Central Computer Research Articles

Facing the Challenges in simulation-based Earth System Sciences and the Role of FAIR Digital Objects

Facing the Challenges in simulation-based Earth System Sciences and the Role of FAIR Digital Objects

(Invited) Small Polarons in Metal-Oxide Based Solar Absorber Materials

The key materials for photo-electrochemical (PEC) processes are the photoelectrocatalysts that are required to absorb sunlight, form electron-hole pairs, and transport the charge carriers efficiently towards the electrolyte-semiconductor interface. Metal-oxides are thought to be the most stable materials under intense interfacial reactive conditions. However, the overall efficiencies of these metal-oxide photoelectrocatalysts are not as high as expected, even for those with near-suitable bandgap energies required for efficient solar energy absorptions. One of the main reasons is attributed to the poor transport properties in metal-oxides with strongly correlated electrons. Typically, strongly correlated systems exhibit polaron conduction via electron-phonon interactions. Polarons act like a bottleneck for photoconductions in such metal-oxides. Recently, there have been significant efforts to characterize polaronic states and their effects on transport properties in photoactive materials. First-principles theoretical and computational studies can reveal the electronic origin of the polaron formation and, therefore, can suggest how to improve overall transport properties in such photoactive materials. In this presentation, representative metal-oxides photo-catalysts will be considered to demonstrate the small polaron formation mechanism. The polaronic states in metal-oxides will be depicted from detailed electronic structure calculations by density functional theory (DFT) based methods. We will show how these polaronic states affect the PEC cell's transport properties and photo-voltages. We will also discuss how these computational results compare with the recent experimental outcome. Some part of the work was funded by National Science Foundation, USA. Computations were performed at the Texas Advanced Computing Center (TACC).

How Halide Sub-Lattice Affects Li Ion Transport in Antiperovskites

Li-based antiperovskites (LiAP, Li3-x OH x X, X = Cl, Br) are an emergent class of Li-ion conductors that are potential candidates for electrolytes in all-solid-state batteries. As a material class, pLiAP shows vast compositional design freedom; however, the resulting properties are susceptible to synthesis and processing methodologies. For example, proton incorporation and halide mixing stabilize the perovskite cubic phase near room temperature, and halides mixtures near the eutectic points drive the solid-state reaction temperature down, allowing for faster synthesis and processing conditions (< 1 h). The mixed halogen compositions, such as Li2OHCl0.37Br0.63, also show a 30-fold improvement in room temperature ionic conductivity of a single halide structure, 1.5 x 10-6 vs. 4.9 x 10-8 S cm-1 (Li2OHCl). Despite the growing interest in these materials, important questions remain about LiAPs on the structure-property correlation upon halide substitution and the correlations between the OH/halide dynamics and the Li-ion transport. We thus attempted to deconvolute how proton dynamics and halide substitution enhance or impede ionic conduction in pLiAP at compositions near the halide salts' eutectic points.We combined infrared spectroscopy and nuclear magnetic resonance (NMR) with first-principles density functional theory (DFT) calculations to deconvolute halide mixing effects from local proton dynamics on Li-ion transport. The NMR results and ab initio molecular dynamics suggest that Li+ transport is more strongly correlated with halide dynamics. While the hydroxide does stabilize the highly conductive cubic structure, it limits correlative ionic transport and thus lowers Li+ conductivity.Experiment design, data analysis, and manuscript preparation (RLS) were supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Science and Engineering. Synthesis (THB and JN) were supported by Asst. Secretary, Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (VTO) through the Advanced Battery Materials Research (BMR) Program. P. J. acknowledges partial support by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-FG02-96ER45579. H. F. was supported from U.S. Department of Energy (Award No. DE-EE0008865). This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The NMR characterization part of the work is supported by the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, and Basic Energy Sciences. The NMR work was performed at the W. R. Wiley Environmental Molecular Sciences Laboratory, a DOE User Facility sponsored by the Office of Biological and Environmental Research, located at Pacific Northwest National Laboratory. Figure 1

