Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering. Many GNN training frameworks have been developed, but they are usually designed as a set of manually written, GNN-specific operators plugged into existing deep learning systems, which incurs high memory consumption, poor data locality, and large semantic gap between algorithm design and implementation. This paper proposes the Seastar system, which presents a vertex-centric programming model for GNN training on GPU and provides idiomatic python constructs to enable easy development of novel homogeneous and heterogeneous GNN models. We also propose novel optimizations to produce highly efficient fused GPU kernels for forward and backward passes in GNN training. Compared with the state-of-the art GNN systems, DGL and PyG, Seastar achieves better usability, up to 2 and 8 times less memory consumption, and 14 and 3 times faster execution, respectively.

Monotask is a unit of work that uses only a single type of resource (e.g., CPU, network, disk I/O). While monotask was primarily introduced as a means to reason about job performance, in this paper we show that this fine-grained, resource-oriented abstraction can be leveraged by job schedulers to maximize cluster resource utilization. Although recent cluster schedulers have significantly improved resource allocation, the utilization of the allocated resources is often not high due to inaccurate resource requests. In particular, we show that existing scheduling mechanisms are ineffective for handling jobs with dynamic resource usage, which exists in common workloads, and propose a resource negotiation mechanism between job schedulers and executors that makes use of monotasks. We design a new framework, called Ursa, which enables the scheduler to capture accurate resource demands dynamically from the execution runtime and to provide timely, fine-grained resource allocation based on monotasks. Ursa also enables high utilization of the allocated resources by the execution runtime. We show by experiments that Ursa is able to improve cluster resource utilization, which effectively translates to improved makespan and average JCT.