Models of machines and computation for mapping in multicomputers
ACM Computing Surveys (CSUR)
Performance improvement of multi-processor systems cosimulation based on SW analysis
Proceedings of the conference on Design, automation and test in Europe
Hardware-Software partitioning and pipelined scheduling of transformative applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Minimizing Synchronization Overhead in Statically Scheduled Multiprocessor Systems
ASAP '95 Proceedings of the IEEE International Conference on Application Specific Array Processors
Minimizing network contention for mapping tasks onto massively parallel computers
PDP '95 Proceedings of the 3rd Euromicro Workshop on Parallel and Distributed Processing
Optimizing synchronization in multiprocessor DSP systems
IEEE Transactions on Signal Processing
Combined data-driven and event-driven scheduling technique for fast distributed cosimulation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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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.