Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures
Jin Wang, Norman Rubin, Haicheng Wu, and Sudhakar Yalamanchili. “Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures.” Sixth Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-6). March 2013.
Abstract
The wide usage of GPGPU programming models and compiler techniques enables the optimization of data-parallel programs on commodity GPUs. However, mapping GPGPU applications running on discrete parts to emerging integrated heterogeneous architectures such as the AMD Fusion APU and Intel Sandy/Ivy bridge with the CPU and the GPU on the same die has not been well studied.
Classic time-step simulation applications represented by agent-based models have the intrinsic parallel structure that is a good fit for GPGPU architectures. However, when mapping these applications directly to the integrated GPUs, the performance may degrade due to less computation units and lower clock speed.
This paper proposes an optimization to the GPGPU implementation of the agent-based model and illustrates it in the traffic simulation example. The optimization adapts the algorithm by moving part of the workload to the CPU to leverage the integrated architecture and the on-chip memory bus which is faster than the PCIe bus that connects the discrete GPU and the host. The experiments on discrete AMD Radeon GPU and AMD Fusion APU demonstrate that the optimization can achieve 1.08-2.71x performance speedup on the integrated architecture over the discrete platform.
Classic time-step simulation applications represented by agent-based models have the intrinsic parallel structure that is a good fit for GPGPU architectures. However, when mapping these applications directly to the integrated GPUs, the performance may degrade due to less computation units and lower clock speed.
This paper proposes an optimization to the GPGPU implementation of the agent-based model and illustrates it in the traffic simulation example. The optimization adapts the algorithm by moving part of the workload to the CPU to leverage the integrated architecture and the on-chip memory bus which is faster than the PCIe bus that connects the discrete GPU and the host. The experiments on discrete AMD Radeon GPU and AMD Fusion APU demonstrate that the optimization can achieve 1.08-2.71x performance speedup on the integrated architecture over the discrete platform.
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Citation
@inproceedings{wang-gpgpu6,
author={Wang, J. and Rubin, N. and Wu, H. and Yalamanchili, S.},
booktitle={Sixth Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-6)},
title={Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures},
year={2013},
month={March},
}
author={Wang, J. and Rubin, N. and Wu, H. and Yalamanchili, S.},
booktitle={Sixth Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-6)},
title={Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures},
year={2013},
month={March},
}