Structured Compression of Convolutional Neural Networks for Specialized Tasks

Convolutional neural networks (CNNs) offer significant advantages when used in various image classification tasks and computer vision applications. CNNs are increasingly deployed in environments from edge and Internet of Things (IoT) devices to high-end computational infrastructures, such as supercomputers, cloud computing, and data centers. The growing amount of data and the growth in their model size and computational complexity, however, introduce major computational challenges. Such challenges present entry barriers for IoT and edge devices as well as increase the operational expenses of large-scale computing systems. Thus, it has become essential to optimize CNN algorithms. In this paper, we introduce the S-VELCRO compression algorithm, which exploits value locality to trim filters in CNN models utilized for specialized tasks. S-VELCRO uses structured compression, which can save costs and reduce overhead compared with unstructured compression. The algorithm runs in two steps: a preprocessing step identifies the filters with a high degree of value locality, and a compression step trims the selected filters. As a result, S-VELCRO reduces the computational load of the channel activation function and avoids the convolution computation of the corresponding trimmed filters. Compared with typical CNN compression algorithms that run heavy back-propagation training computations, S-VELCRO has significantly fewer computational requirements. Our experimental analysis shows that S-VELCRO achieves a compression-saving ratio between 6% and 30%, with no degradation in accuracy for ResNet-18, MobileNet-V2, and GoogLeNet when used for specialized tasks.

Privacy and Security in Federated Learning: A Survey

In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings from our study confirm that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN)-based attacks, while inference-based attacks are the most critical to the privacy of FL. Finally, we identify ongoing research directions on the topic. This paper could be used as a reference to promote cybersecurity-related research on designing FL-based solutions for alleviating future challenges.

An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs

Set reachability query in directed graphs has a plethora of graph-based applications such as dependency analysis and graph centrality calculation. Given two sets <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> of source and target vertices, set reachability query needs to acquire all pairs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s{\in }S$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$t{\in }T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> can reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> . The state-of-the-art approach distributed set reachability (DSR) investigates the set reachability query in a distributed environment and adopts a static graph-based index to enhance the query efficiency. Nevertheless, DSR needs to store the graph-based index in all partitions, which causes a huge space overhead. Furthermore, it cannot efficiently solve the negative query <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> cannot reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , since DSR needs to traverse the whole reachable paths and becomes unable to efficiently reduce the computations. To alleviate these issues, we propose a novel multi-level 2-hop (ML2hop) index for the set reachability query in a distributed environment. Based on ML2hop, we further present a bi-directional query algorithm, called MLQA, to achieve efficient support for both positive and negative queries in Pregel-like systems. Generally, MLQA is equipped with the following three significant properties: (1) Low computation costs. It reduces redundant local computations in each partition by controlling the rounds of path traversals. (2) Low communication costs. It restricts the message exchange among different partitions within one single round with guaranteed accuracy of query results. (3) High parallelism. It adopts a bi-directional query technique for message propagation, achieving the better query efficiency than the forward-traversal query strategy utilized in DSR. Experimental results over several real-world graphs demonstrate that MLQA significantly outperforms the state-of-the-art algorithm by up to two orders of magnitude speedup.

