scholarly journals Data Parallel Skeletons in Java

2012 ◽  
Vol 9 ◽  
pp. 1817-1826 ◽  
Author(s):  
Herbert Kuchen ◽  
Steffen Ernsting
Author(s):  
Jörg Fischer ◽  
Sergei Gorlatch ◽  
Holger Bischof

2002 ◽  
Vol 12 (02) ◽  
pp. 141-155 ◽  
Author(s):  
HERBERT KUCHEN ◽  
MURRAY COLE

We describe a skeletal parallel programming library which integrates task and data parallel constructs within an API for C++. Traditional skeletal requirements for higher orderness and polymorphism are achieved through exploitation of operator overloading and templates, while the underlying parallelism is provided by MPI. We present a case study describing two algorithms for the travelling salesman problem.


2008 ◽  
Vol 18 (01) ◽  
pp. 117-131 ◽  
Author(s):  
MICHAEL POLDNER ◽  
HERBERT KUCHEN

Algorithmic skeletons intend to simplify parallel programming by providing a higher level of abstraction compared to the usual message passing. Task and data parallel skeletons can be distinguished. In the present paper, we will consider several approaches to implement one of the most classical task parallel skeletons, namely the farm, and compare them w.r.t. scalability, overhead, potential bottlenecks, and load balancing. We will also investigate several communication modes for the implementation of skeletons. Based on experimental results, the advantages and disadvantages of the different approaches are shown. Moreover, we will show how to terminate the system of processes properly.


AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 1603-1609 ◽  
Author(s):  
Michael J. Wright ◽  
Graham V. Candler ◽  
Deepak Bose

Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski

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.


Author(s):  
Petar Hristov ◽  
Gunther H. Weber ◽  
Hamish A. Carr ◽  
Oliver Rubel ◽  
James P. Ahrens

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