Data science development, including artificial intelligence, data analysis, network analysis, social media analysis, knowledge graphs, etc., needs the ability to analyse and reason about large-scale data represented in graphs. The challenge is implementing fast graph algorithms that perform well on large sparse graphs, running parallel on multiple machines. How could we accelerate graph computation by exploiting high-performance computing?
In teams of 1-3, participants will develop and implement algorithms to solve tasks. The details of the algorithmic problem, together with example test data and evaluation criteria, will be revealed later.
