Scheduling processing of real-time data streams on heterogeneous multi-GPU systems
Uri Verner, Assaf Schuster, Mark Silberstein, Avi Mendelson · 2012
Processing vast numbers of data streams is a common problem in modern computer systems and is known as the "online big data problem." Adding hard real-time constraints to the processing makes the scheduling problem a very challenging task that this paper aims to address. In such an environment, each data stream is manipulated by a (different) application and each datum (data packet) needs to be processed within a known deadline from the time it was generated. This work assumes a central compute engine which consists of a set of CPUs and a set of GPUs. The system receives a configuration of multiple incoming streams and executes a scheduler on the CPU side. The scheduler decides where each data stream will be manipulated (on the CPUs or on one of the GPUs), and the order of execution, in a way that guarantees that no deadlines will be missed. Our scheduler finds such schedules even for workloads that require high utilization of the entire system (CPUs and GPUs).