Boosting Graph Analytics by Tuning Threads and Data Affinity on NUMA Systems

The execution of large real-world graphs, such as web searches and social networks, has been boosting by modern HPC systems. However, their irregular communication patterns and poor data locality impose many challenges, mainly when executed on NUMA systems. As we show in this paper, there is no one-fits-all configuration for threads/data mapping, and the best combination will vary according to the NUMA system, graph algorithm, and input graph at hand. Based on that, we propose Graphith: a framework that automatically enhances graph processing performance by adapting its execution considering the variables mentioned above. Graphith also goes one step further and improves the existing policies: it uses a Genetic Algorithm to fine-tune the thread-to-core allocation combined with data mapping policies. With that, Graphith improves in 21%, on average, the default execution, and is, on average, 7% better than the best possible combination of standard policies.