data-parallel - significado y definición. Qué es data-parallel
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Qué (quién) es data-parallel - definición

PARALLELIZATION ACROSS MULTIPLE PROCESSORS IN PARALLEL COMPUTING ENVIRONMENTS
Data Parallelism; Data parallel; Data-parallelism; Data level parallelism; Data-level parallelism; Data parallel computation; History of data parallelism
  • Data parallelism in matrix multiplication
  • Sequential vs. data-parallel job execution

parallel processor         
  • 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]]
  • [[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.
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
<parallel> A computer with more than one {central processing unit}, used for parallel processing. (1996-04-23)
Parallel computing         
  • 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]]
  • [[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.
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
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         
  • 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]]
  • [[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.
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

Wikipedia

Data parallelism

Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.

A data parallel job on an array of n elements can be divided equally among all the processors. Let us assume we want to sum all the elements of the given array and the time for a single addition operation is Ta time units. In the case of sequential execution, the time taken by the process will be n×Ta time units as it sums up all the elements of an array. On the other hand, if we execute this job as a data parallel job on 4 processors the time taken would reduce to (n/4)×Ta + merging overhead time units. Parallel execution results in a speedup of 4 over sequential execution. One important thing to note is that the locality of data references plays an important part in evaluating the performance of a data parallel programming model. Locality of data depends on the memory accesses performed by the program as well as the size of the cache.