Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Author: search.filters.author.Goyal, NavneetSubject: search.filters.subject.Big Data

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Now showing 1 - 5 of 5
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    Rapid Prototyping of Hierarchical Agglomerative Clustering Algorithms for Distributed Systems
    (IEEE, 2019) Goyal, Poonam; Goyal, Navneet
    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.
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    Parallel SLINK for big data
    (Springer, 2019-06) Goyal, Navneet; Goyal, Poonam
    The major strength of hierarchical clustering algorithms is that it allows visual interpretations of clusters through dendrograms. Users can cut the dendrogram at different levels to get desired number of clusters. A major problem with hierarchical algorithms is their quadratic runtime complexity, which limits the amount of data that can be clustered in reasonable amount of time. Also, due to its agglomerative merging process, each iteration depends on the data of all previous iterations, making it difficult to parallelize. Thus, there is a need for an efficient parallel implementation of SLINK algorithm which can scale to big data. We present a parallel SLINK algorithm, sGridSLINK, for shared memory architectures. sGridSLINK produces exactly the same dendrogram as the classical SLINK algorithm. We also present, hGridSLINK, a parallel algorithm which fully exploits a multi-core cluster system. To the best of our knowledge, there is no hybrid parallel algorithm for SLINK available in the literature. The proposed algorithms exploit spatial locality of data to reduce the number of distance calculations. Adaptive gridding is used to counter skewness in data and to ensure load balancing. Extensive experiments are carried out to establish the efficiency and scalability of proposed parallel algorithms. sGridSLINK is approximately 840 times faster than the state-of-the-art algorithm using 55 threads on a 48-core machine on a real dataset having 6 million data points. It also achieves a speedup of 47.93 over the best known sequential SLINK, GridSLINK, on a real dataset using 48 threads on a 48-core machine. hGridSLINK achieves a maximum speedup of 68.26 on a 32-node cluster (32×4 processing elements) with respect to GridSLINK. The hGridSLINK algorithm is able to cluster 200 million data points in only 1317 s (less than 22 min). No existing parallel SLINK algorithm is capable of such efficient clustering of Big Data.
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    A Domain Specific Language for Clustering
    (Springer, 2016-11) Goyal, Navneet; Goyal, Poonam
    Clustering of large volumes of data is a complex problem which requires use of sophisticated algorithms as well as High Performance Computing hardware like a cluster of computers. It is highly desirable that data mining experts have a solution which on one hand provides a simple interface for ex-pressing their algorithms in terms of domain specific idioms and on the other hand automatically generates parallel code that can run on a cluster of multicore nodes. The proposed Domain Specific Language (DSL) along with its parallelizing compiler attempts to provide a solution. In this paper, we give the design of the DSL, called DWARF. Various language constructs have been described along with the rationale behind their inclusion in the language. A qualitative comparison of abstraction provided by DWARF is compared with MapReduce, Spark, and other MPI-based implementations to establish the usefulness of the proposed clustering DSL.
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    μDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality
    (IEEE, 2019) Goyal, Navneet; Goyal, Poonam; Challa, Jagat Sesh
    DBSCAN is one of the most popular and effective clustering algorithms that is capable of identifying arbitrary-shaped clusters and noise efficiently. However, its super-linear complexity makes it infeasible for applications involving clustering of Big Data. A major portion of the computation time of DBSCAN is taken up by the neighborhood queries, which becomes a bottleneck to its performance. We address this issue in our proposed micro-cluster based DBSCAN algorithm, μDBSCAN, which identifies core-points even without performing neighbourhood queries and becomes instrumental in reducing the run-time of the algorithm. It also significantly reduces the computation time per neighbourhood query while producing exact DBSCAN clusters. Moreover, the micro-cluster based solution makes it scalable for high dimensional data. We also propose a highly scalable distributed implementation of μDBSCAN, μDBSCAN-D, to exploit a commodity cluster infrastructure. Experimental results demonstrate tremendous improvements in performance of our proposed algorithms as compared to their respective state-of-the-art solutions for various standard datasets. μDBSCAN-D is an exact parallel solution for DBSCAN which is capable of processing massive amounts of data efficiently (1 billion data points in 41 minutes on a 32 node cluster), while producing a clustering that is same as that of traditional DBSCAN.
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    A Rapid Prototyping Approach for High Performance Density-Based Clustering
    (IEEE, 2019-10) Goyal, Navneet; Goyal, Poonam
    Big Data has significantly increased the dependence of data analytics community on High Performance Computing (HPC) systems. However, efficiently programming an HPC system is still a tedious task requiring specialized skills in parallelization and the use of platform-specific languages as well as mechanisms. We present a framework for quickly prototyping new/existing density-based clustering algorithms while obtaining low running times and high speedups via automatic parallelization. The user is required only to specify the sequential algorithm in a Domain Specific Language (DSL) for clustering at a very high level of abstraction. The parallelizing compiler for the DSL does the rest to leverage distributed systems - in particular, typical scale-out clusters made of commodity hardware. Our approach is based on recurring, parallelizable programming patterns known as Kernels, which are identified and parallelized by the compiler. We demonstrate the ease of programming and scalable performance for DBSCAN, SNN, and RECOME algorithms. We also establish that the proposed approach can achieve performance comparable to state-of-the-art manually parallelized implementations while requiring minimal programming effort that is several orders of magnitude smaller than those required on other parallel platforms like MPI/Spark.