FAILURE MODE AND EFFECTS ANALYSIS (FMEA): INSTANT ERROR AND CONDITION MONITORING IN MANUFACTURING

Bu çalışmada, Endüstriyel mutfak ürünleri imal eden ve ürün gamında yüzlerce ana ürün ve binlerce yarı mamul bulunan bir firmanın talaşlı imalat atölyesinde Hata Türü ve Etkileri Analizi (HTEA) yöntemi uygulanmıştır. Atölyede bulunan, 12 adet makinaya yerleştirilen Programlanabilir Mantık Denetleyici (Programmable Logic Controller, PLC) kartlar ile anlık makinanın durumu, ürün ve üretim izlenmiştir. Makinelere yerleştirilen PLC kartları yardımıyla veriler merkezi bilgisayara kaydedilmiş olup, makinanın çalışmasıyla ilgili sağlıklı veriler elde edilmiştir. Çalışma kapsamında, Makineye yerleştirilen kartlardan alınan veriler ile makinelerin çalışma durumları, kapasite kullanım süreleri, makinelerin çalışma süreleri, sadece yatay torna makinelerine ait parça sıkılmış durumda bekleme süreleri, boşta bekleme süreleri, çalışma süreleri ve duruş süreleri elde edilmiştir. Operatörlere bağlı kalmadan elde edilen veriler doğrultusunda işletmede HTEA yöntemi uygulanmış olup, en çok karşılaşılan hata türleri belirlenerek 100’den büyük RÖS değerine sahip olanlar için önleyici ve düzeltici faaliyetler belirlenmiş ve uygulanmıştır. Ayrıca ana çalışmaya ek olarak talaşlı imalat firmalarının katlandığı en büyük maliyet kalemi olan kesici uç takımı maliyetleri de analiz edilmiş olup, HTEA uygulamasının yapılmadığı Ocak 2021 ile uygulamanın yapıldığı Şubat 2021 ve Mart 2021 üretim maliyetleri karşılaştırılmış ve değerlendirilmiştir.

The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)

Abstract. Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction.

Interval possibilistic C-means algorithm and its application in image segmentation

Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance.

347.6: Racial-Ethnic and Sex Disparities in Access to Renal Transplant Waitlisting Vary by Age in the United States

Background: Transplantation provides numerous benefits, but racial/ethnic and gender disparities in access to renal transplantation exist globally. However, in the United States, the interaction between race/ethnicity and gender in transplant access has not been well explored. Further, it is unclear how age at diagnosis modifies these disparities. Methods: We used the United States Renal Data System to identify a retrospective cohort of incident adult dialysis patients from January 1, 2005 – December 31, 2017 and followed through 2018. We used multivariable Cox regression to model the association between combined race/ethnicity and sex (race x gender) group and time to renal transplant waitlisting. We adjusted for clinical and sociodemographic factors and included a time-dependent covariate reflecting a 2014 change in the national kidney allocation policy (KAS) aimed to improve equity. We incorporated a race x gender x age group interaction term to test for effect modification by age group. We also conducted stratified analyses per age group. Results: After excluding prior renal transplant recipients (n=5,227) and pre-emptive transplants (n=3,486), we identified 1,337,386 US incident dialysis patients of whom 19.4% were waitlisted (median time to waitlisting (interquartile range): 350 days (184, 651)). Most patients were over 50; 50-59 (n=215,028, 19.8%), 60-69 (24.8%), or 70 or greater (37.2%). Waitlisting was highest among Asian women aged 18-39 (n=6,260, 69.7%) and lowest among Hispanic women aged 70 or greater (n=2,315, 1.9%). We found significant effect modification by age in the association of demographic group and time to waitlisting (p-value: <0.0001). Across all age groups, Black women experienced reduced access to transplant waitlisting compared to age-matched white men; this disparity was worst among the oldest (aged 70 or greater) (adjusted hazard ratio (aHR): 0.60, 95% confidence interval (CI): 0.56, 0.64) and youngest (aged 18-39) Black women (aHR: 0.77, 95% CI: 0.74, 0.79). Furthermore, older Hispanic and white women (60-69 and 70 or greater) exhibited reduced transplant access compared to aged-matched white men and men within their own race/ethnic group. Hispanic women aged 70 or greater experienced worst disparity with 42% reduced likelihood of waitlisting compared to white men (aHR: 0.58, 95% CI: 0.53, 0.64). Conclusions: The interaction between race/ethnicity and gender in access to renal transplant waitlisting in the United States varies significantly by age. Older Black and Hispanic women and younger Black women have significantly reduced access to transplantation compared to other groups, even after adjusting for clinical and sociodemographic factors. More work is needed to understand and ameliorate these intersections of gender, race/ethnicity, and age-related disparities to promote equity in access to renal transplantation. This research was supported by University of Chicago Medicine’s Center for Healthcare Delivery Science and Innovation (HDSI), the Bucksbaum Institute for Clinical Excellence at the University of Chicago, and the National Kidney Foundation of Illinois. We also acknowledge the University of Chicago Research Computing Center for computational support of this work. The data reported here have been supplied by the United States Renal Data System (USRDS) and the United States Census Bureau. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the U.S. Government.

