Published on Apr 02, 2024
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 use it.
The Parallel 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.
In 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:
" architecture
" data format
" computational speed
" machine load
" network load
" 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 architecture.
" 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 readily available.
" User-level or program-level fault tolerance can be implemented with little effort either in the application or in the underlying operating system.
" Distributed computing can facilitate collaborative work.
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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