(Georgia Institute of Technology, 2013-08)
Kim, Hyesoon; Vuduc, Richard
The main goal of this project is to develop a framework that simplifies programming for heterogeneous platforms. The framework consists of (i) a runtime system to generate code that partitions and schedules work among heterogeneous processors, (ii) a general automated tuning mechanism based on machine learning and (iii) performance and power modeling techniques and profiling techniques to aid code generation.
(Georgia Institute of Technology, 2013)
Lim, Jieun; Lakshminarayana, Nagesh B.; Kim, Hyesoon; Song, William; Yalamanchili, Sudhakar; Sung, Wonyong
Graphics Processing Units (GPUs) are very popular
for both graphics and general-purpose applications. Since GPUs
operate many processing units and manage multiple levels of
memory hierarchy, they consume a significant amount of power.
Although several power models for CPUs are available, the power
consumption of GPUs has not been studied much yet. In this
paper, we develop a new power model for GPUs by utilizing
McPAT, a CPU power tool. We generate initial power model
data from McPAT with a detailed GPU configuration, and then
adjust the models by comparing them with empirical data.We use
the NVIDIA’s Fermi architecture for building the power model,
and our model estimates the GPU power consumption with an
average error of 7.7% and 12.8% for the microbenchmarks and
Merge benchmarks, respectively.