A practical performance model for compute and memory bound GPU kernels

Performance prediction of GPU kernels is generally a tedious procedure with unpredictable results. In this paper, we provide a practical model for estimating performance of CUDA kernels on GPU hardware in an automated manner. First, we propose the quadrant-split model, an alternative of the roofline visual performance model, which provides insight on the performance limiting factors of multiple devices with different compute-memory bandwidth ratios with respect to a particular kernel. We elaborate on the compute-memory bound characteristic of kernels. In addition, a micro-benchmark program was developed exposing the peak compute and memory transfer performance using variable operation intensity. Experimental results of executions on different GPUs are presented. In the proposed performance prediction procedure, a set of kernel features is extracted through an automated profiling execution which records a set of significant kernel metrics. Additionally, a small set of device features for the target GPU is generated using micro-benchmarking and architecture specifications. In conjunction of kernel and device features we determine the performance limiting factor and we generate an estimation of the kernel’s execution time. We performed experiments on DAXPY, DGEMM, FFT and stencil computation kernels using 4 GPUs and we showed an absolute error in predictions of 10.1% in the average case and 25.8% in the worst case