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Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
[article]
2022
arXiv
pre-print
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Many such cluster improvement algorithms are flow-based methods, by which we mean that operationally they require the solution of a sequence of maximum flow problems on a (typically implicitly) modified ...
Viewing these cluster improvement algorithms via a fractional programming framework suggests directions for future algorithm development. ...
of flow algorithms, and finally Kent Quanrud for many helpful pointers. ...
arXiv:2004.09608v3
fatcat:f3ma3k4dsbgjherlsie2jtzc34
A taxonomy of application scheduling tools for high performance cluster computing
2006
Cluster Computing
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After discussing the fundamental scheduling techniques, we propose a framework and taxonomy for the scheduling tools on clusters. ...
This paper presents a survey on the software tools for the graphbased scheduling on cluster systems with the focus on task scheduling. ...
We also discuss the important aspects in improving the usability of the scheduling tools. Our framework can provide a guideline for developing scheduling tools on clusters. ...
doi:10.1007/s10586-006-9747-2
fatcat:e4cbgqmqfvcw7alfzmaiklza3y
Reconfiguring Distributed Applications in FPGA Accelerated Cluster with Wireless Networking
2011
2011 21st International Conference on Field Programmable Logic and Applications
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FPGA accelerators are capable of improving computation and energy efficiency of many applications targeting a cluster of machines. ...
Comparing with conventional Ethernet based approaches, the proposed system with wireless networking enables a lightweight and efficient method for the FPGA devices to exchange information directly. ...
Acknowledgement The support of Imperial College London Research Excellence Award, UK Engineering and Physical Sciences Research Council, Alpha Data and Xilinx is gratefully acknowledged. ...
doi:10.1109/fpl.2011.106
dblp:conf/fpl/NiuTL11
fatcat:zqrylfmni5bizfw6jksihpovzm
Clustering and Flow Conservation Monitoring Tool for Software Defined Networks
2018
Sensors
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In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. ...
Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. ...
In other words, it estimates the means and standard deviations for each cluster to maximize the likelihood. • Density Based: is a clustering algorithm that defines areas with higher object density than ...
doi:10.3390/s18041079
pmid:29614049
pmcid:PMC5948600
fatcat:chgoni5tanb2bpx326hfxvn7fa
Locality-Aware CTA Clustering for Modern GPUs
2017
Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS '17
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By leveraging these insights, we propose the concept of CTA-Clustering and its associated software-based techniques to reshape the default CTA scheduling in order to group the CTAs with potential reuse ...
unified cache. ...
Acknowledgments We would like to thank the anonymous reviewers for their constructive comments and suggestions for improving this work. This research is supported by the U.S. ...
doi:10.1145/3037697.3037709
dblp:conf/asplos/LiS0L0C17
fatcat:vw7yzpigbbhp5ml7alahsiggc4
Locality-Aware CTA Clustering for Modern GPUs
2017
SIGARCH Computer Architecture News
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By leveraging these insights, we propose the concept of CTA-Clustering and its associated software-based techniques to reshape the default CTA scheduling in order to group the CTAs with potential reuse ...
unified cache. ...
Acknowledgments We would like to thank the anonymous reviewers for their constructive comments and suggestions for improving this work. This research is supported by the U.S. ...
doi:10.1145/3093337.3037709
fatcat:4lmnja4e6veonfg2ojox7a6pbq
Locality-Aware CTA Clustering for Modern GPUs
2017
SIGPLAN notices
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By leveraging these insights, we propose the concept of CTA-Clustering and its associated software-based techniques to reshape the default CTA scheduling in order to group the CTAs with potential reuse ...
unified cache. ...
Acknowledgments We would like to thank the anonymous reviewers for their constructive comments and suggestions for improving this work. This research is supported by the U.S. ...
doi:10.1145/3093336.3037709
fatcat:summls6gdza45pmuih42753rhy
Locality-Aware CTA Clustering for Modern GPUs
2017
ACM SIGOPS Operating Systems Review
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By leveraging these insights, we propose the concept of CTA-Clustering and its associated software-based techniques to reshape the default CTA scheduling in order to group the CTAs with potential reuse ...
unified cache. ...
