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<H1><A NAME=SECTION02210000000000000000>1.1 Parallelism and Computing</A></H1>
<P>
<A NAME=280> </A>
A <i> parallel computer</i> is a set of processors that are able to work
cooperatively to solve a computational problem. This definition is
broad enough to include parallel supercomputers that have hundreds or
thousands of processors, networks of workstations, multiple-processor
workstations, and embedded systems. Parallel computers are
interesting because they offer the potential to concentrate
computational resources---whether processors, memory, or I/O
bandwidth---on important computational problems.
<P>
Parallelism has sometimes been viewed as a rare and exotic subarea of
computing, interesting but of little relevance to the average
programmer. A study of trends in applications, computer architecture,
and networking shows that this view is no longer tenable. Parallelism
is becoming ubiquitous, and parallel programming is becoming central to the
programming enterprise.
<P>
<H2><A NAME=SECTION02211000000000000000>1.1.1 Trends in Applications</A></H2>
<P>
<A NAME=283> </A>
<P>
As computers become ever faster, it can be tempting to suppose that
<A NAME=284> </A>
they will eventually become ``fast enough'' and that appetite for
increased computing power will be sated. However, history suggests
that as a particular technology satisfies known applications, new
applications will arise that are enabled by that technology and that
will demand the development of new technology. As an amusing
illustration of this phenomenon, a report prepared for the British
government in the late 1940s concluded that Great Britain's
computational requirements could be met by two or perhaps three
computers. In those days, computers were used primarily for computing
ballistics tables. The authors of the report did not consider other
applications in science and engineering, let alone the commercial
applications that would soon come to dominate computing. Similarly,
the initial prospectus for Cray Research predicted a market for ten
supercomputers; many hundreds have since been sold.
<P>
Traditionally, developments at the high end of computing have been
motivated by numerical simulations of complex systems such as weather,
<A NAME=285> </A>
climate, mechanical devices, electronic circuits, manufacturing
<A NAME=286> </A>
processes, and chemical reactions. However, the most significant
forces driving the development of faster computers today are emerging
commercial applications that require a computer to be able to process
large amounts of data in sophisticated ways. These applications
<A NAME=287> </A>
include video conferencing, collaborative work environments,
<A NAME=288> </A>
computer-aided diagnosis in medicine, parallel databases used for
<A NAME=289> </A>
decision support, and advanced graphics and virtual reality,
<A NAME=290> </A>
particularly in the entertainment industry. For example, the
integration of parallel computation, high-performance networking, and
multimedia technologies is leading to the development of <em> video
<A NAME=291> </A>
servers,</em> computers designed to serve hundreds or thousands of
simultaneous requests for real-time video. Each video stream can
involve both data transfer rates of many megabytes per second and
large amounts of processing for encoding and decoding. In graphics,
three-dimensional data sets are now approaching <IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img79.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img79.gif"> volume elements
(1024 on a side). At 200 operations per element, a display updated 30
times per second requires a computer capable of 6.4<IMG BORDER=0 ALIGN=MIDDLE ALT="" SRC="img80.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img80.gif">
operations per second.
<P>
Although commercial applications may define the architecture of most
future parallel computers, traditional scientific applications will
remain important users of parallel computing technology. Indeed, as
nonlinear effects place limits on the insights offered by purely
theoretical investigations and as experimentation becomes more costly
or impractical, computational studies of complex systems are becoming
ever more important. Computational costs typically increase as the
fourth power or more of the ``resolution'' that determines accuracy,
so these studies have a seemingly insatiable demand for more computer
power. They are also often characterized by large memory and
input/output requirements. For example, a ten-year simulation of the
earth's climate using a state-of-the-art model may involve <IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img81.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img81.gif">
<A NAME=294> </A>
floating-point operations---ten days at an execution speed of
<IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img82.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img82.gif"> floating-point operations per second (10 gigaflops). This
same simulation can easily generate a hundred gigabytes (<IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img83.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img83.gif">
bytes) or more of data. Yet as Table <A HREF="node7.html#tabchammp" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node7.html#tabchammp">1.1</A> shows,
scientists can easily imagine refinements to these models that would
increase these computational requirements 10,000 times.
<P>
<P><A NAME=772> </A><IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img88.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img88.gif">
<BR><STRONG>Table 1.1:</STRONG> Various refinements proposed to climate models, and
the increased computational requirements associated with these
refinements. Altogether, these refinements could increase
computational requirements by a factor of between <IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img86.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img86.gif"> and
<IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img87.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img87.gif">.
<A NAME=tabchammp> </A><BR>
<P>
<P>
In summary, the need for faster computers is driven by the demands of
both data-intensive applications in commerce and computation-intensive
applications in science and engineering. Increasingly, the
requirements of these fields are merging, as scientific and
engineering applications become more data intensive and commercial
applications perform more sophisticated computations.
<P>
<H2><A NAME=SECTION02212000000000000000>1.1.2 Trends in Computer Design</A></H2>
<P>
<A NAME=313> </A>
<P>
The performance of the fastest computers has grown exponentially from
<A NAME=314> </A>
1945 to the present, averaging a factor of 10 every five years.
While the first computers performed a few tens of floating-point
<A NAME=315> </A>
operations per second, the parallel computers of the mid-1990s achieve
tens of billions of operations per second (Figure <A HREF="node7.html#figgrowth" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node7.html#figgrowth">1.1</A>).
Similar trends can be observed in the low-end computers of different
eras: the calculators, personal computers, and workstations. There is
little to suggest that this growth will not continue. However, the
computer architectures used to sustain this growth are changing
radically---from sequential to parallel.
<P>
<P><A NAME=797> </A><IMG BORDER=0 ALIGN=BOTTOM ALT="" SRC="img89.gif" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/img89.gif">
<BR><STRONG>Figure 1.1:</STRONG> <em> Peak performance of some of the fastest supercomputers,
1945--1995. The exponential growth flattened off somewhat in the
1980s but is accelerating again as massively parallel
supercomputers become available. Here, ``o'' are uniprocessors, ``+''
denotes modestly parallel vector computers with 4--16 processors, and
``x'' denotes massively parallel computers with hundreds or thousands
of processors. Typically, massively parallel computers achieve a
lower proportion of their peak performance on realistic applications
than do vector computers.</em><A NAME=figgrowth> </A><BR>
<P>
<P>
<A NAME=321> </A>
The performance of a computer depends directly on the time required to
perform a basic operation and the number of these basic operations
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