MEMOCODE 2016 design contest: K-means clustering

Peter Milder

doi:10.1109/MEMCOD.2016.7797755

MEMOCODE 2016 design contest: K-means clustering

Peter Milder

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

K-means is a clustering algorithm that aims to group data into k similar clusters. The objective of the 2016 MEMOCODE Design Contest is to implement a system to efficiently partition a large set of multidimensional data using k-means. Contestants were given one month to develop a system to perform this operation, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The winning team, which was invited to contribute a paper describing their techniques, combined careful algorithmic and implementation optimizations using CPUs and GPUs.

Original language	English
Title of host publication	2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	125-127
Number of pages	3
ISBN (Electronic)	9781509027910
DOIs	https://doi.org/10.1109/MEMCOD.2016.7797755
State	Published - Dec 27 2016
Event	14th ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016 - Kanpur, India Duration: Nov 18 2016 → Nov 20 2016

Publication series

Name	2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016

Conference

Conference	14th ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016
Country/Territory	India
City	Kanpur
Period	11/18/16 → 11/20/16

Access to Document

10.1109/MEMCOD.2016.7797755

Cite this

Milder, P. (2016). MEMOCODE 2016 design contest: K-means clustering. In 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016 (pp. 125-127). Article 7797755 (2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MEMCOD.2016.7797755

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title = "MEMOCODE 2016 design contest: K-means clustering",

abstract = "K-means is a clustering algorithm that aims to group data into k similar clusters. The objective of the 2016 MEMOCODE Design Contest is to implement a system to efficiently partition a large set of multidimensional data using k-means. Contestants were given one month to develop a system to perform this operation, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The winning team, which was invited to contribute a paper describing their techniques, combined careful algorithmic and implementation optimizations using CPUs and GPUs.",

author = "Peter Milder",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 14th ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016 ; Conference date: 18-11-2016 Through 20-11-2016",

year = "2016",

month = dec,

day = "27",

doi = "10.1109/MEMCOD.2016.7797755",

language = "English",

series = "2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "125--127",

booktitle = "2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016",

}

Milder, P 2016, MEMOCODE 2016 design contest: K-means clustering. in 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016., 7797755, 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 125-127, 14th ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016, Kanpur, India, 11/18/16. https://doi.org/10.1109/MEMCOD.2016.7797755

MEMOCODE 2016 design contest: K-means clustering. / Milder, Peter.
2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 125-127 7797755 (2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - K-means is a clustering algorithm that aims to group data into k similar clusters. The objective of the 2016 MEMOCODE Design Contest is to implement a system to efficiently partition a large set of multidimensional data using k-means. Contestants were given one month to develop a system to perform this operation, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The winning team, which was invited to contribute a paper describing their techniques, combined careful algorithmic and implementation optimizations using CPUs and GPUs.

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Milder P. MEMOCODE 2016 design contest: K-means clustering. In 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 125-127. 7797755. (2016 ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2016). doi: 10.1109/MEMCOD.2016.7797755

MEMOCODE 2016 design contest: K-means clustering

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