DeepAI AI Chat
Log In Sign Up

GraphGuess: Approximate Graph Processing System with Adaptive Correction

by   Morteza Ramezani, et al.

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focus mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most of graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. Although various computing platforms and application domains benefit from approximate computing, it has not been thoroughly explored yet in the context of graph processing systems. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.


page 1

page 2

page 3

page 4


GraphMP: I/O-Efficient Big Graph Analytics on a Single Commodity Machine

Recent studies showed that single-machine graph processing systems can b...

Start Late or Finish Early: A Distributed Graph Processing System with Redundancy Reduction

Graph processing systems are important in the big data domain. However, ...

GraphBolt: Streaming Graph Approximations on Big Data

Graphs are found in a plethora of domains, including online social netwo...

An introduction to approximate computing

Approximate computing is a research area where we investigate a wide spe...

Exoshuffle: Large-Scale Shuffle at the Application Level

Shuffle is a key primitive in large-scale data processing applications. ...

Optimizing Graph Processing and Preprocessing with Hardware Assisted Propagation Blocking

Extensive prior research has focused on alleviating the characteristic p...

Experimental Analysis of Distributed Graph Systems

This paper evaluates eight parallel graph processing systems: Hadoop, Ha...