Power efficient big data analytics algorithms through low-level operations

Gheorghi Guzun; Josiah C McClurg; Guadalupe Canahuate; Raghuraman Mudumbai

doi:10.1109/BigData.2016.7840623

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Conference proceeding

Power efficient big data analytics algorithms through low-level operations

Gheorghi Guzun, Josiah C McClurg, Guadalupe Canahuate and Raghuraman Mudumbai

2016 IEEE International Conference on Big Data (Big Data), pp.355-361

12/2016

DOI: 10.1109/BigData.2016.7840623

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Abstract

We present an empirical performance evaluation of algorithms that replace arithmetic operations with low-level bit operations for power-aware Big Data processing. Specifically, we compare two different data structures in terms of both execution time and power efficiency: (a) a baseline design using arrays, and (b) a design using bit-slice indexing (BSI) and distributed BSI arithmetic. We evaluate two types of queries popular in Big Data analytics: aggregations and top-k. These queries were implemented using each of the two data structure designs on Apache Spark running on a server cluster that was instrumented with specialized hardware for synchronized real-time power measurement for each server in the cluster. We performed a series of experiments running the above queries on several different datasets. These experiments show that the bit-slicing algorithm consistently outperforms the array algorithm in both power efficiency and execution time. An interesting observation is that the power efficiency improvement of the bit-slicing algorithm over the array method is comparable to or greater than the improvement in execution time for both queries evaluated.

Algorithm design and analysis

Power demand

Big data

Encoding

Servers

Indexing

Details

Title: Subtitle: Power efficient big data analytics algorithms through low-level operations
Creators: Gheorghi Guzun - Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Josiah C McClurg - Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Guadalupe Canahuate - Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Raghuraman Mudumbai - Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Resource Type: Conference proceeding
Publication Details: 2016 IEEE International Conference on Big Data (Big Data), pp.355-361
DOI: 10.1109/BigData.2016.7840623
Publisher: IEEE
Language: English
Date published: 12/2016
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984083253402771

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