GPGPU Performance Estimation with Core and Memory Frequency Scaling

Contemporary graphics processing units (GPUs) support dynamic voltage and frequency scaling to balance computational performance and energy consumption. However, accurate and straightforward performance estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is essential to determine the best frequency configuration for energy saving. In this article, we reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Compared to the cycle-level simulators, which are too slow to apply on real hardware, our model only needs simple and one-off micro-benchmarks to extract a set of hardware parameters and kernel performance counters without any source code analysis. Our experimental results show that the proposed performance model can capture the kernel performance scaling behaviors under different frequency settings and achieve decent accuracy (average errors of 3.85, 8.6, 8.82, and 8.83 percent on a set of 20 GPU kernels with four modern Nvidia GPUs).