Transitioning to a Large-Scale Distributed Programming Course

2020 ◽  
Author(s):  
Paul Schmiedmayer ◽  
Lara Marie Reimer ◽  
Marko Jovanovic ◽  
Dominic Henze ◽  
Stephan Jonas
Keyword(s):  
Large Scale ◽  
Distributed Programming

scholarly journals Performance Comparison of OpenMP, MPI, and MapReduce in Practical Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Sol Ji Kang ◽  
Sang Yeon Lee ◽  
Keon Myung Lee
Keyword(s):  
Parallel Programming ◽  
Large Scale ◽  
Memory Systems ◽  
Performance Comparison ◽  
Benchmark Problems ◽  
Distributed Programming ◽  
Problem Size ◽  
Good Picture ◽  
Data Intensive ◽  
The Right

With problem size and complexity increasing, several parallel and distributed programming models and frameworks have been developed to efficiently handle such problems. This paper briefly reviews the parallel computing models and describes three widely recognized parallel programming frameworks: OpenMP, MPI, and MapReduce. OpenMP is the de facto standard for parallel programming on shared memory systems. MPI is the de facto industry standard for distributed memory systems. MapReduce framework has become the de facto standard for large scale data-intensive applications. Qualitative pros and cons of each framework are known, but quantitative performance indexes help get a good picture of which framework to use for the applications. As benchmark problems to compare those frameworks, two problems are chosen: all-pairs-shortest-path problem and data join problem. This paper presents the parallel programs for the problems implemented on the three frameworks, respectively. It shows the experiment results on a cluster of computers. It also discusses which is the right tool for the jobs by analyzing the characteristics and performance of the paradigms.

JDM: BUILDING DISTRIBUTED SYNCHRONIZATION SUPPORT INTO JAVA

2001 ◽  
Vol 02 (03) ◽  
pp. 269-282
Author(s):  
JIANNONG CAO ◽  
NICK K. C. CHEUNG ◽  
ALVIN CHAN
Keyword(s):  
System Architecture ◽  
Large Scale ◽  
Mutual Exclusion ◽  
Distributed Programming ◽  
Shared Resources ◽  
Object Oriented Design ◽  
Distributed Synchronization ◽  
High Level ◽  
Distributed Mutual Exclusion ◽  
Mutual Exclusion Algorithm

The monitor concept has been widely used in a concurrent programming environment for implicitly ensuring mutual exclusion and explicitly achieving process synchronization. It has also been extended to support high-level distributed programming. In this paper, we present JDM, a distributed monitor construct in Java for programming large-scale distributed systems. The distributed monitor construct is based on a well-know tree-based distributed mutual exclusion algorithm proposed by K. Raymond. To increase scalability of the construct, a two-level system architecture is developed, where the node level controls the access to the system-wide shared resources using Raymond's algorithm and the process level synchronizes local processes based on the local monitor concept. An object-oriented design of the system architecture is presented. Implementation and results of performance evaluation are reported and factors that influence the performance of the construct are discussed.

An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing

2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alejandro Corbellini ◽  
Daniela Godoy ◽  
Cristian Mateos ◽  
Silvia Schiaffino ◽  
Alejandro Zunino
Keyword(s):  
Large Scale ◽  
Programming Models ◽  
Distributed Programming ◽  
Graph Processing

scholarly journals The MapReduce Model on Cascading Platform for Frequent Itemset Mining

2018 ◽  
Vol 12 (2) ◽  
pp. 149
Author(s):  
Nur Rokhman ◽  
Amelia Nursanti
Keyword(s):  
Large Scale ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Programming Models ◽  
Distributed Programming ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Mapreduce Model ◽  
Large Scale Data Processing ◽  
Scale Data

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).

scholarly journals Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data

Computers ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 44 ◽  
Author(s):  
Thinh Cao ◽  
Koichi Yamada ◽  
Muneyuki Unehara ◽  
Izumi Suzuki ◽  
Do Nguyen
Keyword(s):  
Information Systems ◽  
Missing Data ◽  
Parallel Algorithms ◽  
Parallel Computation ◽  
Rough Set ◽  
Large Scale ◽  
Programming Model ◽  
Sequential Algorithm ◽  
Distributed Programming ◽  
Decision Attributes

The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

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