Based on a two-qubit isotropic Heisenberg XY model under a constant external magnetic field,we construct a four-level entangled quantum heat engine（QHE）.The expressions for the heat transferred,the work,and the effi...Based on a two-qubit isotropic Heisenberg XY model under a constant external magnetic field,we construct a four-level entangled quantum heat engine（QHE）.The expressions for the heat transferred,the work,and the efficiency are derived.Moreover,the influence of the entanglement on the thermodynamic quantities is investigated analytically and numerically.Several interesting features of the variations of the heat transferred,the work,and the efficiency with the concurrences of the thermal entanglement of two different thermal equilibrium states in zero and nonzero magnetic fields are obtained.展开更多
GPUs are of increasing interests in the multi-core era due to their high computing power. However, the power consumption caused by the rising performance of GPUs has been a general concern. As a consequence, it is bec...GPUs are of increasing interests in the multi-core era due to their high computing power. However, the power consumption caused by the rising performance of GPUs has been a general concern. As a consequence, it is becoming an imperative demand to optimize the GPU power consumption, among which the power consumption estimation is one of the important and useful solutions. In this work, we present a novel statistical model that is capable of dynamically estimating the power consumption of the AMD's integrated GPU (iGPU). Precisely, we adopt the linear regression for power consumption modeling and propose a mechanism called kernel extension to lengthen the kernel execution time so that we can sample system data for model evaluation. The results show that the median absolute error of our model is less than 3%. Furthermore,to reduce the latency of power consumption estimation, we conduct a study to explore the possibility to simplify our statistical model. The results suggest that the accuracy and stability is still acceptable in the simplified model. This provides a desirable option to reduce our model latency when it is applied to the iGPU power consumption optimization in the real world.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 11065008)
文摘Based on a two-qubit isotropic Heisenberg XY model under a constant external magnetic field,we construct a four-level entangled quantum heat engine（QHE）.The expressions for the heat transferred,the work,and the efficiency are derived.Moreover,the influence of the entanglement on the thermodynamic quantities is investigated analytically and numerically.Several interesting features of the variations of the heat transferred,the work,and the efficiency with the concurrences of the thermal entanglement of two different thermal equilibrium states in zero and nonzero magnetic fields are obtained.
文摘GPUs are of increasing interests in the multi-core era due to their high computing power. However, the power consumption caused by the rising performance of GPUs has been a general concern. As a consequence, it is becoming an imperative demand to optimize the GPU power consumption, among which the power consumption estimation is one of the important and useful solutions. In this work, we present a novel statistical model that is capable of dynamically estimating the power consumption of the AMD's integrated GPU (iGPU). Precisely, we adopt the linear regression for power consumption modeling and propose a mechanism called kernel extension to lengthen the kernel execution time so that we can sample system data for model evaluation. The results show that the median absolute error of our model is less than 3%. Furthermore,to reduce the latency of power consumption estimation, we conduct a study to explore the possibility to simplify our statistical model. The results suggest that the accuracy and stability is still acceptable in the simplified model. This provides a desirable option to reduce our model latency when it is applied to the iGPU power consumption optimization in the real world.
