A Retargetable MATLAB-to-C Compiler Exploiting Custom Instructions and Data Parallelism

2020 ◽  
Vol 19 (6) ◽  
pp. 1-27
Author(s):  
Ioannis Latifis ◽  
Karthick Parashar ◽  
Grigoris Dimitroulakos ◽  
Hans Cappelle ◽  
Christakis Lezos ◽  
...  
Keyword(s):  
Data Parallelism ◽  
C Compiler

An Optimized Strategy for Collective Communication in Data Parallelism

2009 ◽  
Vol 31 (2) ◽  
pp. 318-328
Author(s):  
Jue WANG ◽  
Chang-Jun HU ◽  
Ji-Lin ZHANG ◽  
Jian-Jiang LI
Keyword(s):  
Data Parallelism ◽  
Collective Communication

scholarly journals Adaptive data parallelism for internet clients on heterogeneous platforms

2013 ◽  
Vol 48 (2) ◽  
pp. 53-62
Author(s):  
Alessandro Pignotti ◽  
Adam Welc ◽  
Bernd Mathiske
Keyword(s):  
Data Parallelism ◽  
Heterogeneous Platforms

Enhancing data parallelism for Ant Colony Optimization on GPUs

2013 ◽  
Vol 73 (1) ◽  
pp. 42-51 ◽  
Author(s):  
José M. Cecilia ◽  
José M. García ◽  
Andy Nisbet ◽  
Martyn Amos ◽  
Manuel Ujaldón
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Data Parallelism

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

An object-oriented framework for data parallelism

2000 ◽  
Vol 32 (1es) ◽  
pp. 31
Author(s):  
J.-M. Jézéquel
Keyword(s):  
Object Oriented ◽  
Data Parallelism

An equational language for data-parallelism

1993 ◽  
Vol 28 (7) ◽  
pp. 112-118
Author(s):  
Pushpa Rao ◽  
Clifford Walinsky
Keyword(s):  
Data Parallelism
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