In this paper, we present PPT-GPU, a scalable performance prediction toolkit for GPUs. PPT-GPU achieves scalability through a hybrid high-level modeling approach where some computations are extrapolated and multiple parts of the model are parallelized. The tool primary prediction models use pre-collected memory and instructions traces of the workloads to accurately capture the dynamic behavior of the kernels.

GPUs are prevalent in modern computing systems at all scales. They consume a significant fraction of the energy in these systems. However, vendors do not publish the actual cost of the power/energy overhead of their internal microarchitecture. In this paper, we accurately measure the energy consumption of various PTX instructions found in modern NVIDIA GPUs. We provide an exhaustive comparison of more than 40 instructions for four high-end NVIDIA GPUs from four different generations (Maxwell, Pascal, Volta, and Turing). Furthermore, we show the effect of the CUDA compiler optimizations on the energy consumption of each instruction. We use three different software techniques to read the GPU on-chip power sensors, which use NVIDIA's NVML API and provide an in-depth comparison between these techniques. Additionally, we verified the software measurement techniques against a custom-designed hardware power measurement. The results show that Volta GPUs have the best energy efficiency of all the other generations for the different categories of the instructions. This work should aid in understanding NVIDIA GPUs' microarchitecture. It should also make energy measurements of any GPU kernel both efficient and accurate.

In this work, we accurately measure the energy consumption of the different instructions that can be executed in modern NVIDIA GPGPUs. We use three different software techniques to read the GPU on-chip power sensors, which use NVIDIA's NVML API and provide an in-depth comparison between these techniques. Additionally, we verified the software measurement techniques against a custom-designed hardware power measurement. The results show that Volta GPUs have the best energy efficiency of all the other generations for the different categories of the instructions. This work should give GPU architects and developers a more concrete understanding of these representative NVIDIA GPUs' microarchitecture. It should also make energy measurements of any GPU kernel both efficient and accurate.