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To meet the exascale I/O requirements for the High-Performance Computing (HPC), a new I/O subsystem, Burst Buffer, based on solid state drives (SSD), has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation that requires costly garbage collection (GC) and increases the number of bytes written to the SSD. To address this data fragmentation challenge, a new multi-stream feature has been developed for SSDs. In this work, we develop an I/O Separation scheme called BIOS to leverage this multi-stream feature to group the I/O streams based on the user IDs. We propose a stream-aware scheduling policy based on burst buffer pools in the workload manager, and integrate the BIOS with the workload manager to optimize the I/O separation scheme in burst buffer. We evaluate the proposed framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. Experimental results show that the BIOS could improve the performance by 1.44x on average and reduce the Write Amplification Factor (WAF) by up to 1.20x. These demonstrate the potential benefits of the I/O separation scheme for solid state storage systems.

The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130~Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170~GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors., Accepted at the IEEE BigData 2020 conference

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

Botnet detection is an active research topic as botnets are a source of many malicious activities, including distributed denial-of-service (DDoS), click-fraud, spamming, and crypto-mining attacks. However, it is getting more complicated to identify botnets due to the continuous evolution of botnet software and families that harness new types of devices and attack vectors. Recent studies employing machine learning (ML) showed improved performance to detect botnets to some extent, but they are still limited and ineffective with the lack of sequential pattern analysis, which is a key to detect various classes of botnets. In this paper, we propose a novel botnet detection method, built upon Recurrent Variational Autoencoder (RVAE), that effectively captures sequential characteristics of botnet anomalies. We validate the feasibility of the proposed method with the CTU-13 dataset that have been widely employed for botnet detection studies, and show that our method is at least comparable to existing techniques in terms of detection accuracy. In addition, our experimental results show that the proposed method can detect previously unseen botnets by utilizing sequential patterns of network traffic. We will also show how our method can detect botnets in the streaming mode, which is the essential requirement to perform real-time, on-line detection.

A fluorescence-quenching immunochromotographic assay (ICA) was developed for sensitive detection of aflatoxin B1 (AFB1) in soybean sauce based on the inner filter effect (IFE) between flower-like gold nanoparticles (AuNFs) and quantum dots (QDs). QDs were sprayed on the test and control line zones as background fluorescence signals, whereas AuNFs were designed as the fluorescence absorber of QDs because the surface plasma resonance peak of AuNFs totally matched with the maximum emission peak of QDs. Under the optimal conditions, the fluorescence-quenching ICA strip showed a good linear detection for AFB1 in standard AFB1 solution from 0.008 μg/L to 1 μg/L with a low detection limit of 0.004 μg/L. The average recoveries for different concentrations of AFB1-spiked soybean sauce samples ranged from 84.69% to 120.44% with a coefficient of variation ranging from 2.73% to 10.41%. In addition, the reliability of the proposed method was further confirmed by ultra-performance liquid chromatography with fluorescence detection method. In brief, this novel IFE-based strip offers a simple, rapid, sensitive, and accurate strategy for quantitative detection of AFB1 in soybean sauce.

Matrix factorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegative matrix factorization and its incremental variants have attracted much attention. The existing incremental algorithms are established based on the assumption of samples are independent and only update the new latent variable of weighting coefficient matrix when the new sample comes, which may lead to inferior solutions. To address this issue, we investigate a novel incremental nonnegative matrix factorization algorithm based on correlation and graph regularizer (ICGNMF). The correlation is mainly used for finding out those correlated rows to be updated, that is, we assume that samples are dependent on each other. We derive the updating rules for ICGNMF by considering the correlation. We also present tests on widely used image datasets, and show ICGNMF reduces the error by comparing other methods.

Herein, for the first time we report a novel competitive fluorescence immunoassay for the ultrasensitive detection of aflatoxin B1 (AFB1) using histone-ds-poly(AT) templated copper nanoparticles (His-pAT CuNPs) as the fluorescent indicator. In this immunoassay, glucose oxidase (Gox) was used as the carrier of the competing antigen to catalyze the formation of hydrogen peroxide (H2O2) from glucose. H2O2 was converted to a hydroxyl radical using Fenton's reagent, which further regulated the fluorescence signals of His-pAT CuNPs. Owing to the ultrahigh sensitivity of the ss-DNA to the hydroxyl radical, the proposed fluorescence immunoassay exhibited a favorable dynamic linear detection of AFB1 ranging from 0.46 pg mL-1 to 400 pg mL-1 with an good half maximal inhibitory concentration and limit of detection of 6.13 and 0.15 pg mL-1, respectively. The intra- and inter-assay showed that the average recoveries for AFB1 spiked corn samples ranged from 96.87% to 100.73% and 96.67% to 114.92%, respectively. The reliability of this method was further confirmed by adopting ultra-performance liquid chromatography coupled with the fluorescence detector method. In summary, this work offers a novel screening strategy with high sensitivity and robustness for the quantitative detection of mycotoxins or other pollutants for food safety and clinical diagnosis.

