Just like death and taxes, one thing we can count on is that
tomorrow’s operating systems and applications will require more processing
power than today’s. And the hardware makers are more
than keeping up with the demand. Most IT professionals who have been in the
business for any length of time are intimately familiar with the effects of “Moore’s
Law,” first postulated by Gordon Moore of Intel in the 1960s, which
estimates that computer processing power roughly doubles every 18 months.

You don’t even have to upgrade to new software to need
greater and greater processing capabilities. As the sheer volume of data to be
processed grows, you need to be able to process it faster in order to get
through it all in a reasonable amount of time. So it’s almost inevitable that
you’ll be adding processing power, either by upgrading your current systems or
by replacing them with newer, faster systems, within the next few years. The
question is: what’s the most effective and cost-effective way to do that?

Faster processors vs. multiprocessing

Let’s say you have an application, such as e-mail services
or a SQL database, that needs more processing power.
The first and most obvious way to get it is to upgrade to a faster processor,
for instance, from a 2.0GHz processor to a 3.0 GHz processor. You could do that
either by replacing the processor in the server with a faster one (if the
motherboard will support it), or by replacing the entire server with one that
has a faster processor.

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The first option will cost less, but may not be an option if
the computer’s motherboard doesn’t support a faster processor than it currently
has. It will also require either having the manufacturer install the new
processor or opening up the box and installing it yourself, possibly voiding
the OEM warranty. It will obviously cost more to buy a whole new machine, but
you’ll likely get other benefits, such as support for more RAM, faster buses,
and so forth.

Whether you’re upgrading the old machine or replacing it
with a new one, buying the fastest available processor is usually much more
expensive than, for example, upgrading to a processor speed that’s just under
the current top of the line. For example, in building a new Dell Precision
recently, I found that to go from a 3.0 GHz dual core processor to a 3.20 GHz
only costs an additional $194, but going from a 3.20 GHz to a 3.40 GHz costs an
additional $270.

For this reason, you might find that you get more “bang
for your buck” by adding another processor, or purchasing a computer with
two lower speed processors. Of course, this presupposes that the machine has a
dual processor motherboard as well as an operating system that supports
multiprocessing. A computer with two 3.0 GHz processors may not be as fast as
one with a single 6.0 GHz processor, but it’s likely to outperform one with a
4.0 GHz processor (especially for certain types of tasks/applications) and be
significantly less expensive.

Another consideration when you add processors is the cost of
software licensing. Some software is licensed per-processor. Other programs are
not. If you add a processor to a system running Microsoft ISA Server, you must
pay an additional $1,499 to $5,999 per processor, depending on whether you’re
using Standard or Enterprise edition. However, you can add a many processors as
you want to a Windows Server 2003 file server without paying extra licensing
fees, although you may need to buy extra client licenses (CALs)
if the extra processing power is needed because of an increased number of users
and you’re using the “per device or user” licensing mode (formerly
known as “per seat” mode).

Multiprocessing vs. parallel processing

Whereas multiprocessing usually refers to two or more
processors installed in the same computer (which can be accomplished via
multiple separate processor units or via multiple chips in one unit or even
multiple cores on one processor die), parallel processing is more often used to
describe multiple separate computers that work together to process a particular

For example, a group of computers can be connected through a
fast Ethernet connection to make up a cluster, which is seen as a single
computer to the rest of the network. Clusters can be implemented for different
purposes. Some clusters are designed to provide fault tolerance (where another
member of the cluster takes over if the primary member fails). This type of
cluster doesn’t provide real multiprocessing. However, other types of clusters
perform load balancing, where the processing load is distributed across two or
more computers. High Performance clusters are designed to spread processing
tasks across multiple computers to improve performance. These types of clusters
can also incorporate fault tolerance.

Clustering requires some administrative overhead to
implement, and you need an operating system that supports clustering or third-party
clustering software. This may or may not result in significant software cost.
Windows Server 2003 supports load balancing clusters in all editions (Web,
Standard, Enterprise and Datacenter). Free software is available to run Linux
high performance clusters on several different Linux distros,
as well. DragonFly BSD also supports native

Distributed processing

For processing huge amounts of data, distributed systems can
be employed where many widely dispersed computers work on a problem by having
each tackle a different part of the computation. These computers can be
geographically spread out, don’t have to be running the same operating system
or working on the task at the same time. This is also referred to as grid
computing. One of the most famous examples of distributed computing is the SETI
project hosted at the University of California at Berkeley, which uses
computers all over the world to process data in search of extraterrestrial
intelligence. Computer users connected to the Internet download and install the
SETI software and their systems work on the project during otherwise idle time.

Distributed computing takes advantage of the power of
hundreds or thousands of machines working on the same problem. Disadvantages
include network reliability (or the lack thereof), security issues, and the
decentralized administration.

Planning a scalable strategy for increasing processing power

Increasing the processing power of a single machine is
inherently limited in scalability. However, if you plan ahead, you can make
sure you purchase systems with motherboards that will support an upgrade to a
faster processor later, or buy a multi-processor system with one processor and then
add additional processors as you need them, for best scalability.

Server clusters can be very scalable because you can add
more machines to spread the processing load across as your needs expand. Be
sure to look at the number of cluster nodes supported by the system you’re
planning to implement. For example, Windows Server 2003 supports up to 32
network load balancing (NLB) cluster nodes.

Distributed processing is appropriate for extremely large
scale projects where the systems do not need to work tightly together and where
security and/or loss of one or more nodes in the distributed system are not big