Approximate computing enables processing of large-scale graphs by trading off quality for
performance. Approximate computing techniques have become critical not only due to the …

Distributed frameworks, such as MapReduce and Spark, have been developed by industry
and research groups to analyze the vast amount of data that is being generated on a daily …

Graph algorithms have gained popularity and are utilized in high performance and mobile
computing paradigms. Input dependence due to input graph changes leads to performance …

Benchmarking of high performance computing systems can help provide critical insights for
efficient design of computing systems and software applications. Although a large number of …

In recent years, a large number of graph processing frameworks have been introduced, with
their goal to simplify analysis of real-world graphs on commodity hardware. Additionally, the …

With the prevalence of graph data in real-world applications and their ever-increasing size,
many graph computing systems have been developed in recent years to scale the …

Graph analysis has revealed patterns and relationships hidden in data from a variety of
domains such as transportation networks, social networks, clinical pathways, and …

Recently, large-scale graph analytics has become a very popular topic owing to the
emergence of gigantic graphs whose number of vertices and edges is in millions, billions or …

The rapidly growing number of large network analysis problems has led to the emergence of
many parallel and distributed graph processing systems-one survey in 2014 identified over …

It is common for real-world applications to analyze big graphs using distributed graph
processing systems. Popular in-memory systems require an enormous amount of resources …