Daily transportation mode choice is largely habitual, but transitions between life events may disrupt travel habits and can shift choices between alternative transportation modes. Although much is known about general mode switches following life event transitions, less is understood about differences that may exist between subpopulations, especially from a long-term perspective. Understanding these differences will help planners and policymakers introduce more targeted policy interventions to promote sustainable transportation modes and inform longer-term predictions. Extending beyond existing literature, we use data collected from a retrospective survey to investigate the effects of life course events on mode use situated within different long-term life trajectory contexts. We apply a machine-learning method called joint social sequence clustering to define five distinct and interpretable cohorts based on trajectory patterns in family and career domains over their life courses. We use these patterns as an innovative contextual system to investigate (1) the heterogeneous effects of life events on travel mode use and (2) further differentiation between gender and generation groups in these life event effects. We find that events occurring relatively early in life are more strongly associated with changes in mode-use behavior, and that mode use can also be affected by the relative order of events. This timing and order effect can have lasting impacts on mode use aggregated over entire life cycles: members of our “Have-it-alls” cohort-who finish their education, start working, partner up, and have children early in life-ramp up car use at each event, resulting in the highest rate of car use occurring the earliest among all the cohorts. Women drive more when having children primarily when their family formation and career formation are intertwined early in life, and younger generations rely relatively more on car use during familial events when their careers have a later start.

Wi-Fi has become the wireless networking standard that allows short- to medium-range device to connect without wires. For the last 20 year, the Wi-Fi technology has so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this paper is to evaluate a federated learning approach that would alleviate such privacy concerns. This initial work on intrusion detection is performed in a simulated environment. Once proven feasible, this process would allow edge devices to collaboratively update global anomaly detection models, without sharing sensitive training data. On a set of tests with the AWID intrusion detection data set, we show that our federated approach is effective in terms of classification accuracy, computation cost, as well as communication cost.

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

Scientific workflows are increasingly transferring large amounts of data between high performance computing (HPC) systems. Even though these HPC systems are connected via high-speed dedicated networks and use dedicated data transfer nodes (DTNs), it is still difficult to predict the data transfer throughput because of variations in data transfer protocols, host configurations, performance of file systems, and overlapping workloads. In order to provide reliable performance prediction for better resource management and job scheduling, we need models for predicting data transfer throughput under real-world conditions. In this paper, we explore different machine learning approaches for building data-driven models to improve performance and prediction of large-scale data transfer throughput. In addition to the variables already collected by the network monitoring system, we also develop heuristics to derive additional metrics for improving the prediction accuracy. We use the prediction results to identify the importance of different network parameters in predicting the throughput for large-scale data transfers. Through extensive tests, we identify key network parameters, discover interesting variations among different HPC sites, and show that we can predict throughput with high accuracy. We also analyze our models and results to provide recommendations for improving the performance of big data transfers.

The demands of increasingly large scientific application workflows lead to the need for more powerful supercomputers. As the scale of supercomputing systems have grown, the prediction of fault tolerance has become an increasingly critical area of study, since the prediction of system failures can improve performance by saving checkpoints in advance. We propose a real-time failure detection algorithm that adopts an event-based prediction model. The prediction model is a convolutional neural network that utilizes both traditional event attributes and additional spatio-temporal features. We present a case study using our proposed method with six years of reliability, availability, and serviceability event logs recorded by Mira, a Blue Gene/Q supercomputer at Argonne National Laboratory. In the case study, we have shown that our failure prediction model is not limited to predict the occurrence of failures in general. It is capable of accurately detecting specific types of critical failures such as coolant and power problems within reasonable lead time ranges. Our case study shows that the proposed method can achieve a F 1 score of 0.56 for general failures, 0.97 for coolant failures, and 0.86 for power failures.

