GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.Comment: 50 pages, 14 figures, 14 table

Multi-GPU Graph Analytics

We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU implementations, our design only requires programmers to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, such as direction optimizing traversal, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system.Comment: 12 pages. Final version submitted to IPDPS 201

Half-titanocene 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate chlorides: Synthesis, characterization and ethylene (co-) polymerization behavior

A series of half-titanocene chloride complexes bearing 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate ligands (L), CpTiLCl₂, has been synthesized in acceptable yields by the stoichiometric reaction of CpTiCl₃ with the respective potassium 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate. All half-titanocene complexes were fully characterized by elemental analysis and NMR spectroscopy, and the molecular structures of the representative complexes C1 and C2 were confirmed as pseudo octahedral at titanium by single-crystal X-ray diffraction. When activated with methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all titanium complexes exhibited good activities (up to 4.8 × 10⁵ g mol⁻¹(Ti) h⁻¹) towards ethylene polymerization. The obtained polyethylene exhibited ultra-high molecular weight (up to 11.82 × 10⁵ g mol⁻¹) with narrow polydispersity. Furthermore, effective co-polymerization of ethylene with 1-hexene or 1-octene was achieved with several percentages of co-monomer incorporation in the resultant polyethylenes

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur

Biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridyl-cobalt complexes: synthesis, characterization and ethylene polymerization behavior

A series of biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridine derivatives reacted with cobalt dichloride in dichloromethane/ethanol to afford the corresponding binuclear cobalt complexes. The cobalt complexes were characterized by FT-IR spectroscopy and elemental analysis, and the structure of a representative complex was confirmed by single-crystal X-ray diffraction. Upon activation with either MAO or MMAO, these cobalt complexes performed with high activities of up to 1.2 × 10⁷ g (mol of Co)⁻¹ h⁻¹ in ethylene polymerization, which represents one of the most active cobalt-based catalytic systems in ethylene reactivity. These biphenyl-bridged bis(imino)pyridylcobalt precatalysts exhibited higher activities than did their mononuclear bis(imino)pyridylcobalt precatalyst counterparts, and more importantly, the binuclear precatalysts revealed a better thermal stability and longer lifetimes. The polyethylenes obtained were characterized by GPC, DSC, and high-temperature NMR spectroscopy and mostly possessed unimodal and highly linear features