by Burleson Consulting
The Data Warehouse Development Life Cycle
Parallelism And Oracle Data Warehousing
Before we get too deep into our discussion of parallel processing, a
distinction needs to be made between multitasking and
multiprocessing. Multitasking refers to the ability of a software
package to manage multiple concurrent processes, thereby allowing
parallel processing. OS/2 and Windows-NT are examples of this
technology, but multitasking is found within all midrange and
mainframe databases. Multiprocessing, on the other hand, refers to
the use of multiple CPUs within a distributed environment where a
master program directs parallel operations against numerous
machines. There are two areas of multiprocessing: hardware and
software. At the hardware level, arrays of CPUs are offered, and at
the software level, a single CPU is partitioned into separate
"logical" processors. The Prism software in the IBM mainframe
environment is an example of multiprocessing technology.
In any case, programming for multiprocessors is quite different from
programming for linear systems. Multiprocessing programming falls
into two arenas: data parallel programming and control parallel
programming. Indata parallel programming the data is partitioned
into discrete pieces and the same program is run in parallel against
each piece (see Figure 7.1). Data parallel can be used in any type
of Oracle environment, including uniprocessor, symmetric
multiprocessor (SMP), or massively parallel processor (MPP)
environments. With control parallel programming, independent
functions are identified and independent CPUs are used to
simultaneously solve each independent function. Control parallel is
generally associated with MPP environments.
Figure 7.1 An example of parallel processing.
One of the greatest problems faced by developers when implementing
parallel processing systems is the identification of parallelism.
Parallelism refers to the ability of a computer system to perform
processing on two data sources at the same time. Whereas many of the
traditional database applications were linear in nature, today's
systems have ample opportunities for parallel processing.
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