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1. A comparison of statistical relational learning and graph neural networks for aggregate graph queries

   https://doi.org/10.1007/s10994-021-06007-5  

   Embar, Varun ; Srinivasan, Sriram ; Getoor, Lise ( June 2021 , Machine Learning)

   Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complexaggregate graph queries(AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation,more »we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

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2. Assessing Resource Provisioning and Allocation of Ensembles of In Situ Workflows

   https://doi.org/10.1145/3458744.3474051  

   Do, Tu Mai ; Pottier, Loïc ; Ferreira da Silva, Rafael ; Caíno-Lores, Silvina ; Taufer, Michela ; Deelman, Ewa ( July 2021 , Fourteen International Workshop on Parallel Programming Models and Systems Software for High-End Computing (P2S2))

   Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a single long-running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, each independent simulation is usually coupled with several analyses that apply identical or distinct algorithms on data produced by the corresponding simulation. Today, In situ methods are used to analyze large volumes of data generated by scientific simulations at runtime. This work studies the execution of ensemble-based simulations paired with In situ analyses using in-memory staging methods. Because simulations and analyses forming an ensemble typically run concurrently, deploying an ensemble requires efficient co-location-aware strategies, making sure the data flow between simulations and analyses that form an In situ workflow is efficient. Using an ensemble of molecular dynamics In situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize the complex behaviors of ensembles. Thus, we propose a method to evaluate the performance of ensembles of workflows that captures resource usage (efficiency), resource allocation, and component placement. Experimental results demonstrate that our proposed method can effectively capture themore »performance of different component placements in an ensemble. By evaluating different co-location scenarios, our performance indicator demonstrates improvements of up to four orders of magnitude when co-locating simulation and coupled analyses within a single computational host.« less
3. Performance assessment of ensembles of in situ workflows under resource constraints

   https://doi.org/10.1002/cpe.7111  

   Do, Tu Mai ; Pottier, Loïc ; Ferreira da Silva, Rafael ; Caíno‐Lores, Silvina ; Taufer, Michela ; Deelman, Ewa ( April 2022 , Concurrency and Computation: Practice and Experience)

   Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a long-running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, simulations are usually coupled with analyses of data produced by the simulations. In situ methods can be used to analyze large volumes of data generated by scientific simulations at runtime (i.e., simulations and analyses are performed concurrently). In this work, we study the execution of ensemble-based simulations paired with in situ analyses using in-memory staging methods. Using an ensemble of molecular dynamics in situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize complex behaviors of ensembles. We propose a method to evaluate the performance of ensembles of workflows that captures multiple resource usage aspects: resource efficiency, resource allocation, and resource provisioning. Experimental results demonstrate that the proposed method can effectively distinguish the performance of different component placements in an ensemble with up to 32 ensemble members. By evaluating different co-location scenarios, our proposed performance indicators demonstrate benefits of co-locating simulation and coupled analyses within a compute node.
4. Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

   https://doi.org/10.7717/peerj.9777  

   Carlos-Júnior, Lélis A. ; Creed, Joel C. ; Marrs, Rob ; Lewis, Rob J. ; Moulton, Timothy P. ; Feijó-Lima, Rafael ; Spencer, Matthew ( January 2020 , PeerJ)

   Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β -diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performancemore »of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.« less
5. A General Novel Parallel Framework for SPH-centric Algorithms

   https://doi.org/10.1145/3321360  

   Huang, Kemeng ; Ruan, Jiming ; Zhao, Zipeng ; Li, Chen ; Wang, Changbo ; Qin, Hong ( June 2019 , Proceedings of the ACM on Computer Graphics and Interactive Techniques)

   To date, large-scale fluid simulation with more details employing the Smooth Particle Hydrodynamics (SPH) method or its variants is ubiquitous in computer graphics and digital entertainment applications. Higher accuracy and faster speed are two key criteria evaluating possible improvement of the underlying algorithms within any available framework. Such requirements give rise to high-fidelity simulation with more particles and higher particle density that will unavoidably increase computational cost significantly. In this paper, we develop a new general GPGPU acceleration framework for SPH-centric simulations founded upon a novel neighbor traversal algorithm. Our novel parallel framework integrates several advanced characteristics of GPGPU architecture (e.g., shared memory and register memory). Additionally, we have designed a reasonable task assignment strategy, which makes sure that all the threads from the same CTA belong to the same cell of the grid. With this organization, big bunches of continuous neighboring data can be loaded to the shared memory of a CTA and used by all its threads. Our method has thus low global-memory bandwidth consumption. We have integrated our method into both WCSPH and PCISPH, that are two improved variants in recent years, and demonstrated its performance with several scenarios involving multiple-fluid interaction, dam break, and elastic solid.more »Through comprehensive tests validated in practice, our work can exhibit up to 2.18x speedup when compared with other state-of-the-art parallel frameworks.« less