Power-Constrained Performance Scheduling of Data Parallel Tasks

2016 ◽  
Author(s):  
Eric Anger ◽  
Jeremiah Wilke ◽  
Sudhakar Yalamanchili
Keyword(s):  
Data Parallel ◽  
Parallel Tasks

scholarly journals An Accelerator-based Logistic Map Image Cryptosystems for Grayscale Images

2021 ◽  
Author(s):  
M. Raviraja Holla ◽  
Alwyn R. Pais ◽  
D. Suma
Keyword(s):  
High Speed ◽  
Logistic Map ◽  
Data Dependencies ◽  
Data Parallel ◽  
Parallel Tasks ◽  
Computationally Intensive ◽  
Coalesced Memory ◽  
Two Stages ◽  
The Cost ◽  
And Diffusion

The logistic map is a class of chaotic maps. It is still in use in image cryptography. The logistic map cryptosystem has two stages, namely permutation, and diffusion. These two stages being computationally intensive, the permutation relocates the pixels, whereas the diffusion rescales them. The research on refining the logistic map is progressing to make the encryption more secure. Now there is a need to improve its efficiency to enable such models to fit for high-speed applications. The new invention of accelerators offers efficiency. But the inherent data dependencies hinder the use of accelerators. This paper discusses the novelty of identifying independent data-parallel tasks in a logistic map, handing them over to the accelerators, and improving their efficiency. Among the two accelerator models proposed, the first one achieves peak efficiency using coalesced memory access. The other cryptosystem further improves performance at the cost of more execution resources. In this investigation, it is noteworthy that the parallelly accelerated logistic map achieved a significant speedup to the larger grayscale image used. The objective security estimates proved that the two stages of the proposed systems progressively ensure security.

scholarly journals Communication-Aware Scheduling of Data-Parallel Tasks on Multicore Architectures

2019 ◽  
Vol 12 (0) ◽  
pp. 65-73 ◽  
Author(s):  
Kana Shimada ◽  
Ittetsu Taniguchi ◽  
Hiroyuki Tomiyama
Keyword(s):  
Multicore Architectures ◽  
Data Parallel ◽  
Parallel Tasks

A branch-and-bound approach to scheduling of data-parallel tasks on multi-core architectures

2020 ◽  
Vol 12 (1) ◽  
pp. 125
Author(s):  
Ittetsu Taniguchi ◽  
Hiroyuki Tomiyama ◽  
Lin Meng ◽  
Yang Liu
Keyword(s):  
Branch And Bound ◽  
Data Parallel ◽  
Parallel Tasks

Compiling Data Parallel Tasks for Coordinated Execution⋆

1999 ◽  
pp. 413-417
Author(s):  
Erwin Laure ◽  
Hans Zima ◽  
Matthew Haines ◽  
Piyush Mehrotra
Keyword(s):  
Data Parallel ◽  
Parallel Tasks

High Performance Fortran, Version 2

1997 ◽  
Vol 07 (04) ◽  
pp. 437-449 ◽  
Author(s):  
Robert Schreiber
Keyword(s):  
High Performance ◽  
Data Parallelism ◽  
Data Mapping ◽  
Parallel Language ◽  
Data Parallel ◽  
High Performance Fortran ◽  
Concurrent Execution ◽  
Parallel Tasks ◽  
New Ideas ◽  
Data Parallel Programming

This paper introduces the ideas that underly the data-parallel language High Performance Fortran (HPF) and the new ideas in version 2 of HPF. It first reviews HPF's key language elements. It discusses the meaning of data parallelism and the limitations of HPF version 1 as a data-parallel programming language. The second part of the paper is a review of the development of version 2 of HPF. The extended language, under development in 1996, includes a richer data mapping capability; an extension to the independent loop that allows reduction operations in the loop range; a means for directing the mapping of computation as well as data; and a way to specify concurrent execution of several parallel tasks on disjoint subsets of processors.

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