Prediction of daily transportation mode use (car, public transit, or active travel) is a important task in transportation research. Unlike statistical models that impose a predetermined model structure, machine learning models are learned from the data, making them more flexible with higher prediction accuracy. However, prediction of mid-to long-term habitual modes still largely relies on traditional statistical analysis using small samples of cross-sectional data. Low interpretability of “black-box” machine learning models limits their usefulness for generating behavior insights needed for designing appropriate interventions. This paper, leveraging a set of unique longitudinal life course data, is the first use case to demonstrate machine learning methods applied for both predicting and interpreting regularly used travel modes. We combine sequence clustering and tree-based machine learning methods coupled with TreeExplainer to predict and interpret habitual travel modes using mid-to long-term predictors. Five life course clusters are derived to provide evaluation and interpretation contexts. This allows us to improve upon a recently developed TreeExplainer method to better distinguish predictor importance locally and globally; and predictor interactions across subpopulations within distinctive life history contexts. Our results demonstrate a promising step toward interpretable machine learning applications to mid-to long-term prediction of travel modes for transportation planning.

Author(s): Yildiz, B; Wu, K; Byna, S; Shoshani, A | Abstract: Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating-point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word-Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

A prime question for plasma physicists is how a fraction of charged particles is accelerated to very high energy.To answer this question, physicists simulate trillions of particles with detailed dynamics and analyze their trajectories. This process requires a range of data analysis tasks with high diversity. In this paper, we present a use case of formulating various analysis tasks on terabyte-scale particle data with a novel data analysis framework called ArrayUDF. The flexibility of ArrayUDF allows it to compose a wide range of particle data operations. We also present optimization strategies to avoid frequent global reduction and to take full advantage of the data locality. Tests show that our optimization methods could accelerate these particle data analysis operations by up to 1,600 times.

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

A blockchain is designed to make consistent and reliable agreement in an unreliable and decentralized environment. It also permits processing transactions, making smart contracts, which allows end users to perform the contracts without any intermediate entities. However, there are some challenges in retrieving the state in a smart contract on the blockchain. For example, an external database or user-defined data structures can be used to retrieve the data from a smart contract in a range, which can increase the management overhead and decrease the overall performance of the blockchain system. In this paper, we propose a scheme that enables SQL query operations in a blockchain system. In our proposed scheme, the register and query managers provide fast retrieval of range data without any user-defined data structure, and management at low cost without any external database, respectively. We have implemented our scheme on quorum which is an Ethereum-based blockchain system and evaluated it using a synthetic benchmark. The experimental results show that our system can improve the search performance up to about 22x compared with the existing system with low memory usage.

In this paper, a highly sensitive plasmonic enzyme-linked immunosorbent assay (pELISA) was developed for the naked-eye detection of fumonisin B1 (FB1). Glucose oxidase (GOx) was used as an alternative to horseradish peroxidase as the carrier of the competing antigen. GOx catalyzed the oxidation of glucose to produce hydrogen peroxide, which acted as a reducing agent to reduce Au3+ to Au on the surface of gold seeds (5 nm), This reaction led to a color change in the solution from colorless to purple, which was observable to the naked eye. Various parameters that could influence the detection performance of pELISA were investigated. The developed method exhibited a considerably high sensitivity for FB1 qualitative naked-eye detection, with a visible cut-off limit of 1.25 ng/mL. Moreover, the proposed pELISA showed a good linear range of 0.31&ndash, 10 ng/mL with a half maximal inhibitory concentration (IC50) of 1.86 ng/mL, which was approximately 13-fold lower than that of a horseradish peroxidase- (HRP)-based conventional ELISA. Meanwhile, the proposed method was highly specific and accurate. In summary, the new pELISA exhibited acceptable accuracy and precision for sensitive naked-eye detection of FB1 in maize samples and can be applied for the detection of other chemical contaminants.

