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JXPAMG is a parallel algebraic multigrid (AMG) solver for solving the extreme-scale, sparse linear systems on modern supercomputers. JXPAMG features the following characteristics: 1) It integrates some application-driven parallel AMG algorithms, including αSetup-AMG (adaptive Setup based AMG), AI-AMG (algebraic interface based AMG) and AMG-PCTL (physical-variable based coarsening two-level AMG); 2) A hierarchical parallel sparse matrix data structure, labeled hierarchical parallel Compressed Sparse Row (hpCSR), that matches the computer architecture is designed, and the highly scalable components based on hpCSR are implemented; 3) A flexible software architecture is designed to separate algorithm development from implementation. These characteristics allow JXPAMG to use different AMG strategies for different application features and architecture features, and thereby JXPAMG becomes aware of changes in these features. This paper introduces the algorithms, implementation techniques and applications of JXPAMG. Numerical experiments for typical real applications are given to illustrate the strong and weak parallel scaling properties of JXPAMG.

Synthetic fibre reinforced polymer (FRP) composite materials have been widely used in engineering fields, e.g., civil, automotive, and aerospace industry, due to their high specific modulus and strength, corrosion resistance, and relatively high durability. The interface between fibre and polymer matrix is critical for the short-term and long-term performance of the FRP composite materials due to the shear lag stress transfer from the matrix to the fibre via their interface. This paper presents an overview of the fibre–matrix interface and interfacial properties. First, the interface mechanisms (i.e., interdiffusion, chemical bonding and mechanical interlocking) of FRP composites are discussed. Next, the methodology for measuring interfacial properties, characterizing interface morphology and chemical composition, and numerical simulations on FRP interface are introduced. Lastly, the challenges for the characterization of interfacial properties are highlighted.

Based on the existing situation that natural aggregates are over-exploited and man-made aggregates have become a growing trend, this paper uses economical and abundant agricultural by-product, moso bamboo, as coarse aggregates to partially replace natural coarse aggregate (NCA) for casting a novel bamboo aggregate concrete (BAC), to minimize the use of NCA and to explore feasible sustainable construction materials. Monotonic axial compression tests were conducted on BAC to investigate the effects of bamboo coarse aggregate (BCA) replacement rate (r=0%∼45%) and modification methods (unmodified and epoxy mortar-modified) on failure modes, key mechanical property indexes, and compressive stress-strain relationship. Fourier transform infrared spectroscopy, X-ray diffraction and scanning electron microscopy were utilized to analyze the chemical composition and microstructure of BAC. Results showed that when r≥15%, the strength of epoxy mortar-modified BAC can meet the strength requirements of most non-special structural components in general construction while exhibiting good ductility, so it is recommended to use r≥15% epoxy mortar-modified BCA. Finally, the conversion equations between the main mechanical property indexes and the stress-strain constitutive equations of BAC were proposed. The proposal of BAC is in line with the industry demand of vigorously promoting green building and sustainable building materials, and the study of its compression properties lays a theoretical foundation for its future engineering applications and structural design.

In exploratory data analysis, analysts often have a need to identify histograms that possess a specific distribution, among a large class of candidate histograms, e.g., find countries whose income distribution is most similar to that of Greece. This distribution could be a new one that the user is curious about, or a known distribution from an existing histogram visualization. At present, this process of identification is brute-force, requiring the manual generation and evaluation of a large number of histograms. We present FastMatch: an end-to-end approach for interactively retrieving the histogram visualizations most similar to a user-specified target, from a large collection of histograms. The primary technical contribution underlying FastMatch is a probabilistic algorithm, HistSim, a theoretically sound sampling-based approach to identify the top-kclosest histograms under 1distance. While HistSim can be used independently, within FastMatch we couple HistSim with a novel system architecture that is aware of practical considerations, employing asynchronous block-based sampling policies. FastMatch obtains near-perfect accuracy with up to 35× speedup over approaches that do not use sampling on several real-world datasets.

Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. We introduce OrpheusDB, a dataset version control system that "bolts on" versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database "for free". We develop and evaluate multiple data models for representing versioned data, as well as a light-weight partitioning scheme, LyreSplit, to further optimize the models for reduced query latencies. With LyreSplit, OrpheusDB is on average 103× faster in finding effective (and better) partitionings than competing approaches, while also reducing the latency of version retrieval by up to 20× relative to schemes without partitioning. LyreSplitcan be applied in an online fashion as new versions are added, alongside an intelligent migration scheme that reduces migration time by 10× on average.

Better tools are needed to enable researchers to quickly identify and explore effective and interpretable feature-based explanations for discriminating multi-class genomic datasets, e.g., healthy versus diseased samples. We develop an interactive exploration tool, GENVISAGE, which rapidly discovers the most discriminative feature pairs that separate two classes of genomic objects and then displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially for large numbers of objects and features, we propose a suite of optimizations to make GENVISAGE responsive at scale and demonstrate that our optimizations lead to a 400× speedup over competitive baselines for multiple biological datasets. We apply our rapid and interpretable tool to identify literature-supported pairs of genes whose transcriptomic responses significantly discriminate several chemotherapy drug treatments. With its generalizable optimizations and framework, GENVISAGE opens up real-time feature-based explanation generation to data from massive sequencing efforts, as well as many other scientific domains.