POIGEM: A Programming-Oriented Instruction Level GPU Energy Model for CUDA Program

Abstract

GPU architectures tend to be increasingly important in multi-core era nowadays due to their formidable computational horsepower. With the assistant of effective programming paradigms as CUDA, GPUs are widely adopted to accelerate scientific applications. Meanwhile, the surging energy consumption by GPUs becomes a major challenge to both GPU architects and programmers. In addition to the efforts designing energy efficient GPU architecture, comprehensive understanding on how programming affects the energy consumption of GPU application is also indispensable from the programmer perspective.

In this paper, we present a programming-oriented PTX instruction level energy model to provide programmers the ability of predicting the energy consumption of their program. Distinct from previous models which require hardware performance counters or architectural simulations, our model relies on the PTX instruction of a CUDA program which is not only portable but also accurate. With the selected PTX instructions based on empirical study, we apply linear regression to build the GPU energy model. One appealing advantage of our model is that it does not require any instrumentation or profiling of the GPU application during execution. Actually, our model is able to advise the programmers step by step to illustrate how their way of programming impacts the final energy consumption, especially at the stage of hacking the codes. Our model is evaluated on NVIDIA GeForce GTX 470 with Rodinia benchmark suites. The results show the accuracy of our model is promising with average prediction error below 3.7%. With the help of our GPU energy model, the programmers are gaining valuable insights to improve the energy efficiency of the application.