Accelerating Medoids-based clustering with the Intel Many Integrated Core architecture

Rechkalov, Timofey; Zymbler, Mikhail

doi:10.1109/icaict.2015.7338591

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…Loop-optimizations unrolling, 9,17,23,24,29,50,84,90 collapsing, 4,6,7,13,20,21,44,54 splitting 22,28 Blocking (tiling) in cache, 14,15,18,[20][21][22]27,39,44,52,54,69 registers 68,69 Compile-time optimizations using pre-computed values, 35,52 specifying array and loop bounds at compile time 6,54 Compute-related optimizations Reusing intermediate variables, 22,35 using conflict-detection instruction of AVX-512, 52,85 performing redundant computation to avoid data-communication or atomic operations 52,82 Array transpose 6, 79…”

Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

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A survey on evaluating and optimizing performance of Intel Xeon Phi

Mittal

2020

Concurrency and Computation

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Summary Intel's Xeon Phi combines the parallel processing power of a many‐core accelerator with the programming ease of CPUs. In this paper, we present a survey of works that study the architecture of Phi and use it as an accelerator for a broad range of applications. We review performance optimization strategies as well as the factors that bottleneck the performance of Phi. We also review works that perform comparison or collaborative execution of Phi with CPUs and GPUs. This paper will be useful for researchers and developers in the area of computer‐architecture and high‐performance computing.

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Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

“…,[12][13][14][16][17][18]20,21,24,[27][28][29][30][33][34][35][36][37]39,[42][43][44][45][46][47][48]50,52,55,57,59,63,66,84,86,90,92,93,95,99 IntelMKL 2,17,19,31,32,40,93,99 …”

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confidence: 99%

A survey on evaluating and optimizing performance of Intel Xeon Phi

Mittal

2020

Concurrency and Computation

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“…Such a point exists and is unique because the polytope M is a bounded, closed and convex set. Combining the dist definition, equation (18), the triangle inequality for the norm and equations (29) and (14), we get…”

Section: Lemma 1 Under the Conditions Of Theorem 1 We Havementioning

confidence: 99%

“…The first set must satisfy the non-formalized constraints, while the second must not. To obtain representative patterns, methods of data mining [14] and time series analysis can be used [15]. To overcome the problem of non-stationary input data, the authors proposed in [16,17] the pursuit algorithm for solving non-stationary LP problems on cluster computing systems.…”

Section: Introductionmentioning

confidence: 99%

On the Solution of Linear Programming Problems in the Age of Big Data

Sokolinskaya

Sokolinsky

2017

Communications in Computer and Information Science

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The Big Data phenomenon has spawned large-scale linear programming problems. In many cases, these problems are non-stationary. In this paper, we describe a new scalable algorithm called NSLP for solving high-dimensional, non-stationary linear programming problems on modern cluster computing systems. The algorithm consists of two phases: Quest and Targeting. The Quest phase calculates a solution of the system of inequalities defining the constraint system of the linear programming problem under the condition of dynamic changes in input data. To this end, the apparatus of Fejer mappings is used. The Targeting phase forms a special system of points having the shape of an n-dimensional axisymmetric cross. The cross moves in the n-dimensional space in such a way that the solution of the linear programming problem is located all the time in an "-vicinity of the central point of the cross.Comment: Parallel Computational Technologies - 11th International Conference, PCT 2017, Kazan, Russia, April 3-7, 2017, Proceedings (to be published in Communications in Computer and Information Science, vol. 753

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“…Clustering is one of the basic techniques in the data mining domain aimed at organizing a set of data objects into their natural subsets (called clusters), when labels for the data are unavailable. An ideal cluster is an isolated set of data points which are similar to one another, but dissimilar to the points in other clusters [8,9]. Similarity is commonly defined in terms of how close the objects are and is based on a specified distance metric [8].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Architecture for the Partitioning around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare

Mushtaq

Khawaja

Akram

et al. 2018

Sensors

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Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.

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Accelerating Medoids-based clustering with the Intel Many Integrated Core architecture

Cited by 10 publications

References 18 publications

A survey on evaluating and optimizing performance of Intel Xeon Phi

A survey on evaluating and optimizing performance of Intel Xeon Phi

On the Solution of Linear Programming Problems in the Age of Big Data

A Parallel Architecture for the Partitioning around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare

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