by Burleson Consulting
The Data Warehouse Development Life Cycle
Parallelism And Oracle Data Warehousing
For the most part, parallelism is an issue of scale--not of
speed, per se. Some think that if a linear process can solve a
problem in one hour, then a parallel system with 60 processors
should be able to solve the problem in one minute. This is analogous
to stating that because it takes nine months to have a baby, then
nine women should be able to produce a baby in one month. Clearly,
while there are many separate processes in having a baby (zygote,
fetus, infant), each of these tasks requires the completion of the
previous task. Traditional linear systems (systems in which one
process may not begin until the preceding one ends) will not benefit
from parallel processing.
On the other hand, parallel processing can return enormous benefits
for large databases that are not designed as traditional linear
systems. In other words, a query against a very large database can
be dramatically improved if the data is partitioned and,
simultaneously, each partitioned is accessed by a process. For
example, if a query against a text database takes one minute to scan
a terabyte, the partitioning of the data and processing into 60
pieces will result in a retrieval time of one second. There is also
the issue of balancing the CPU processing with the I/O processing.
In a traditional data processing environment, the systems are not
computationally intensive, and most of the elapsed time is spent
waiting on I/O. However, this does not automatically exclude
business systems from taking advantage of multiprocessing.
Parallelism is especially effective in scientific applications that
can benefit from having hundreds or even thousands of processors
working together to solve a problem. Basically, parallelism can
dramatically reduce the response time for any query that can be
split into sub-queries and each sub-query assigned to a processor
(see Figure 7.2).
Figure 7.2 Speed reduction with parallel processors.
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