Lattice-based scheduling for multi-FPGA systems

Abstract

Accelerators are becoming increasingly prevalent in distributed computation. FPGAs have been shown to be fast and power efficient for particular tasks, yet scheduling on FPGA-based multi-accelerator systems is challenging when workloads vary significantly in granularity in terms of task size and/or number of computational units required. We present a novel approach for dynamically scheduling tasks on networked multi-FPGA systems which maintains high performance, even in the presence of irregular tasks. Our topological ranking-based scheduling allows realistic irregular workloads to be processed while maintaining a significantly higher level of performance than existing schedulers.