Laius: T owards l atency a wareness and i mproved u tilization of s patial multitasking accelerators in datacenters

Datacenters use accelerators to provide the significant compute throughput required by emerging user-facing services. The diurnal user access pattern of user-facing services provides a strong incentive to co-located applications for better accelerator utilization, and prior work has focused on enabling co-location on multicore processors and traditional non-preemptive accelerators. However, current accelerators are evolving towards spatial multitasking and introduce a new set of challenges to eliminate QoS violation. To address this open problem, we explore the underlying causes of QoS violation on spatial multitasking accelerators. In response to these causes, we propose Laius, a runtime system that carefully allocates the computation resource to co-located applications for maximizing the throughput of batch applications while guaranteeing the required QoS of user-facing services. Our evaluation on a Nvidia RTX 2080Ti GPU shows that Laius improves the utilization of spatial multitasking accelerators by 20.8%, while achieving the 99%-ile latency target for user-facing services.