AdaptPerf: Adaptive Measurement for Computing Power of Heterogeneous Devices Based on NAS

Computing power measurement is critical for effective resource utilization in computing power networks. However, it faces the challenges of heterogeneity of devices, poor scalability with workloads, and discrepancies between measurement methods and models. In this paper, we propose a computing power measurement framework called AdaptPerf to address these issues. AdaptPerf uses the technique of Neural Architecture Search to build a hypernetwork of benchmark models for heterogeneous devices and adaptively selects the ones suitable for them. It eliminates the impact of memory access factors among heterogeneous models by conducting kernel-level latency analysis, achieving equivalency in computing power measurement. We implement AdaptPerf in a real-world environment, constructing a hypernetwork of over 4.2 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> models for heterogeneous devices to autonomously select the benchmark model adaptive to the devices. We use different tools, such as Nsight Compute, Nsight System, and etc., to analyze and evaluate the feasibility and performance of AdaptPerf. It is found that the models generated by AdaptPerf increase parameter volume by 5.19 times and improve by 4.99% in data reuse rate, compared with more than 50 conventional models. AdaptPerf also exhibits superior load adaptability and model complexity compared with existing methods such as MLPerf.