Abstract
In this paper we present Horizon, a distributed graph processing framework achieving close to native performance without penal- izing productivity by providing a multi-layer, multi-abstraction model of computation. Compared to current frameworks, Horizon extends the scope of computation by exposing two notions usually relegated to implementations: graph data models and communica- tion models. Horizon can reduce execution time by an average of 5.3× across different applications and datasets and process an order of magnitude larger graphs when compared to the state of the art.