PGAS for graph analytics: can one sided communications break the scalability barrier?

Abstract

As the world is becoming increasingly interconnected and systems increasingly complex. Therefore, technologies that can analyze connected systems and their dynamic characteristics become indispensable. Consequently, the last decade has seen increasing interest in graph analytics, which allows obtaining insights from such connected data. Parallel graph analytics can reveal the workings of intricate systems and networks at massive scales, which are found in diverse areas such as social networks, economic transactions, and protein interactions. While sequential graph algorithms have been studied for decades, the recent availability of massive datasets has given rise to the need for parallel graph processing, which poses unique challenges.