Co-Allocation Based Scheduling For Parallel Systems

State-of-the-art supercomputers are made up of multiple types of resources. User jobs also have wide spectrum of resource requirements. Hence, a supercomputer can be thought of as a collection of heterogeneous resources with heterogeneous usage requirements from the users. Schedulers for such systems are challenged by several issues like scalability, GPU, topology and energy awareness. We view each scheduling step as solving a coallocation problem, i.e. the problem of allocating multiple resources simultaneously to jobs. Collection of jobs can be repeatedly taken from the front of the job queue (i.e. a window of jobs) and a co-allocation problem formulated as an (integer) linear program (ILP/LP) can be solved. ILP formulations for single-type and multiple instances, a CPU-GPU and generalized systems are provided. Co-allocation solver is applied to both the window of jobs and the backfilled jobs. Simulation results show effectiveness of our approaches when compared with pure first-come-firstserved schedulers.