2017 |
Volume 2 |
Issue 1
Efficient Support Vector Machine for Multi-Core Systems
1.
Rahul Kumar
– Soft Computing And Expert Systems Lab, Abv-iiitm, Gwalior, Madhya Pradesh, India.
2.
Saumil Maheshwari
– Soft Computing And Expert Systems Lab, Abv-iiitm, Gwalior, Madhya Pradesh, India.
3.
Apoorva Mishra
– Soft Computing And Expert Systems Lab, Abv-iiitm, Gwalior, Madhya Pradesh, India.
4.
Ishu Garg
– Soft Computing And Expert Systems Lab, Abv-iiitm, Gwalior, Madhya Pradesh, India.
5.
Anupam Shukla
– Soft Computing And Expert Systems Lab, Abv-iiitm, Gwalior, Madhya Pradesh, India.
Received
02-Feb-2018
|
Accepted
-
|
Published
02-Feb-2018
|
Abstract
We are in an era where scaling up the processing speed of computers by increasing the clock speed of processors had become an ineffective strategy for handling Big Data. Now, computers have increased number of processors embedded within, which has motivated various programmer to take advantage of these architectures. To increase the processing speed, parallelization of algorithms can be used as an alternative rather than optimizing the existing algorithms. In the paper, we have deployed a parallel programming framework that could take advantage of multi-core structure of processors. This framework can be used for different learning algorithms that satisfies Statistical Query Model (SQM) i.e., can be simplified in Summation form. We have used Google’s MapReduce model for achieving parallelization in learning algorithms on two datasets (Ionoshpere, Breast Cancer datasets) from UCI machine learning repository. Our implemented model gives better result than existing implementations and also verifies the linear speedup of processing with an increase in number of cores in the processors.
Keywords Big data, Machine learning, MapReduce, Multi-core, Statistical Query Model (SQM).
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