Self-assembled iron oxide nanocomposites are good magnetic nano-adsorbents that can be prepared using simple methods. Four types of organic acid-functionalised (oleic acid, undecenoic acid, caprylic acid or hexanoic acid) magnetic nanoparticles (MNPs) were synthesised through a one-pot chemisorption method for the removal of tetracycline (TC) from aqueous solution. The undecenoic acid-coated MNPs (UA-MNPs) exhibited the highest adsorption efficiency and can be easily retrieved with a low-gradient magnetic separator (0.4 Tesla) at pH 5.0 aqueous solution. The TC adsorption process on the UA-MNPs followed the Langmuir isotherm and the maximum adsorption capacities increased from 86.96 mg g(-1) to 222.2 mg g(-1) with the increase in temperature from 288 K to 318 K. The kinetics of adsorption fits pseudo-second-order model perfectly with a rate constant, 5.946 g mg(-1) min(-1) at 298 K. The positive values of the enthalpy (AH) and the negative value of the free energy (AG) indicated an endothermic and spontaneous adsorption process of TC on the UA-MNPs. Moreover, the UA-MNPs possessed excellent ability to adsorb the other three major types of TC antibiotics, including chlortetracycline, oxytetracycline and doxycycline.

In high-performance computing, storage is a shared resource and used by all users with many different application requirements and knowledge of storage. Consequently, the optimal storage configuration varies according to the I/O behavior of each application. While system logs are helpful resources in understanding the storage behavior, it is non-trivial for each user to analyze the logs and adjust complex configurations. Even for experienced users, it is difficult to understand the full stack of I/O systems and find the optimal configuration for the specific application. In this work, we analyzed the I/O activities of CORI which is an HPC system in National Energy Research Scientific Computing Center (NERSC). The result of our analysis shows that most users do not adjust storage configurations and use the default settings. Also, it shows that only a few applications are executed repeatedly in the HPC environment. Based on this result, we have developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm, which utilizes system log information and widely adapted rules to adjust storage configurations automatically without any user intervention. DCA-IO utilizes existing system logs and does not require any modifications in code or an additional library. To demonstrate the effectiveness of DCA-IO, we have performed experiments using I/O kernels of the real applications in both isolated small-sized Lustre environment and CORI. Our experimental result shows that the use of our scheme can lead to improvements in the performance of HPC applications by up to 75% in an isolated environment and 50% in a real HPC environment without user intervention.

© 2000-2011 IEEE. Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions.

Accurately predicting network traffic volume is beneficial for congestion control, improving routing, allocating network resources and network optimization. Traffic congestion happens when a network device is receiving more data packets than its processing capability. The number of retransmissions per flow, packet duplication and synthetic reordering can seriously degrade the overall TCP performance. An unsupervised/supervised technique to accurately identify TCP anomalies occurring during file transfers based on passive measurements of TCP traffic collected using Tstat is proposed. This method will be validated on real large datasets collected from several data transfer nodes. The preliminary results indicate that the percentage of TCP anomalies correlate well with the average throughput in any given time window.

Pub/sub systems form the underlying framework for many distributed applications including large social networking applications. In this paper, we consider the optimization of the end-to-end latency of a pub/sub system in which the publisher, the broker, and the subscriber are in different administrative domains. While general pub/sub systems provide reliability of message delivery, good end-to-end latency in a multi-domain environment requires that the pub/sub system adapts to workload changes and bottlenecks in the different sub-systems. This study is motivated by two applications. First, a pub/sub based Simple Lookup Service (sLS) that is used in perfSONAR to provide information about network performance in Research and Education (R&E) networks. Second, the pub/sub system that is used to distribute alerts generated in the data pipeline in the Zwicky Transient Factory (ZTF). In this multi-domain pub/sub performance study, we consider a publisher with a multi-threaded architecture that uses batching to coalesce messages over some variable polling period. We propose a control algorithm that auto-tunes the batch-processing parameters namely, the batch size and the polling interval, to the input message load and to broker-side congestion. Using a detailed simulation model, we demonstrate the performance of the control algorithm for different scenarios. We then study the performance using a real-trace obtained from the Simple Lookup Service (sLS). We show that the proposed algorithm quickly adapts to rapid changes in the workload and yields lower mean end-to-end delay performance when compared with delays in the current deployment.

As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority - by as much as two orders of magnitude - of the proposed framework over existing techniques in terms of cache overhead and workload execution time.

Scientific computing systems are becoming increasingly complex and indeed are close to reaching a critical limit in manageability when using current human-in-the-loop techniques. In order to address this problem, autonomic, goal-driven management actions based on machine learning must be applied end to end across the scientific computing landscape. Even though researchers proposed architectures and design choices for autonomic computing systems more than a decade ago, practical realization of such systems has been limited, especially in scientific computing environments. Growing interest and recent developments in machine learning have spurred proposals to apply machine learning for goal-based optimization of computing systems in an autonomous fashion. We review recent work that uses machine learning algorithms to improve computer system performance, identify gaps and open issues. We propose a hierarchical architecture that builds on the earlier proposals for autonomic computing systems to realize an autonomous science infrastructure.

