Over the last twenty years, the open source community has provided more and more software on which the world's High Performance Computing (HPC) systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. But although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual PetaScale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and GPUs. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

We investigated the surface segregation phenomena in the (111) surface of ordered Pt(3)Ti crystal using density functional theory (DFT) calculation (with no configuration sampling) and Monte Carlo (MC) simulation method (employing modified embedded atom method potentials and with extensive configuration sampling). Our DFT study suggested that the off-stoichiometric effect (specifically, a Pt concentration higher than 75 at. %) accounted for the experimentally observed Pt segregation to the outermost layer of the Pt(3)Ti (111). Our MC simulations predicted that in a Pt(3)Ti (111) sample with a Pt concentration slightly above 75 at. %, Pt atoms would segregate to the surface to form a pure Pt outermost layer, while the ordered Pt(3)Ti crystal structure would be maintained in the second layer and below. Moreover, our DFT calculations revealed that the d-band center of the Pt-segregated Pt(3)Ti (111) surface would downshift by 0.21 eV as compared to that of a pure Pt (111) surface. As a result, O adsorption energy on the Pt-segregated Pt(3)Ti (111) surface was found to be at least 0.16 eV weaker than that on the pure Pt (111) surface. Thus, we theoretically modeled the geometric and electronic structures of the Pt-segregated Pt(3)Ti (111) surface and further suggested that the Pt surface segregation could lead to enhanced catalytic activity for oxygen reduction reactions on Pt(3)Ti alloy catalysts.

This paper reviews three important aspects of tribology (adhesion, lubrication and wear) on the atomic scale with a focus on our work on aluminum surfaces. Adhesion is critical to the success of many applications but there is no simple analytical model available to predict adhesion between different materials, so we discuss the use of electronic structure methods to investigate adhesion between Al and various ceramics to determine the factors that control adhesion. Lubricants used to control friction usually include 'boundary additives' to bind the lubricant more strongly to the surface, so that higher stresses can be employed and wear can be reduced. Little is known about how boundary additives bond to Al surfaces, so we used electronic structure methods to investigate that phenomenon. Regarding wear, we review the literature on molecular dynamics simulations to investigate nanoindentation and wear. We discuss our molecular dynamics simulations of nanoindentation and asperity-asperity shear and the effect of temperature, loading rate, interaction strength and geometry.

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