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OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff
TUMCREATE, Singapore, Singapore
Concurrency and Computation: Practice and Experience, e5807, 2020

@article{xiao2020openablext,

   title={OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms},

   author={Xiao, Jiajian and Andelfinger, Philipp and Cai, Wentong and Richmond, Paul and Knoll, Alois and Eckhoff, David},

   journal={Concurrency and Computation: Practice and Experience},

   pages={e5807},

   year={2020},

   publisher={Wiley Online Library}

}

The execution of agent-based simulations (ABSs) on hardware accelerator devices such as graphics processing units (GPUs) has been shown to offer great performance potentials. However, in heterogeneous hardware environments, it can become increasingly difficult to find viable partitions of the simulation and provide implementations for different hardware devices. To automate this process, we present OpenABLext, an extension to OpenABL, a model specification language for ABSs. By providing a device-aware OpenCL backend, OpenABLext enables the co-execution of ABS on heterogeneous hardware platforms consisting of central processing units, GPUs, and field programmable gate arrays (FPGAs). We present a novel online dispatching method that efficiently profiles partitions of the simulation during run-time to optimize the hardware assignment while using the profiling results to advance the simulation itself. In addition, OpenABLext features automated conflict resolution based on user-specified rules, supports graph-based simulation spaces, and utilizes an efficient neighbor search algorithm. We show the improved performance of OpenABLext and demonstrate the potential of FPGAs in the context of ABS. We illustrate how co-execution can be used to further lower execution times. OpenABLext can be seen as an enabler to tap the computing power of heterogeneous hardware platforms for ABS.
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