QIG: Quantifying the Importance and Interaction of GPGPU Architecture Parameters

Graphic processing units (GPUs) are widely used for general-purpose computing-so-called GPGPU computing. GPUs feature a large number of architecture parameters, resulting in a huge design space. To quickly explore this design space and identify the optimum architecture for a group of widely used computing kernels, it is critical to know how important each parameter is and how strongly these parameters interact with each other. This paper proposes an ensemble-learning-based approach, called quantifying the importance and interaction of Gpgpu architecture parameters (QIG), to quantify the importance of architecture parameters and their interactions with respect to performance. QIG employs a stochastic gradient boosted regression tree to construct performance models using performance data from a random set of GPU architectures. Leveraging these models, QIG observes the impact of each architecture parameter on performance, and calculates its importance and interaction intensity with other parameters. Using 25 widely used GPGPU kernels, we demonstrate that QIG accurately ranks the importance and interaction of GPU architecture parameters while the previously proposed Plackett-Burman design does not. Moreover, we show that QIG leads to a substantially more accurate performance model compared to prior work, including Starchart and approaches using artificial neural networks and supported vector machines: average error of 4.2% for QIG versus 23+% for prior work. Finally, QIG reveals a number of interesting insights for GPU architectures running GPGPU workloads.