La educación de calidad en el programa educativo de derecho de la Universidad Autónoma Indígena de México ante el COVID-19

The Indigenous Autonomous University of Mexico, located in Sinaloa, receives indigenous students from all the states of the republic, it has a student dormitory and a dining room free for them. At UAIM students have access to library services and computer centers, in addition to wireless internet, so they do not have connectivity problems. However, when confinement began and they retired to their homes, the complete lack of connectivity for some was evident, because there is no internet service nearby or poor service. The objective of this article is to identify the main difficulties faced by students according to the teachers to maintain quality education in the virtual classrooms and propose possible strategies to replace content for the return. face-to-face. This essay was done through the biographical narrative methodology with a report on the experiences of 8 professors of the Law degree at the Autonomous Indigenous University of Mexico who for two years worked in the Classroom at Home with their students, their strategies and frustrations to complete course after course. Monthly reports issued by the Coordination of the school were consulted. It is concluded that a great effort was made by all the actors involved for the preparation of the students, however, the distance, the times and the deficiencies, but above all the lack of formal preparation of the teachers in the use of the educational platforms, but above all due to the extreme flexibility during the pandemic, the quality decreased a lot and did not allow more than 80% of the expected learning to be achieved among students and that as teachers they must be aware of it, but above all do it know the students.

Ultrasound-Guided Fine Needle Aspiration of Deep Thyroid Nodule: Is There a Correlation between the Nodule's Depth and Nondiagnostic Results?

Objective To evaluate whether thyroid nodule depth correlates with nondiagnostic results in ultrasound-guided fine needle aspiration cytopathology. Background Many factors correlate with nondiagnostic ultrasound-guided fine needle aspiration cytology (FNAC) results, including older age, macrocalcification, small-sized nodules, aspirin medication, and cystic portion in more than 50% of the thyroid nodules. However, there are few studies which have examined whether there is a relationship between the depth of nodules and the percentage of nondiagnostic results in cytology (Bethesda category I). We conducted this study in order to investigate if such a correlation exists. Materials and Methods FNAC was performed on 283 thyroid nodules between January 2019 and December 2020. Cytological analyses of the nodules were reviewed and sorted as nondiagnostic and diagnostic according to the Bethesda score. Patient files and ultra sound (US) scans were reviewed for clinical information (such as age, sex, and ethnic group) and sonographic features of nodules (such as depth, size, cystic portion, type of calcification, and echogenicity) and were compared between the nondiagnostic and diagnostic nodule results. The depth of a nodule was calculated as the shortest distance from the skin to the most superficial border of the nodule in the axial plane, using our medical center's computer program, which allows reviewing all saved shots of the US scan. Results Age, sex, and ethnicity were not significantly different between the nondiagnostic group and the diagnostic group (p > 0.05). Nodule diameter, cystic portion, calcification, and echogenicity were also not associated with the frequency of nondiagnostic results. The depth of nodules ≥9 mm was correlated with nondiagnostic US-guided FNA cytological results (OR = 2.55, p=0.018). Conclusions Deep thyroid nodules correlated with nondiagnostic US-guided FNA cytological results. Further studies are needed for optimizing the approach to deep thyroid nodules in order to improve the efficacy of FNA in deep thyroid nodules.

