Scheduling complex streaming applications on the Cell processor

International audienceIn this paper, we consider the problem of scheduling streaming applications described by complex task graphs on a heterogeneous multicore processor, the STI Cell BE processor. We first present a theoretical model of the Cell processor. Then, we use this model to express the problem of maximizing the throughput of a streaming application on this processor. Although the problem is proven NP-complete, we present an optimal solution based on mixed linear programming. This allows us to compute the optimal mapping for a number of applications, ranging from a real audio encoder to complex random task graphs. These mappings are then tested on two platforms embedding Cell processors, and compared to simple heuristic solutions. We show that we are able to achieve a good speed-up, whereas the heuristic solutions generally fail to deal with the strong memory and communication constraints

Scheduling complex streaming applications on the Cell processor

In this paper, we consider the problem of scheduling streaming applications described by complex task graphs on a heterogeneous multicore processor, the STI Cell BE processor. We first present a theoretical model of the Cell processor. Then, we use this model to express the problem of maximizing the throughput of a streaming application on this processor. Although the problem is proven NP-complete, we present an optimal solution based on mixed linear programming. This allows us to compute the optimal mapping for a number of applications, ranging from a real audio encoder to complex random task graphs. These mappings are then tested on two platforms embedding Cell processors, and compared to simple heuristic solutions. We show that we are able to achieve a good speed-up, whereas the heuristic solutions generally fail to deal with the strong memory and communication constraints