![DSpace logo](/jspui/image/logo.gif)
Please use this identifier to cite or link to this item:
http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8152
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Goyal, Poonam | - |
dc.contributor.author | Goyal, Navneet | - |
dc.date.accessioned | 2022-12-27T06:50:14Z | - |
dc.date.available | 2022-12-27T06:50:14Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9006390 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8152 | - |
dc.description.abstract | Hierarchical Agglomerative Clustering (HAC) algorithms are used in many applications where clusters have a hierarchical relationship between them. Their parallelization is challenging due to the dependence of every agglomeration step on all previous agglomerations. Although a few parallel algorithms have been proposed for SLINK HAC algorithm, only limited work has been done to parallelize other HAC algorithms. In this paper, we present a high-level abstraction, which provides a uniform way to specify any HAC algorithm, and a framework for automatic parallelization of the same for distributed memory systems. The abstraction is supported by constructs in a high level, domain specific language, and a compiler translates algorithms expressed in this language to efficient parallel code targeting distributed systems. Our experiments on multiple HAC algorithms proves that the runtime performance achieved is comparable with state-of-the-art manual parallel implementations on Spark and MPI while requiring only a fraction of the programming effort. At runtime, master-slave execution is used, and load is balanced among the slaves in an algorithm-agnostic way, which is a significant contrast to custom load-balancing techniques seen in the literature on parallel HAC algorithms. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Hierarchical Agglomerative Clustering | en_US |
dc.subject | High Performance Computing | en_US |
dc.subject | Big Data | en_US |
dc.subject | Automatic Parallelization | en_US |
dc.title | Rapid Prototyping of Hierarchical Agglomerative Clustering Algorithms for Distributed Systems | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Computer Science and Information Systems |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.