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  1. Proceedings of the 1st Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS '16)
  2. Fatman vs. littleboy: scaling up linear algebraic operations in scale-out data platforms
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Scientific workflows at datawarp-speed: accelerated data-intensive science using NERSC's burst buffer
Parallel I/O characterisation based on server-side performance counters
Replicating HPC I/O workloads with proxy applications
Can non-volatile memory benefit mapreduce applications on HPC clusters?
Fatman vs. littleboy: scaling up linear algebraic operations in scale-out data platforms
Klimatic: a virtual data lake for harvesting and distribution of geospatial data
Get out of the way!: applying compression to internal data structures
Towards energy efficient data management in HPC: the open ethernet drive approach
A generic framework for testing parallel file systems
A bloom filter based scalable data integrity check tool for large-scale dataset

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Proceeding

Fatman vs. littleboy: scaling up linear algebraic operations in scale-out data platforms

Content Provider ACM Digital Library
Author Xu, Luna Butt, Ali R. Kannan, Ramakrishnan Lim, Seung-Hwan Sukumar, Sreenivas R.
Abstract Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are highly desirable to enable efficient processing over millions of data points. To this end, we present a matrix manipulation approach to effectively scale-up each node in a scale-out data parallel platform such as Apache Spark. Specifically, we enable hardware acceleration for matrix multiplications in a distributed Spark setup without user intervention. Our approach supports both dense and sparse distributed matrices, and provides flexible control of acceleration by matrix density. We demonstrate the benefit of our approach for generalized matrix multiplication operations over large matrices with up to four billion elements. To connect the effectiveness of our approach with machine learning applications, we performed Gramian matrix computation via generalized matrix multiplications. Our experiments show that our approach achieves more than 2x performance speed-up, and up to 96.1% computation improvement, compared to a state of the art Spark MLlib for dense matrices.
Starting Page 25
Ending Page 30
Page Count 6
ISBN 9781509052165
DOI 10.1109/PDSW-DISCS.2016.8
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-13
Access Restriction Subscribed
Content Type Text
Resource Type Article
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