Acknowledgments We would like to thank the anonymous reviewers for their constructive comments and suggestions for improving this work. This research is supported by the U.S. ...
doi:10.1145/3093315.3037709
fatcat:h7vhnovsqndmxduewwjw5fpy6e
Efficiently Combining SVD, Pruning, Clustering and Retraining for Enhanced Neural Network Compression
2018
Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning - EMDL'18
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Unified compression framework The compression strategies presented in section 3.1 and 3.2 are represented in a common algorithmic framework for fair comparison. ...
Moreover, the method
Experiments and analysis This section compares the different state-of-the-art techniques with the newly introduced compression flows using the unified algorithmic framework algorithm ...
doi:10.1145/3212725.3212733
dblp:conf/mobisys/GoetschalckxMWV18
fatcat:hdohy7pdxrbdzf2bffclzlzlmq
Data stream clustering: a review
2020
Artificial Intelligence Review
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2020 pre-print arXiv:2007.10781v1 fatcat:bbui32ece5ghdj32xbvytqiuja
We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy. ...
A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms. ...
This situation requires a continuous performance improvement in data stream clustering algorithms. It is possible to improve the performance by using parallel programming and edge computing. ...
doi:10.1007/s10462-020-09874-x
fatcat:27fq6ccbrzb4xpfoaatic3rlim
Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems
[article]
2022
arXiv
pre-print
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And it has been further used as embedded information in the clustering algorithm along with other important electrical information from the system to enrich the modeling capability of the proposed framework ...
In this study, we propose a hierarchical spectral clustering-based intentional islanding strategy at the transmission level with renewable generations. ...
Hierarchical Spectral Clustering In Sec. II-A, we have briefly introduced the spectral clustering algorithm for graph systems based on the K-Means algorithm. ...
arXiv:2203.06579v1
fatcat:otxk4rpgwbehdeijrmfj3jfz64
Programming framework for clusters with heterogeneous accelerators
2011
SIGARCH Computer Architecture News
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We describe a programming framework for high performance clusters with various hardware accelerators. ...
The framework has been used to support physics simulation and financial application development. We achieve significant performance improvement on a 16-node cluster with FPGA and GPU accelerators. ...
Acknowledgments The support of Imperial College London Research Excellence Award, UK Engineering and Physical Sciences Research Council, Alpha Data, nVidia and Xilinx is gratefully acknowledged. ...
doi:10.1145/1926367.1926377
fatcat:ijaqj4z3ardaxnlty5tyd3hnte
SparkCL: A Unified Programming Framework for Accelerators on Heterogeneous Clusters
[article]
2015
arXiv
pre-print
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The framework allows a single code base to target any type of compute core that supports OpenCL and easy integration of new core types into a Spark cluster. ...
We introduce SparkCL, an open source unified programming framework based on Java, OpenCL and the Apache Spark framework. ...
Such a system employs both conventional and unconventional cores on different types of tasks. Heterogeneous computing brings improved performance and power efficiency through specialization. ...
arXiv:1505.01120v1
fatcat:xzuwpq4ykzfbfddjp4p4kk2y2y
Scalable, Balanced Model-based Clustering
[chapter]
2003
Proceedings of the 2003 SIAM International Conference on Data Mining
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This paper presents a general framework for adapting any generative (model-based) clustering algorithm to provide balanced solutions, i.e., clusters of comparable sizes. ...
Partitional, model-based clustering algorithms are viewed as an iterative two-step optimization process-iterative model re-estimation and sample re-assignment. ...
In this section, we introduce a unified framework for modelbased clustering algorithms, based on a bipartite graph view ( Fig. 1 ) of a data set, O, containing n data samples, {o 1 , o 2 , ..., o n }, ...
doi:10.1137/1.9781611972733.7
dblp:conf/sdm/ZhongG03
fatcat:5bjfvo2u2baz7cthogdgcqievi
Oncilla: A GAS runtime for efficient resource allocation and data movement in accelerated clusters
2013
2013 IEEE International Conference on Cluster Computing (CLUSTER)
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Accelerated and in-core implementations of Big Data applications typically require large amounts of host and accelerator memory as well as efficient mechanisms for trans ferring data to and from accelerators ...
in heterogeneous clusters. ...
ACKNOWLEDGEMENTS We would like to acknowledge the EXTOLL team for their expert assistance with hardware debugging and cluster support, Advanced Industrial Computer (AIC) for the donation of HTX based server ...
doi:10.1109/cluster.2013.6702679
dblp:conf/cluster/YoungSYMSF13
fatcat:lyphff6ovrhxhdtzay55cv4uli
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