Graphics processing units (GPUs) provide significant improvements in performance and performance-per-watt as compared to traditional multicore CPUs. This energy- efficiency of GPUs has facilitated use of GPUs in many application domains. Albeit energy efficient, GPUs still consume non-trivial power independently of CPUs. It is desired to analyze the power and performance charateristic of GPUs and their causal relation with CPUs. In this paper, we provide a power and performance analysis of GPU-accelerated systems for better understandings of these implications. Our analysis discloses that system energy could be reduced by about 28% retaining a decrease in performance within 1%. Specifically, we identify that energy saving is particularly significant when (i) reducing the GPU memory clock for compute- intensive workload and (ii) reducing the GPU core clock for memory-intensive workload. We also demonstrate that voltage and frequency scaling of CPUs is trivial and even should not be applied in GPU-accelerated systems. We believe that these findings are useful to develop dynamic voltage and frequency scaling (DVFS) algorithms for GPU-accelerated systems.