VarySched: A framework for variable scheduling in heterogeneous environments

Despite many efforts to better utilize the potential of GPUs and CPUs, it is far from being fully exploited. Although many tasks can be easily sped up by using accelerators, most of the existing schedulers are not flexible enough to really optimize the resource usage of the complete system. The main reasons are (i) that each processing unit requires a specific program code and that this code is often not provided for every task, and (ii) that schedulers may follow the run-until-completion model and, hence, disallow resource changes during runtime. In this paper, we present VarySched, a configurable task scheduler framework tailored to efficiently utilize all available computing resources in a system. VarySched allows a more fine-grained task-to-resource placement which is even further enhanced by allowing the tasks to migrate to another resource during their runtime. In addition, VarySched can manage multiple scheduling strategies - optimizing, for instance, throughput or energy efficiency - and switch between them at any time