Parallel processing, the method of having many small tasks solve one large problem,
has emerged as a key enabling technology in modern computing. The past several
years have witnessed an ever-increasing acceptance and adoption of parallel processing,
both for high-performance scientific computing and for more "general-purpose"
applications, was a result of the demand for higher performance, lower cost, and
sustained productivity. The acceptance has been facilitated by two major developments:
massively parallel processors (MPPs) and the widespread use of distributed computing.
MPPs are now the most powerful computers in the world. These machines combine
a few hundred to a few thousand CPUs in a single large cabinet connected to hundreds
of gigabytes of memory. MPPs offer enormous computational power and are used to
solve computational Grand Challenge problems such as global climate modeling and
drug design. As simulations become more realistic, the computational power required
to produce them grows rapidly. Thus, researchers on the cutting edge turn to MPPs
and parallel processing in order to get the most computational power possible.
The second major development affecting scientific problem solving is distributed
computing. Distributed computing is a process whereby a set of computers connected
by a network are used collectively to solve a single large problem. As more and
more organizations have high-speed local area networks interconnecting many general-purpose
workstations, the combined computational resources may exceed the power of a single
high-performance computer. In some cases, several MPPs have been combined using
distributed computing to produce unequaled computational power. The most important
factor in distributed computing is cost. Large MPPs typically cost more than $10
million. In contrast, users see very little cost in running their problems on
a local set of existing computers. It is uncommon for distributed-computing users
to realize the raw computational power of a large MPP, but they are able to solve
problems several times larger than they could use one of their local computers.
Common between distributed computing and MPP is the notion of message passing.
In all parallel processing, data must be exchanged between cooperating tasks.
Several paradigms have been tried including shared memory, parallelizing compilers,
and message passing. The message-passing model has become the paradigm of choice,
from the perspective of the number and variety of multiprocessors that support
it, as well as in terms of applications, languages, and software systems that
Virtual Machine (PVM) system described in this book uses the message passing model
to allow programmers to exploit distributed computing across a wide variety of
computer types, including MPPs. A key concept in PVM is that it makes a collection
of computers appear as one large virtual machine, hence its name.
Heterogeneous Network Computing
an MPP, every processor is exactly like every other in capability, resources,
software, and communication speed. Not so on a network. The computers available
on a network may be made by different vendors or have different compilers. Indeed,
when a programmer wishes to exploit a collection of networked computers, he may
have to contend with several different types of heterogeneity:
" computational speed
" machine load
computing offers many advantages:
By using existing hardware, the cost of this computing can be very low.
Performance can be optimized by assigning each individual task to the most appropriate
" One can exploit the heterogeneous nature of a computation.
Heterogeneous network computing is not just a local area network connecting workstations
together. For example, it provides access to different data bases or to special
processors for those parts of an application that can run only on a certain platform.
The virtual computer resources can grow in stages and take advantage of the latest
computational and network technologies.
" Program development can be enhanced
by using a familiar environment. Programmers can use editors, compilers, and debuggers
that are available on individual machines.
" The individual computers
and workstations are usually stable, and substantial expertise in their use is
" User-level or program-level fault tolerance can be
implemented with little effort either in the application or in the underlying
" Distributed computing can facilitate collaborative
All these factors translate into reduced development and debugging
time, reduced contention for resources, reduced costs, and possibly more effective
implementations of an application. It is these benefits that PVM seeks to exploit.
From the beginning, the PVM software package was designed to make programming
for a heterogeneous collection of machines straightforward
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