Scientists are increasingly turning to datacenter-scale computers to analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC programs even for the most mundane queries. This impedance mismatch is due to the cumbersome and costly data format conversions that are needed to use scientific data management tools, such as SciDB, in an HPC setting. Our goal is to make declarative array manipulations from SciDB interoperable with imperative, file-centric analyses from HDF5-based programs. This paper describes ArrayBridge, a bi-directional array view mechanism for the HDF5 file format, that allows scientists to use SciDB, TensorFlow and HDF5-based analysis code in the same file-centric pipeline without converting between file formats. In addition to fast querying over HDF5 array objects, ArrayBridge produces arrays in the HDF5 file format as easily as it can read from it. ArrayBridge also supports time travel queries from imperative codes through the unmodified HDF5 API, and automatically deduplicates between versions for space efficiency. Our performance evaluation in a large scientific computing facility shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine and is 3× faster than TileDB.

Our society depends heavily on the electric power infrastructure. To ensure its reliability, key power grid components such as transformers are extensively monitored for signs of failures and errors. This work concentrates on a type of event known as the partial discharge (PD) because it is the symptom of insulation weakness, the most common cause of transformer failures. More specifically, our work is to locate the position of a partial discharge to provide information for preventive maintenance. Our method utilizes the information from a set of ultra-high frequency (UHF) sensors inside the transformer, and proceeds in two steps: first determine the signal arrival time and then locate the position based on time differences. To determine the arrival time, we develop a convolutional filtering method based on the Savitzky-Golay filter. To provide accurate locations, we simulate the electromagnetic wave propagation using finite-difference time-domain (FDTD) to generate a reference table of the travel time from each FDTD mesh point to the sensors. We exercise our method using two sets of UHF measurements with different signal to noise ratios. In both cases, our method provides more accurate locations than other methods. The difference is particularly prominent when the signal is weak. With weak signals, the best existing method, the cumulative energy method, was only able to predict the PD location within 300 mm of the known sources in 13% of the test cases, while our method is correct in 48% of the test cases.

Science data gateways are effective in providing complex science data collections to the world-wide user communities. In this paper we describe a gateway for the Advanced Simulation Capability for Environmental Management ASCEM framework. Built on top of established web service technologies, the ASCEM data gateway is specifically designed for environmental modeling applications. Its key distinguishing features include 1 handling of complex spatiotemporal data, 2 offering a variety of selective data access mechanisms, 3 providing state-of-the-art plotting and visualization of spatiotemporal data records, and 4 integrating seamlessly with a distributed workflow system using a RESTful interface. ASCEM project scientists have been using this data gateway since 2011. Copyright © 2015 John Wiley & Sons, Ltd.

© 2018 Inderscience Enterprises Ltd. Palomar Transient Factory (PTF) is a comprehensive detection system for the identification and classification of transient astrophysical objects. In this paper, we make two significant contributions to the PTF pipeline. First, we present an experimental study that evaluates a novel implementation of the real-time classifier in GLADE - a parallel data processing system that combines the efficiency of a database with the extensibility of map-reduce. We show how each stage in the classifier maps optimally into GLADE tasks by taking advantage of the unique features of the system - range-based data partitioning, columnar storage, multi-query execution, and in-database support for complex aggregate computation. Second, we introduce a novel parallel similarity join algorithm for advanced transient classification. We implement this algorithm in GLADE and execute it on a massive supercomputer with more than 3,000 threads, achieving more than three orders of magnitude improvement over the PostgreSQL solution.

When working with a large dataset, a relatively small fraction of data records are of interest in each analysis operation. For example, while examining a billion-particle dataset from an accelerator model, the scientists might focus on a few thousand fastest particles, or on the particle farthest from the beam center. In general, this type of selective data access is challenging because the selected data records could be anywhere in the dataset and require a significant amount of time to locate and retrieve. In this paper, we report our experience of addressing this data access challenge with the Adaptable IO System ADIOS. More specifically, we design a query interface for ADIOS to allow arbitrary combinations of range conditions on known variables, implement a number of different mechanisms for resolving these selection conditions, and devise strategies to reduce the time needed to retrieve the scattered data records. In many cases, the query mechanism can retrieve the selected data records orders of magnitude faster than the brute-force approach.

