scholarly journals Exploiting Vector and Multicore Parallelism for Recursive, Data- and Task-Parallel Programs

2017 ◽  
Vol 52 (8) ◽  
pp. 117-130 ◽  
Author(s):  
Bin Ren ◽  
Sriram Krishnamoorthy ◽  
Kunal Agrawal ◽  
Milind Kulkarni
Keyword(s):  
Parallel Programs ◽  
Task Parallel ◽  
Recursive Data ◽  
And Task

Models and Scheduling Algorithms for Mixed Data and Task Parallel Programs

1997 ◽  
Vol 47 (2) ◽  
pp. 168-184 ◽  
Author(s):  
Soumen Chakrabarti ◽  
James Demmel ◽  
Katherine Yelick
Keyword(s):  
Scheduling Algorithms ◽  
Parallel Programs ◽  
Mixed Data ◽  
Task Parallel ◽  
And Task

User transparent data and task parallel multimedia computing with Pyxis-DT

2013 ◽  
Vol 29 (8) ◽  
pp. 2252-2261
Author(s):  
Timo van Kessel ◽  
Ben van Werkhoven ◽  
Niels Drost ◽  
Jason Maassen ◽  
Henri E. Bal ◽  
...  
Keyword(s):  
Multimedia Computing ◽  
Task Parallel ◽  
And Task

AN ANALYTICAL METHOD FOR PARALLELIZATION OF RECURSIVE FUNCTIONS

2000 ◽  
Vol 10 (04) ◽  
pp. 359-370 ◽  
Author(s):  
JOONSEON AHN ◽  
TAISOOK HAN
Keyword(s):  
Analytical Method ◽  
Parallel Programs ◽  
Data Parallelism ◽  
Complex Data ◽  
Recursive Functions ◽  
Parallel Skeletons ◽  
Data Flows ◽  
Recursive Data Structures ◽  
Recursive Data

Programming with parallel skeletons is an attractive framework because it encourages programmers to develop efficient and portable parallel programs. However, extracting parallelism from sequential specifications and constructing efficient parallel programs using the skeletons are still difficult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have defined a classification of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classification. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data flows.

A data and task parallel image processing environment

2002 ◽  
Vol 28 (7-8) ◽  
pp. 945-965 ◽  
Author(s):  
Cristina Nicolescu ◽  
Pieter Jonker
Keyword(s):  
Image Processing ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Task Parallel ◽  
And Task
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