GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millions of nodes. However, how to scale them to networks that have tens of millions or even hundreds of millions of nodes remains a challenging problem. In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system. On the CPU end, augmented edge samples are parallelly generated by random walks in an online fashion on the network, and serve as the training data. On the GPU end, a novel parallel negative sampling is proposed to leverage multiple GPUs to train node embeddings simultaneously, without much data transfer and synchronization. Moreover, an efficient collaboration strategy is proposed to further reduce the synchronization cost between CPUs and GPUs. Experiments on multiple real-world networks show that GraphVite is super efficient. It takes only about one minute for a network with 1 million nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20 hours for a network with 66 million nodes and 1.8 billion edges. Compared to the current fastest system, GraphVite is about 50 times faster without any sacrifice on performance.Comment: accepted at WWW 201

Graphene oxide and graphene based catalysts in photochemical ractions

Graphene has impressible absorbing ability and its electron transmission capacity makes it a great prosperity in many science horizons. In this study graphene or graphite nitride has been employed as a carrier in order to modify TiO2, ZnO and Ta2O5 photocatalysts.Graphene modified TiO2 particles were obtained by a sol-gel method from titanium isoproproxide (or P25) and reduced graphene oxide (RGO). The X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FE-SEM), UV-vis diffuse reflectance (UV-vis DRS) and thermo gravimetric differential thermal analysis (TG-DTA) were investigated over the samples. The diffuse reflectance spectra (DRS) studies indicate that G-TiO2 has a significant light absorption increasing and red shift of absorption peak. G-TiO2 photocatalyst could decompose methylene blue under visible light (> 430 nm). G-TiO2 synthesised from titanium isoproproxide presented better activity than G-TiO2 (P25). The catalysts could also produce ●OH and [O2]- radicals via electron scavengers (peroxymonosulphate, peroxydisulphate and hydrogen peroxide) to enhance degradation process with visible illumination.ZnO loaded RGO photocatalysts were synthesized through Zn powder and graphite oxide. The structural, morphological, and physicochemical properties of the samples were thoroughly investigated by XRD, FT-IR, FE-SEM, UV-visible DRS, TG-DTA, and Raman spectroscopy. Zn powder could successfully reduce GO and ZnO was obtained simultaneously by one-step hydrothermal method. RGO-ZnO photocatalysts could bleach MB under UV-vis illumination.Three different compounds: ammonia, graphene and C3N4 were utilized to dope tantalum pentoxide photocatalyst. Catalysts were analyzed by X-ray diffraction, UV–vis diffuse reflectance spectra and FTIR spectroscopy. The photocatalytic behavior was thorough investigated in bleaching methylene blue under UV-visible illuminations; the modified catalysts could decompose methylene blue, showing better activity than undoped Ta2O5. However, only N-doped Ta2O5 will show activity under visible light

Minimal Restriping For Data Center Expansion

A system and a method are proposed to minimize restriping during data center expansion. Minimal restriping technique takes into account the old data center topology, and aims to minimize restriping between the new topology and the old topology. A low complexity minimal restriping algorithm proposed is used to minimize restriping. The topology solution is also guaranteed to have high network capacity, because it satisfies superblock ports, spineblock ports,and superblock-spineblock level balancedness constraints. The constraints may consider the number of links in original topology and the algorithm may determine the optimum new topology that minimizes restriping. The disclosed technique may not cause significant bandwidth reduction, and thus greatly shortens the data center expansion time