Fast cosimulation of transformative systems with OS support on SMP computer

Transformative applications are a class of dataflow computation characterized by iterative behavior. The problem of partitioning a transformative application specification to a set of available hardware (HW) and software (SW) processing elements (PEs) and derivation of a job execution order (scheduling) on them has been quite well studied, but the problem of obtaining fast simulation of these applications poses different constraints. In this paper, we propose an efficient framework for a symmetric multi-processor (SMP) simulation host to achieve fast HW/SW co-simulation for transformative applications, given the partition solutions and the derived schedulers. The framework overcomes the limitations in existing Linux SMP kernel and requires only a reasonable amount of modifications to it. We also present a heuristic algorithm which effectively assigns simulation tasks to the processors on the simulation host, considering both average job simulation time on each processor and other simulation overhead. Our experiments show that the algorithm is able to find satisfactory suboptimal solutions with very little computation time. Based on the task assignment solution, the simulation time can be reduced by 25% to 50% from the obvious but naive approach.