Graphics processing units (GPUs) support dynamic voltage and frequency scaling to balance computational performance and energy consumption. However, simple and accurate performance estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is important to decide the best frequency configuration for energy saving. We reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Over a 2 x range of both core and memory frequencies among 20 GPU kernels, our model achieves accurate results (4.83 error on average) with real hardware. Compared to the cycle-level simulators, our model only needs simple micro-benchmarks to extract a set of hardware parameters and kernel performance counters to produce such high accuracy without kernel source analysis.
This paper presents CRISP, the first runtime analytical model of performance in the face of changing frequency in a GPGPU. It shows that prior models not targeted at a GPGPU fail to account for important characteristics of GPGPU execution, including the high degree of overlap between memory access and computation and the frequency of store-related stalls. CRISP provides significantly greater accuracy than prior runtime performance models, being within 4 on average when scaling frequency by up to 7X. Using CRISP to drive a runtime energy efficiency controller yields a 10.7 improvement in energy-delay product, vs 6.2 attainable via the best prior performance model. Dynamic Voltage and Frequency Scaling (DVFS) on Graphics Processing Units (GPUs) components is one of the most promising power management strategies, due to its potential for significant power and energy savings. However, there is still a lack of simple and reliable models for the estimation of the GPU power consumption under a set of different voltage and frequency levels. Accordingly, a novel GPU power estimation model with both core and memory frequency scaling is herein proposed. This model combines information from both the GPU architecture and the executing GPU application and also takes into account the non-linear changes in the GPU voltage when the core and memory frequencies are scaled. The model parameters are estimated using a collection of 83 microbenchmarks carefully crafted to stress the main GPU components. Based on the hardware performance events gathered during the execution of GPU applications on a single frequency configuration, the proposed model allows to predict the power consumption of the application over a wide range of frequency configurations, as well as to decompose the contribution of different parts of the GPU pipeline to the overall power consumption. Validated on 3 GPU devices from the most recent NVIDIA microarchitectures (Pascal, Maxwell and Kepler), by using a collection of 26 standard benchmarks, the proposed model is able to achieve accurate results (7 , 6 and 12 mean absolute error) for the target GPUs (Titan Xp, GTX Titan X and Tesla K40c).