Workload management and server speed setting for cost‐performance ratio optimization

AbstractThe cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data

Abstract. The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. The DKRZ (German Climate Computing Center; German: Deutsches Klimarechenzentrum) is the only remaining DDC Partner from the original group jointly managing the DDC. In spite of changes in prioritization, it has been supporting the IPCC Assessments and preserving the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. An active and engaged collaborative community achieved advances in data standardization, data management best practices, and infrastructure developments. These evolving standards are reflected in the activities of the DDC. The introduction of the IPCC FAIR Guidelines into the current Sixth IPCC Assessment Report (AR6) has significantly changed the role of the DDC Partner DKRZ from an independent partner for long-term data preservation into an active partner involved in the IPCC's Sixth Assessment cycle. As a result, the DDC has gained exposure and visibility, posing a challenge and an opportunity to operationalize the IPCC's FAIR Guidelines and long-term preservation approaches. While the value of DDC services has been recognized, DDC sustainability remains unresolved and is currently being discussed within the IPCC as part of a general AR6 review process to formulate recommendations for the AR7 data management.

Networked Fitness Management System Based on Internet of Things

In recent years, with the rapid development of Internet of things and other technologies, the digitalization, networking, and intelligence of sports have become the current research focus. In this paper, the fitness management system based on the Internet of things is studied. By analyzing the system function and performance requirements, the design of fitness client (small tablet) of networked fitness management system is based on Internet of things. Receiving the fitness data uploaded by the fitness device through Bluetooth, the fitness data can be processed and displayed in real time with graphics. After the exercise, the fitness data can be uploaded to the central computer through Wi-Fi wireless. Taking barbell as an example, by analyzing the movement characteristics of barbell, Bluetooth MPU6050 module is used for data acquisition; the data collected includes angle, number, etc.; the relevant functions of barb-dumbbell movement are analyzed and designed; and the Bluetooth communication module and Wi-Fi communication module in the small tablet software system are designed and implemented. The relevant experiments were carried out based on the developed software and hardware platform. Recognition experiments on 7 classes of actions show that the proposed deep neural network learns well on small datasets, achieving an action recognition accuracy of 97.61%, and the SVM also achieves a recognition accuracy of more than 96%. In the 50 action cycle calculation experiments, the number statistics algorithm reached 100% calculation accuracy, and the action cycle calculation results are also close to the real value, proving the effectiveness of the periodic calculation method.

From base data to knowledge discovery – A life cycle approach – Using multilayer networks

Analysis of complex data sets to infer/discover meaningful information/knowledge involves (after data collection and cleaning): (i) Modeling the data — an approach for deriving a suitable representation of data for analysis, (ii) translating analysis objectives into computations on the generated model instance; these computations can be as simple as a query or a complex computation (e.g., community detection over multiple layers), (iii) computation of expressions generated — considering efficiency and scalability, and (iv) drill-down of results to understand them clearly. Beyond this, it is also useful to visualize results for easier understanding. Covid-19 visualization dashboard presented in this paper is an example of this.This paper covers the above steps of data analysis life cycle using a representation (or model) that is gaining importance. With complex data sets containing multiple entity types and relationships, an appropriate model to represent the data is important. For these data sets, we first establish the advantages of Multilayer Networks (or MLNs) as a data model. Then we use an entity-relationship based approach to convert the data set into MLNs for a precise representation of the data set. After that, we outline how expected analysis objectives can be translated using keyword-mapping to aggregate analysis expressions. Finally, we demonstrate, through a set of example data sets and objectives, how the expressions corresponding to objectives are evaluated using an efficient decoupling-based approach. Results are further drilled down to obtain actionable knowledge from the data set.Using the widely popular Enhanced Entity Relationship (EER) approach for requirements representation, we demonstrate how to generate EER diagrams for data sets and further generate, algorithmically, MLNs as well as Relational schema for analysis and drill down, respectively. Using communities and centrality for aggregate analysis, we demonstrate the flexibility of the chosen model to support diverse set of objectives. We also show that compared to current analysis approaches, a “decoupling-based” approach using MLNs is more appropriate as it preserves structure as well as semantics of the results and is very efficient. For this computation, we need to derive expressions for each analysis objective using the MLN model. We provide guidelines to translate English queries into analysis expressions based on keywords.Finally, we use several data sets to establish the effectiveness of modeling using MLNs and their analysis using the decoupling approach that has been proposed recently. For coverage, we use different types of MLNs for modeling, and community and centrality computations for analysis. The data sets used are from US commercial airlines, IMDb (a large international movie data set), the familiar DBLP (or bibliography database), and the Covid-19 data set. Our experimental analyses using the identified steps validate modeling, breadth of objectives that can be computed, and overall versatility of the life cycle approach. Correctness of results is verified, where possible, using independently available ground truth. Furthermore, we demonstrate drill-down that is afforded by this approach (due to structure and semantics preservation) for a better understanding and visualization of results.

