Multiple Parallel Processing - significado y definición. Qué es Multiple Parallel Processing
Diclib.com
Diccionario ChatGPT
Ingrese una palabra o frase en cualquier idioma 👆
Idioma:

Traducción y análisis de palabras por inteligencia artificial ChatGPT

En esta página puede obtener un análisis detallado de una palabra o frase, producido utilizando la mejor tecnología de inteligencia artificial hasta la fecha:

  • cómo se usa la palabra
  • frecuencia de uso
  • se utiliza con más frecuencia en el habla oral o escrita
  • opciones de traducción
  • ejemplos de uso (varias frases con traducción)
  • etimología

Qué (quién) es Multiple Parallel Processing - definición

PROGRAMMING PARADIGM IN WHICH MANY CALCULATIONS OR THE EXECUTION OF PROCESSES ARE CARRIED OUT SIMULTANEOUSLY
Parallel computer; Parallel processor; Parallel computation; Parallel programming; Parallel Programming; Parallel computers; Concurrent language; Concurrent event; Computer Parallelism; Parallel machine; Concurrent (programming); Parallel architecture; Parallel Computing; Parallelisation; Parallelization; Parallelized; Multicomputer; Parallelism (computing); Parellel computing; Superword Level Parallelism; Parallel programming language; Message-driven parallel programming; Parallel computer hardware; Parallel program; Parallel code; Parallel language; Parallel processing (computing); Multiple processing elements; Parallel execution units; History of parallel computing; Parallel hardware; Parallel processing computer
  • A graphical representation of [[Amdahl's law]]. The speedup of a program from parallelization is limited by how much of the program can be parallelized. For example, if 90% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 10 times no matter how many processors are used.
  • Beowulf cluster]]
  • Blue Gene/L]] massively parallel [[supercomputer]]
  • The [[Cray-1]] is a vector processor
  • 1=IPC = 1}}).
  • A graphical representation of [[Gustafson's law]]
  • Blue Gene/P]] [[massively parallel]] [[supercomputer]]
  • [[ILLIAC IV]], "the most infamous of supercomputers"<ref name="infamous"/>
  • 1=IPC = 0.2 < 1}}).
  • A logical view of a [[non-uniform memory access]] (NUMA) architecture. Processors in one directory can access that directory's memory with less latency than they can access memory in the other directory's memory.
  • Tesla GPGPU card]]
  • 1=IPC = 2 > 1}}).
  • Taiwania 3 of [[Taiwan]], a parallel supercomputing device that joined [[COVID-19]] research.

parallel processor         
<parallel> A computer with more than one {central processing unit}, used for parallel processing. (1996-04-23)
Parallel computing         
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.
parallel computing         

Wikipedia

Parallel computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

Parallel computing is closely related to concurrent computing—they are frequently used together, and often conflated, though the two are distinct: it is possible to have parallelism without concurrency, and concurrency without parallelism (such as multitasking by time-sharing on a single-core CPU). In parallel computing, a computational task is typically broken down into several, often many, very similar sub-tasks that can be processed independently and whose results are combined afterwards, upon completion. In contrast, in concurrent computing, the various processes often do not address related tasks; when they do, as is typical in distributed computing, the separate tasks may have a varied nature and often require some inter-process communication during execution.

Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks.

In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, but explicitly parallel algorithms, particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common. Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting optimal parallel program performance.

A theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahl's law, which states that it is limited by the fraction of time for which the parallelization can be utilised.