Credit: KAIST
A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale Graph Processing Simulation) by the developer Professor Min-Soo Kim from the School of Computing at KAIST, it can process a graph with one trillion edges using a single computer.
Graphs are widely used to represent and analyze real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience. As the number of graph applications increases rapidly, developing and testing new graph algorithms is becoming more important than ever before. Nowadays, many industrial applications require a graph algorithm to process a large-scale graph (e.g., one trillion edges). So, when developing and testing graph algorithms such for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilizing large
Processes a trillion edge graph without touching main memory
Korean research institute Kaist has found a way to develop a one trillion edge graph algorithm on a single computer without storing the graph in the main memory or on disc.
‘Develop’ is the important word here, as the research covers honing algorithms on synthetic data sets rather than on real big data.
“Graphs are widely used to represent and analyse real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience,” said Kaist. “When developing and testing algorithms for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilising large-scale real graphs is very limited due to their being proprietary or being practically impossible to collect.”