TITLE : Practical Steady-State Scheduling for Tree-Shaped Task Graphs
In this work, we focus on the problem of scheduling a collection of similar task graphs on a heterogeneous platform, when the task graph is a tree. We rely on steady-state scheduling techniques, and try to optimize the throughput of the system. Contrarily to previous studies, we concentrate on practical aspects of steady-state scheduling, when dealing with a collection (or batch) of limited size. We focus here on two optimizations. The first one consists in reducing the processing time of each task graph, thus making steady-state scheduling applicable to smaller batches. The second one consists in degrading a little the optimal-throughput solution to get a simpler solution, more efficient on small batches. We present our optimizations in details, and show that they both help to overcome the limitation of steady-state scheduling: our simulations show that we are able to reach a better efficiency on small batches, to reduce the size of the buffers, and to significantly decrease the processing time of a single task graph (latency).