Parallel operating systems are a type of computer processing platform that breaks large tasks into smaller pieces that are done at the same time in different places and by different mechanisms. They are sometimes also described as “multi-core” processors. This type of system is usually very efficient at handling very large files and complex numerical codes. It’s most commonly seen in research settings where central server systems are handling a lot of different jobs at once, but can be useful any time multiple computers are doing similar jobs and connecting to shared infrastructures simultaneously. They can be difficult to set up at first and can require a bit of expertise, but most technology experts agree that, over the long term, they’re much more cost effective and efficient than their single-computer counterparts.
The Basics of Parallel Computing
A parallel operating system works by dividing sets of calculations into smaller parts and distributing them between the machines on a network. To facilitate communication between the processor cores and memory arrays, routing software has to either share its memory by assigning the same address space to all of the networked computers, or distribute its memory by assigning a different address space to each processing core. Sharing memory allows the operating system to run very quickly, but it is usually not as powerful. When using distributed shared memory, processors have access to both their own local memory and the memory of other processors; this distribution may slow the operating system, but it is often more flexible and efficient.
The architecture of the software is typically build around a UNIX-based platform, which allows it to coordinate distributed loads between multiple computers in a network. Parallel systems are able to use software to manage all of the different resources of the computers running in parallel, such as memory, caches, storage space, and processing power. These systems also allow a user to directly interface with all of the computers in the network.
Origins and First Uses
In 1967, Gene Amdahl, an American computer scientist working for IBM, conceptualized the idea of using software to coordinate parallel computing. He released his findings in a paper called Amdahl's Law, which outlined the theoretical increase in processing power one could expect from running a network with a parallel operating system. His research led to the development of packet switching, and thus to the modern parallel operating system. This development of packet switching is widely regarded as the breakthrough that later started the "Arpanet Project," which is responsible for the basic foundation of the Internet, the world's largest parallel computer network.
Modern Applications
Most fields of science use this sort of operating system, including biotechnology, cosmology, theoretical physics, astrophysics, and computer science. The complexity and capacity of these systems can also help create efficiency in such industries as consulting, finance, defense, telecom, and weather forecasting. In fact, parallel computing has become so robust that it has been used by many of the leading cosmologists to answer questions about the origin of the universe. Scientists were able to run simulations of large sections of space all at once. It only took one month for scientists to compile a simulation of the formation of the Milky Way using this sort of operating system, for instance, a feat previously thought to be impossible because of how complex and cumbersome it is.
Cost Considerations
Scientists, researches, and industry leaders often choose to use these sorts of operating systems primarily because of their efficiency, but cost is usually a factor, too. In general, it costs far less to assemble a parallel computer network than it would cost to develop and build a super computer for research — or to invest in numerous smaller computers and divide the work. Parallel systems are also completely modular, which in most cases allows for inexpensive repairs and upgrades.
