parallel processing

parallel processing, the concurrent or simultaneous execution of two or more parts of a single computer program, at speeds far exceeding those of a conventional computer. Parallel processing requires two or more interconnected processors, each of which executes a portion of the task; some supercomputer parallel-processing systems have hundreds of thousands of microprocessors. The processors access data through shared memory. The efficiency of parallel processing is dependent upon the development of programming languages that optimize the division of the tasks among the processors.

Bibliography

See E. Rietman, Exploring Parallel Processing (1990); K. M. Chandy and S. Taylor, An Introduction to Parallel Programming (1992); D. I. Moldovon, Parallel Processing from Applications to Systems (1993); G. S. Almasi and A. Gottlieb, Highly Parallel Computing (1993).

---

(1) An architecture within a single computer that performs more than one operation at the same time. See pipeline processing, array processor and vector processor.

(2) An architecture using multiple computers. See parallel computing.

---

(parallel)

parallel processing - (Or "multiprocessing") The simultaneous use of more than one computer to solve a problem. There are many different kinds of parallel computer (or "parallel processor"). They are distinguished by the kind of interconnection between processors (known as "processing elements" or PEs) and between processors and memory. Flynn's taxonomy also classifies parallel (and serial) computers according to whether all processors execute the same instructions at the same time ("single instruction/multiple data" - SIMD) or each processor executes different instructions ("multiple instruction/multiple data" - MIMD). The processors may either communicate in order to be able to cooperate in solving a problem or they may run completely independently, possibly under the control of another processor which distributes work to the others and collects results from them (a "processor farm"). The difficulty of cooperative problem solving is aptly demonstrated by the following dubious reasoning: If it takes one man one minute to dig a post-hole then sixty men can dig it in one second. Amdahl's Law states this more formally. Processors communicate via some kind of network or bus or a combination of both. Memory may be either shared memory (all processors have equal access to all memory) or private (each processor has its own memory - "distributed memory") or a combination of both. Many different software systems have been designed for programming parallel computers, both at the operating system and programming language level. These systems must provide mechanisms for partitioning the overall problem into separate tasks and allocating tasks to processors. Such mechanisms may provide either implicit parallelism - the system (the compiler or some other program) partitions the problem and allocates tasks to processors automatically or explicit parallelism where the programmer must annotate his program to show how it is to be partitioned. It is also usual to provide synchronisation primitives such as semaphores and monitors to allow processes to share resources without conflict. Load balancing attempts to keep all processors busy by allocating new tasks, or by moving existing tasks between processors, according to some algorithm. Communication between tasks may be either via shared memory or message passing. Either may be implemented in terms of the other and in fact, at the lowest level, shared memory uses message passing since the address and data signals which flow between processor and memory may be considered as messages. The terms "parallel processing" and "multiprocessing" imply multiple processors working on one task whereas "concurrent processing" and "multitasking" imply a single processor sharing its time between several tasks. See also cellular automaton,symmetric multi-processing. Usenet newsgroup: news:comp.parallel. Institutions, research groups.