We propose a new class of lossy compression based on locally exchangeable measure that captures the distribution of repeating data blocks while preserving unique patterns. The technique has been demonstrated to reduce data volume by more than 100-fold on power grid monitoring data where a large number of data blocks can be characterized as following stationary probability distributions. To capture data with more diverse patterns, we propose two techniques to transform non-stationary time series into locally stationary blocks. We also propose a strategy to work with values in bounded ranges such as phase angles of alternating current. These new ideas are incorporated into a software package named IDEALEM. In experiments, IDEALEM reduces non-stationary data volume up to 100-fold. Compared with the state-of-the-art lossy compression methods such as SZ, IDEALEM can produce more compact output overall.

User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management tasks to the system. This general approach enables numerous custom analysis functions and is at the heart of the modern Big Data systems. Even though the UDF mechanism can theoretically support arbitrary operations, a wide variety of common operations -- such as computing the moving average of a time series, the vorticity of a fluid flow, etc., -- are hard to express and slow to execute. Since these operations are traditionally performed on multi-dimensional arrays, we propose to extend the expressiveness of structural locality for supporting UDF operations on arrays. We further propose an in situ UDF mechanism, called ArrayUDF, to implement the structural locality. ArrayUDF allows users to define computations on adjacent array cells without the use of join operations and executes the UDF directly on arrays stored in data files without requiring to load their content into a data management system. Additionally, we present a thorough theoretical analysis of the data access cost to exploit the structural locality, which enables ArrayUDF to automatically select the best array partitioning strategy for a given UDF operation. In a series of performance evaluations on large scientific datasets, we have observed that -- using the generic UDF interface -- ArrayUDF consistently outperforms Spark, SciDB, and RasDaMan.

Science applications are producing an ever-increasing volume of multi-dimensional data that are mainly processed with distributed array databases. These raw arrays are ``cooked'' into derived data products using complex pipelines that are time-consuming. As a result, derived data products are released infrequently and become stale soon thereafter. In this paper, we introduce materialized array views as a database construct for scientific data products. We model the ``cooking'' process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance without overhead. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production environment.

Scientific discoveries are increasingly relying on analysis of massive amounts of data. The ability to directly access the most relevant data records through query, without shifting through all of them becomes essential. However, scientific datasets are commonly stored on parallel file systems and I/O systems that are optimized for reading/writing large chunks of data, and many scientific datasets have spatial-temporal data similarity, such that the records with similar values often locate in a close proximity of each other. Therefore, our previous work started to investigate the benefit of using block range index technique for scientific datasets, which only records the value range of all the records in a data block. In this paper, we extend our work in several aspects. First, we implement and integrate our block index technique with the ADIOS I/O system. Second, we show our proposed method can be significantly better than the existing minmax and bitmaps indexing methods supported in ADIOS, and can also have comparable performance in the worst case. Third, we propose several techniques that can take advantage of the block index information to greatly reduce data retrieval time from query results. Fourth, we evaluate our approach using several real scientific datasets, and analyze the spatial-temporal data similarity characteristics in them. Through our study, we believe block index can be an effective indexing technique for scientific datasets with little implementation and operating overhead. It's size is small enough for building the indexes on-the-fly, and yet its query information is sufficient for efficient data access.

© 2017 IEEE. Large data analysis problems often involve a large number of variables, and the corresponding analysis algorithms may examine all variable combinations to find the optimal solution. For example, to model the time required to complete a scientific workflow, we need to consider the impact of dozens of parameters. To reduce the model building time and reduce the likelihood of overfitting, we look to variable selection methods to identify the critical variables for the performance model. In this work, we create a combination of variable selection and performance prediction methods that is as effective as the exhaustive search approach when the exhaustive search could be completed in a reasonable amount of time. To handle the cases where the exhaustive search is too time consuming, we develop the parallelized variable selection algorithm. Additionally, we develop a parallel grouping mechanism that further reduces the variable selection time by 70%.As a case study, we exercise the variable selection technique with the performance measurement data from the Palomar Transient Factory (PTF) workflow. The application scientists have determined that about 50 variables and parameters are important to the performance of the workflows. Our tests show that the Sequential Backward Selection algorithm is able to approximate the optimal subset relatively quickly. By reducing the number of variables used to build the model from 50 to 4, we are able to maintain the prediction quality while reducing the model building time by a factor of 6. Using the parallelization and grouping techniques we developed in this work, the variable selection process was reduced from over 18 hours to 15 minutes while ending up with the same variable subset.

