Madeleine II: a portable and efficient communication library for high-performance cluster computing

2002 ◽  
Author(s):  
O. Aumage ◽  
L. Bouge ◽  
A. Denis ◽  
J.-F. Mehaut ◽  
G. Mercier ◽  
...  
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Efficient Communication

Madeleine II: a portable and efficient communication library for high-performance cluster computing

2002 ◽  
Vol 28 (4) ◽  
pp. 607-626 ◽  
Author(s):  
Olivier Aumage ◽  
Luc Bougé ◽  
Jean-François Méhaut ◽  
Raymond Namyst
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Efficient Communication

On Construction of a Diskless Cluster Computing Environment in a Computer Classroom

2012 ◽  
Vol 4 (4) ◽  
pp. 68-88
Author(s):  
Chao-Tung Yang ◽  
Wen-Feng Hsieh
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Relevant Information ◽  
Computing Environment ◽  
Cluster Architecture ◽  
Computer Classroom ◽  
Computation Node ◽  
Cluster Environment ◽  
Computing Performance ◽  
Performance Computing

This paper’s objective is to implement and evaluate a high-performance computing environment by clustering idle PCs (personal computers) with diskless slave nodes on campuses to obtain the effectiveness of the largest computer potency. Two sets of Cluster platforms, BCCD and DRBL, are used to compare computing performance. It’s to prove that DRBL has better performance than BCCD in this experiment. Originally, DRBL was created to facilitate instructions for a Free Software Teaching platform. In order to achieve the purpose, DRBL is applied to the computer classroom with 32 PCs so to enable PCs to be switched manually or automatically among different OS (operating systems). The bioinformatics program, mpiBLAST, is executed smoothly in the Cluster architecture as well. From management’s view, the state of each Computation Node in Clusters is monitored by “Ganglia”, an existing Open Source. The authors gather the relevant information of CPU, Memory, and Network Load for each Computation Node in every network section. Through comparing aspects of performance, including performance of Swap and different network environment, they attempted to find out the best Cluster environment in a computer classroom at the school. Finally, HPL of HPCC is used to demonstrate cluster performance.

scholarly journals Improved Model of Scheduling Algorithm

2018 ◽  
pp. 104-106
Author(s):  
Artur Vardanyan
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Scheduling Algorithm ◽  
Great Influence ◽  
Cluster Management ◽  
Task Scheduling Algorithm ◽  
Long Time ◽  
Improved Model ◽  
First In First Out

Cluster computing is becoming increasingly practical for high performance computing research and development. A computer cluster is a set of connected computers that work together so that, they can be viewed as a single system. Clusters offer a scalable means of linking computers together to provide an expansive environment for hosting enterprise applications. As the number of nodes in cluster configurations grows, the cluster administration becomes more challenging. We need to study the challenges of cluster management and to provide a solution. To have an effective cluster management we need to have an effective task scheduling algorithm. With the explosive growth of information, the demand on computing is sharply increasing. Due to a large number of computing tasks, the scheduling algorithm is an important part of cluster computing and has a great influence on the quality of claster service. In cluster computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. This paper provides an overview of an improved scheduling algorithm that shortens the execution time of tasks and increases the resource utilization.

An Efficient Live Process Migration Approach for High Performance Cluster Computing Systems

2011 ◽  
pp. 362-373 ◽  
Author(s):  
Ehsan Mousavi Khaneghah ◽  
Najmeh Osouli Nezhad ◽  
Seyedeh Leili Mirtaheri ◽  
Mohsen Sharifi ◽  
Ashakan Shirpour
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Process Migration ◽  
Computing Systems

scholarly journals Teaching high-performance service in a cluster computing course

2018 ◽  
Vol 117 ◽  
pp. 138-147
Author(s):  
Pedro López ◽  
Elvira Baydal
Keyword(s):  
High Performance ◽  
Cluster Computing

Designing 3-D Nonlinear Diffusion Filters for High Performance Cluster Computing

2002 ◽  
pp. 290-297 ◽  
Author(s):  
Andrés Bruhn ◽  
Tobias Jakob ◽  
Markus Fischer ◽  
Timo Kohlberger ◽  
Joachim Weickert ◽  
...  
Keyword(s):  
Nonlinear Diffusion ◽  
High Performance ◽  
Cluster Computing

Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach

2012 ◽  
Vol 49 (2) ◽  
pp. 275-298 ◽  
Author(s):  
Anthony M. Filippi ◽  
Budhendra L. Bhaduri ◽  
Thomas Naughton ◽  
Amy L. King ◽  
Stephen L. Scott ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
High Performance ◽  
Cluster Computing ◽  
Radiative Transfer Modeling

HAMR: A dataflow-based real-time in-memory cluster computing engine

2016 ◽  
Vol 31 (5) ◽  
pp. 361-374 ◽  
Author(s):  
Yao Wu ◽  
Long Zheng ◽  
Brian Heilig ◽  
Guang R Gao
Keyword(s):  
Big Data ◽  
Memory Management ◽  
High Performance ◽  
Cluster Computing ◽  
Programming Model ◽  
Distributed Processing ◽  
Large Data ◽  
Computing System ◽  
Fine Grain ◽  
Execution Model

As the attention given to big data grows, cluster computing systems for distributed processing of large data sets become the mainstream and critical requirement in high performance distributed system research. One of the most successful systems is Hadoop, which uses MapReduce as a programming/execution model and takes disks as intermedia to process huge volumes of data. Spark, as an in-memory computing engine, can solve the iterative and interactive problems more efficiently. However, currently it is a consensus that they are not the final solutions to big data due to a MapReduce-like programming model, synchronous execution model and the constraint that only supports batch processing, and so on. A new solution, especially, a fundamental evolution is needed to bring big data solutions into a new era. In this paper, we introduce a new cluster computing system called HAMR which supports both batch and streaming processing. To achieve better performance, HAMR integrates high performance computing approaches, i.e. dataflow fundamental into a big data solution. With more specifications, HAMR is fully designed based on in-memory computing to reduce the unnecessary disk access overhead; task scheduling and memory management are in fine-grain manner to explore more parallelism; asynchronous execution improves efficiency of computation resource usage, and also makes workload balance across the whole cluster better. The experimental results show that HAMR can outperform Hadoop MapReduce and Spark by up to 19x and 7x respectively, in the same cluster environment. Furthermore, HAMR can handle scaling data size well beyond the capabilities of Spark.

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