High Performance Clustering of Social Images in a Map- Collective Programming Model

Large-scale iterative computations are common in many important data mining and machine learning algorithms needed in analytics and deep learning. In most of these applications, individual iterations can be specified as MapReduce computations, leading to the Iterative MapReduce programming model for efficient execution of data-intensive iterative computations interoperably between HPC and cloud environments. Further one needs additional communication patterns from those familiar in MapReduce and we base our initial architecture on collectives that integrate capabilities developed by the MPI and MapReduce communities. This leads us to the Map-Collective programming model which here we develop based on requirements of a range of applications by extending our existing Iterative MapReduce environment Twister. This paper studies the implications of large scale Social Image clustering where large scale problems study 10-100 million images represented as points in a high dimensional (up to 2048) vector space which need to be divided into up to 1-10 million clusters. This Kmeans application needs 5 stages in each iteration: Broadcast, Map, Shuffle, Reduce and Combine, and this paper focuses on collective communication stages where large data transfers demand performance optimization. By comparing and combining ideas from MapReduce and MPI communities, we show that a topologyaware and pipeline-based broadcasting method gives better performance than other MPI and (Iterative) MapReduce systems

A Piston-Swiveling-Cylinder Pair in a High Water-Based Hydraulic Motor with Self-Balanced Distribution Valves

To improve the low viscosity and poor lubrication characteristics of high-water-based hydraulic liquid, the abrasion and leakage problems in hydraulic components need to be addressed. In a high water-based hydraulic motor with self-balanced distribution valve (HWBHM-SDV), there are two key friction pairs: the piston-crankshaft pair and piston-swivelling-cylinder (PSC) pair. To study the working performance of the PSC pair in HWBHM-SDV, we firstly designed the structural parameters. We found that, within the working speed 0–100 rpm, the leakage in the PSC pair is mainly caused by pressure-gradient flow, and the influence of the seal will not be significant when the seal length is 24 mm. Then, the friction coefficients of different matching materials were tested. It was found that the friction coefficient of 316L stainless steel with OVINO-GIC (OVINO-graphite intercalated compound) coating (316L-GIC)/PEEK reinforced with 30% carbon fibre (PEEK-30CF) is about 0.02~0.04, and the friction coefficient of 316L-GIC/316L-GIC is about 0.05–0.07. Finally, the influences of factors (clearance, temperature, pressure, and material) on leakage performance were analysed based on an orthogonal test method considering fluid-structure interaction. It was found that clearance has the most significant influence on leakage, followed by pressure and liquid temperature, and the difference between matching materials 316L-GIC/316L-GIC and 316L-GIC/PEEK-30CF is insignificant when the clearance is less than 8 μm and the working pressure is less than 10 MPa. Moreover, the difference in volume efficiency loss between theoretical analysis and calculated result considering fluid-structure interaction increases with the increase of working pressure and working speed. To ensure good working performance of a PSC pair, matching materials 316L-GIC/PEEK-30CF could be selected for pressures below 15 MPa, while 316L-GIC/316L-GIC could be used at 28 MPa

Coal and Gangue Underground Pneumatic Separation Effect Evaluation Influenced by Different Airflow Directions

Coal and gangue underground pneumatic separation is of key importance for green mining. Two kinds of arrangement schemes for high-pressure value used in pneumatic separation system are proposed in this study. Pneumatic separation effects are examined under different arrangement of high-pressure value. Here, theoretical pneumatic separation distance formulas of mineral particles affected by different airflow directions are derived and validated by a series of numerical simulations and orthogonal experiments. In the following analysis, the effects of gangue diameter (d), conveyor velocity (v0), and the height difference between conveyor belt and air nozzle (hp) are mainly considered. The numerical simulation and experimental results indicate that pneumatic separation effects under the condition of u and v0 being in the opposite direction will be better than that of u and v0 being in the same direction. The pneumatic separation distance ΔS shows a decreasing trend with the increasing of the three factors. The study also shows that gangue diameter has the most significant influence on separation distance, followed by conveyor velocity v0 and height difference hp