We propose a new class of lossy compression based on locally exchangeable measure that captures the distribution of repeating data blocks while preserving unique patterns. The technique has been demonstrated to reduce data volume by more than 100-fold on power grid monitoring data where a large number of data blocks can be characterized as following stationary probability distributions. To capture data with more diverse patterns, we propose two techniques to transform non-stationary time series into locally stationary blocks. We also propose a strategy to work with values in bounded ranges such as phase angles of alternating current. These new ideas are incorporated into a software package named IDEALEM. In experiments, IDEALEM reduces non-stationary data volume up to 100-fold. Compared with the state-of-The-Art lossy compression methods such as SZ, IDEALEM can produce more compact output overall.

Hierarchical storage subsystems that include multiple layers of burst buffers (BB) and disk-based parallel file systems (PFS), are becoming an essential part of HPC systems to address the I/O performance gap. However, the state-of-the-art software for managing these hierarchical storage subsystems, such as Cray DataWarp, requires user involvement in moving data among storage layers. Such manual data movement may experience poor performance because of resource contention on the I/O servers of a layer for serving data movement in the hierarchy as well as regular read/write requests. In this paper, we propose a new system, named Data Elevator, for transparently and efficiently moving data in hierarchical storage. Users specify the final destination for their data, typically a PFS. Data Elevator intercepts the I/O calls, stages data on a fast persistent storage layer (for example, an SSD-based burst buffer), and then asynchronously transfers the data to the final destination in the background. Data Elevator reduces the resource contention on BB servers via offloading the data movement from a fixed number of BB server nodes to compute nodes. The number of the compute nodes is configurable based on the data movement load. Data Elevator also allows optimizations, such as overlapping read and write operations, choosing I/O modes, and aligning buffer boundaries. In our tests with large-scale scientific applications, Data Elevator is as much as 4.2X faster than Cray DataWarp, and 4X faster than directly writing data to PFS.

Runtime data sharing across applications is of great importance for avoiding high I/O overhead for scientific data analytics. Sharing data on a staging space running on a set of dedicated compute nodes is faster than writing data to a slow disk-based parallel file system (PFS) and then reading it back for post-processing. Originally, the staging space has been purely based on main memory (DRAM), and thus was several orders of magnitude faster than the PFS approach. However, storing all the data produced by large-scale simulations on DRAM is impractical. Moving data from memory to SSD-based burst buffers is a potential approach to address this issue. However, SSDs are about one order of magnitude slower than DRAM. To optimize data access performance over the staging space, methods such as prefetching data from SSDs according to detected spatial access patterns and distributing data across the network topology have been explored. Although these methods work well for uniform mesh data, which they were designed for, they are not well suited for adaptive mesh refinement (AMR) data. Two mąjor issues must be addressed before constructing such a memory hierarchy and topology-aware runtime AMR data sharing framework: (1) spatial access pattern detection and prefetching for AMR data; (2) AMR data distribution across the network topology at runtime. We propose a framework that addresses these challenges and demonstrate its effectiveness with extensive experiments on AMR data. Our results show the framework's spatial access pattern detection and prefetching methods demonstrate about 26% performance improvement for client analytical processes. Moreover, the framework's topology-aware data placement can improve overall data access performance by up to 18%.

© 2016 ACM. Applications such as scientific simulations and power grid monitoring are generating so much data quickly that compression is essential to reduce storage requirement or transmission capacity. To achieve better compression, one is often willing to discard some repeated information. These lossy compression methods are primarily designed to minimize the Euclidean distance between the original data and the compressed data. But this measure of distance severely limits either reconstruction quality or compression performance. We propose a new class of compression method by redefining the distance measure with a statistical concept known as exchangeability. This approach reduces the storage requirement and captures essential features, while reducing the storage requirement. In this paper, we report our design and implementation of such a compression method named IDEALEM. To demonstrate its effectiveness, we apply it on a set of power grid monitoring data, and show that it can reduce the volume of data much more than the best known compression method while maintaining the quality of the compressed data. In these tests, IDEALEM captures extraordinary events in the data, while its compression ratios can far exceed 100.

