Abstract
Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance
and energy efficiency for high throughput applications. Although GPUs consume large amounts of power,
their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently,
continued development relies on understanding their power consumption. This work is a survey of
GPU power modeling and profiling methods with increased detail on noteworthy efforts. As direct measurement
of GPU power is necessary for model evaluation and parameter initiation, internal and external power
sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation
to power use and performance. Statistical correlation between power and performance counters has
yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles
for power modeling. Developments and challenges of counter-based GPU power modeling is discussed.
Often building on the counter-based models, research efforts for GPU power simulation, which make power
predictions from input code and hardware knowledge, provide opportunities for optimization in programming
or architectural design. Noteworthy strides in power simulations for GPUs are included along with
their performance or functional simulator counterparts when appropriate. Lastly, possible directions for
future research are discussed.
