Massive GPU acceleration processors have been used in high-performance computing systems. The Dennard scaling has led to power and thermal constraints limiting the performance of such systems. The demand for both increased performance and energy efficiency is highly desired. This article presents a multilayer low-power optimization method for warps and tasks parallelisms. We present a dynamic frequency regulation scheme for performance parameters in terms of load balance and load imbalance. The method monitors the energy parameters in runtime and adjusts adaptively the voltage level to ensure performance efficiency with energy reduction. The experimental results show that the multilayer low-power optimization with dynamic frequency regulation can achieve 40% energy consumption reduction with only 1.6% performance degradation, thus reducing 59% maximum energy consumption. It can further save about 30% energy consumption in comparison with the single-layer energy optimization.

Summary With the continued development of hardware and software, graphics processing unit (GPU) has been used in general purpose computational fields, while accelerating applications for CPUs. To achieve high computing performance, a GPU typically includes hundreds of computing units. The high density of computing resource on‐chip incurs high power consumption as well as engendering high performance. The power consumption problem has become one of the most important problems for the development of GPUs. Focusing on a CPU‐GPU heterogeneous parallel system, this research proposed an architecture‐level GPU energy model, with the aim of reducing system energy demand and improving system efficiency. Taking the influences of memory and temperature on GPU energy demand into account, a dynamic energy model based on division of computation and memory and a static energy model based on real‐time temperature perception were established. Validation, through the evaluation and comparative analysis of nine typical GPU programmes, demonstrated that these models could reduce chip energy demand under the performance constraint conditions imposed. Copyright © 2014 John Wiley & Sons, Ltd.

There is heavy computation involved in Global / Regional Assimilation and Prediction System (GRAPES) which is a typical non-linear discrete system of the Earth's atmosphere developed for numerical weather prediction. Researchers have recently paid a lot of attention to the parallel acceleration of the GRAPES model by low-cost, low-power, high-performance Graphics Processor Units (GPUs). We implement in this paper the acceleration for the GRAPES model in GPUs, which is however not as efficient as supposed. On this basis, we further propose strategies to optimize the performance, including the ease of data transmission time, reducing the amount of device memory loaded and used to store various objects, and avoiding the branches of thread control flows. One can find from the experiments that, parallel acceleration with GPUs helps to improve the performance of GRAPES in terms of measures like accuracy and timeliness.

Data grid is a very important and useful technique to process the large number of data produced by scientific experiments and simulations. However, the high latency of the Internet turns to be the bottleneck in accessing the files in the grid. The replication strategy can shorten the time of getting the files by creating many replicas and storing the replicas in appropriate locations. Restricted by the storage capacity, it is important to design an effective algorithm for the replication replacement task. In this paper, we propose a novel replacement strategy which calculates the weight of replicas based on the access times in the future time window, the access bandwidth and the file size. Results from the simulation procedure show the better performance of our algorithm than former ones.