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC., 14 pages, 40 figures

Scientific applications are generating an ever-increasing volume of multi-dimensional data that are largely processed inside distributed array databases and frameworks. Similarity join is a fundamental operation across scientific workloads that requires complex processing over an unbounded number of pairs of multi-dimensional points. In this paper, we introduce a novel distributed similarity join operator for multi-dimensional arrays. Unlike immediate extensions to array join and relational similarity join, the proposed operator minimizes the overall data transfer and network congestion while providing load-balancing, without completely repartitioning and replicating the input arrays. We define formally array similarity join and present the design, optimization strategies, and evaluation of the first array similarity join operator.

Parallel sorting is an essential algorithm in large-scale data analytics using distributed memory systems. As the number of processes increases, existing parallel sorting algorithms could become inefficient because of the unbalanced workload. A common cause of load imbalance is the skewness of data, which is common in application data sets from physics, biology, earth and planetary sciences. In this work, we introduce a new scalable dynamic skew-aware parallel sorting algorithm, named SDS-Sort. It uses a skew-aware partition method to guarantee a tighter upper bound on the workload of each process. To improve load balance among parallel processes, existing algorithms usually add extra variables to the sorting key, which increase the time needed to complete the sorting operation. SDS-Sort allows a user to select any sorting key without sacrificing performance. SDS-Sort also provides optimizations, including adaptive local merging, overlapping of data exchange and data processing, and dynamic selection of data processing algorithms for different hardware configurations and for partially ordered data. SDS-Sort uses local-sampling based partitioning to further reduce its overhead. We tested SDS-Sort extensively on Edison, a Cray XC30 supercomputer. Timing measurements show that SDS-Sort can scale to 130K CPU cores and deliver a sorting throughput of 117TB/min. In tests with real application data from large science projects, SDS-Sort outperforms HykSort, a state-of-art parallel sorting algorithm, by 3.4X.

As scientific simulations and experiments move toward extremely large scales and generate massive amounts of data, the data access performance of analytic applications becomes crucial. A mismatch often happens between write and read patterns of data accesses, typically resulting in poor read performance. Data layout reorganization has been used to improve the locality of data accesses. However, current data reorganizations are static and focus on generating a single (or set of) optimized layouts that rely on prior knowledge of exact future access patterns. We propose a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern. This framework supports reading individual elements and chunks of a multi-dimensional array of variables. Our pattern-driven layout selection strategy achieves multi-fold speedups compared to reading from the original dataset.

© 2016 IEEE. Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management; (2) achieving a load-balanced AMR data distribution for the data staging space at runtime; and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone's effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores; in the best case, our framework achieves a 46% performance improvement.

Scientific discoveries are increasingly relying on analysis of massive amounts of data generated from scientific experiments, observations, and simulations. The ability to directly access the most relevant data records, without shifting through all of them becomes essential. While many indexing techniques have been developed to quickly locate the selected data records, the time and space required for building and storing these indexes are often too expensive to meet the demands of in situ or real-time data analysis. Existing indexing methods generally capture information about each individual data record, however, when reading a data record, the I/O system typically has to access a block or a page of data. In this work, we postulate that indexing blocks instead of individual data records could significantly reduce index size and index building time without increasing the I/O time for accessing the selected data records. Our experiments using multiple real datasets on a supercomputer show that block index can reduce query time by a factor of 2 to 50 over other existing methods, including SciDB and FastQuery. But the size of block index is almost negligible comparing to the data size, and the time of building index can reach the peak I/O speed.

Query-driven analytics on scientific datasets is one of fundamental approaches for scientific discoveries. Existing studies have explored query-driven analytics on uniform resolution meshes. However, querying on adaptive mesh refinement (AMR) data has not been explored yet. As many simulations have been lately transitioning to AMR, new methods for efficient query-driven analysis on AMR data are needed. In this paper, we present the first work to support scalable AMR-aware analysis. We propose an AMR-aware hybrid index for supporting two common forms (i.e., spatial and value-based query selections) in query-driven analytics. To sustainably support future-scale analysis, we design an in situ (run-time) index building strategy with minimized performance impact to the co-located simulation. Additionally, we develop a parallel post-processing query method with an adaptive workload-balanced strategy. Our evaluation demonstrates the scalability of our in situ indexing and scalable querying methods up to 16,384 and 1,024 cores, respectively, using a Chombo-based benchmark. Compared to non-AMR-aware indexing and querying, we demonstrate up to 12.4x and 500x performance improvement, respectively.