scholarly journals Efficient Lock-Free Work-Stealing Iterators for Data-Parallel Collections

2015 ◽  
Author(s):  
Aleksandar Prokopec ◽  
Dmitry Petrashko ◽  
Martin Odersky
Keyword(s):  
Work Stealing ◽  
Data Parallel

Data-parallel line relaxation method for the Navier-Stokes equations

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 1603-1609 ◽  
Author(s):  
Michael J. Wright ◽  
Graham V. Candler ◽  
Deepak Bose
Keyword(s):  
Stokes Equations ◽  
Relaxation Method ◽  
Parallel Line ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Data Parallel

Work-stealing with configurable scheduling strategies

2013 ◽  
Vol 48 (8) ◽  
pp. 315-316 ◽  
Author(s):  
Martin Wimmer ◽  
Daniel Cederman ◽  
Jesper Larsson Träff ◽  
Philippas Tsigas
Keyword(s):  
Work Stealing ◽  
Scheduling Strategies

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Energy-efficient work-stealing language runtimes

2014 ◽  
Vol 49 (4) ◽  
pp. 513-528
Author(s):  
Haris Ribic ◽  
Yu David Liu
Keyword(s):  
Energy Efficient ◽  
Work Stealing ◽  
Efficient Work

scholarly journals Data Parallel Hypersweeps for in Situ Topological Analysis

2020 ◽  
Author(s):  
Petar Hristov ◽  
Gunther H. Weber ◽  
Hamish A. Carr ◽  
Oliver Rubel ◽  
James P. Ahrens
Keyword(s):  
Topological Analysis ◽  
Data Parallel

Asymmetry-aware work-stealing runtimes

2016 ◽  
Vol 44 (3) ◽  
pp. 40-52 ◽  
Author(s):  
Christopher Torng ◽  
Moyang Wang ◽  
Christopher Batten
Keyword(s):  
Work Stealing

Fence-free work stealing on bounded TSO processors

2014 ◽  
Vol 49 (4) ◽  
pp. 413-426 ◽  
Author(s):  
Adam Morrison ◽  
Yehuda Afek
Keyword(s):  
Work Stealing
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