has been motivated by numerical simulations of complex systems and "Grand Challenge Problems" such as:
    • weather and climate
    • chemical and nuclear reactions
    • biological, human genome
    • geological, seismic activity
    • mechanical devices - from prosthetics to spacecraft
    • electronic circuits
    • manufacturing processes

  7. Today, commercial applications are providing 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. Example applications include:
    • parallel databases, data mining
    • oil exploration
    • web search engines, web based business services
    • computer-aided diagnosis in medicine
    • management of national and multi-national corporations
    • advanced graphics and virtual reality, particularly in the entertainment industry
    • networked video and multi-media technologies
    • collaborative work environments

  8. Ultimately, parallel computing is an attempt to maximize the infinite but seemingly scarce commodity called time.

Why Use Parallel Computing?

  1. There are two primary reasons for using parallel computing:
    • Save time - wall clock time
    • Solve larger problems

  2. Other reasons might include:
    • Taking advantage of non-local resources - using available compute resources on a wide area network, or even the Internet when local compute resources are scarce.
    • Cost savings - using multiple "cheap" computing resources instead of paying for time on a supercomputer.
    • Overcoming memory constraints - single computers have very finite memory resources. For large problems, using the memories of multiple computers may overcome this obstacle.

  3. 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.

  4. The future: during the past 10 years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) suggest that parallelism is the future of computing.
NEXT:Concept & Terminology

 
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