SNAG: SDN-Managed Network Architecture for GridFTP Transfers Using Application-Awareness

Increasingly, academic campus networks support large-scale data transfer workflows for data-intensive science. These data transfers rely on high-performance, scalable, and reliable protocols for moving large amounts of data over a high-bandwidth, high-latency network. GridFTP is a widely used protocol for wide area network (WAN) data movement. However, as the GridFTP protocol does not share connection information with the network-layer, network operators have reduced flexibility, particularly in identifying/managing flows across the network. We address this problem by deploying a production “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-aware</i> ” software defined network (SDN) for managing GridFTP transfers for data-intensive science workflows. We first propose a novel application-aware architecture called SNAG (SDN-managed Network Architecture for GridFTP transfers). SNAG combines <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-layer and network-layer collaboration</i> (termed “application-awareness”) with SDN-enabled network management to classify, monitor and to manage network resources actively. Until now, our SNAG deployment has successfully classified over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5 Billion</i> GridFTP connections at the Holland Computing Center (HCC), University of Nebraska-Lincoln (UNL). Next, we develop an application-aware SDN system to provide differentiated network services for distributed computing workflows. At HCC, we also demonstrate how our system ensures the quality of service (QoS) for high-throughput workflows such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). Further, we also demonstrate how application-aware SDN can be exploited to create <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy-driven</i> approaches to achieve accurate resource accounting for each workflow. We present strategies for implementing differentiated network services and discuss their capacity improvement benefits. Lastly, we provide some guidelines and recommendations for developing application-aware SDN architectures for general-purpose applications.

Deep Data and Precision Health

Inside Precision MedicineVol. 9, No. 4 AI SpotlightDeep Data and Precision HealthAmir BahmaniAmir BahmaniAmir Bahmani, PhD, is the Director of Stanford Deep Data Research Computing Center (DDRCC) and the Director of Science and Technology at Stanford Healthcare Innovation Lab (SHIL) and a lecturer at Stanford University. He has been working on distributed and parallel computing applications since 2008. Currently, Amir is an active researcher in the VA Million Veteran Program (MVP), Human Tumor Atlas Network (HTAN), the Human BioMolecular Atlas Program (HuBMAP), Stanford Metabolic Health Center (MHC) and Integrated Personal Omics Profiling (iPOP).Search for more papers by this authorPublished Online:25 Aug 2022https://doi.org/10.1089/ipm.09.04.12AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 9Issue 4Aug 2022 InformationCopyright © GEN PublishingTo cite this article:Amir Bahmani.Deep Data and Precision Health.Inside Precision Medicine.Aug 2022.44-46.http://doi.org/10.1089/ipm.09.04.12Published in Volume: 9 Issue 4: August 25, 2022PDF download

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this work, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

A new shape-based clustering algorithm for time series

Time series clustering is a research hotspot in data mining. Most of the existing clustering algorithms combine with the classical distance measure which ignore the offset of sequence shape. As a result, shape-based clustering algorithms are becoming increasingly popular. On the majority of data sets, the most representative shape-based clustering algorithm, KShape, which defines a shape-based distance with shift invariance, has been shown to outperform other algorithms.In this paper, we propose a new shape-based clustering algorithm named Fractional Order Shape-based k-cluster(FrOKShape), which defines a multi-variable shape-based distance by normalized fractional order cross-correlation and uses the DTW Barycenter Averaging (DBA) as a center computation strategy. Our distance exhibits excellent shape shift deviating properties and good compatibility integrated with a variety of existing clustering center strategies so that it can provide more potential good results. Experiments show that combining our distance with a traditional clustering algorithm produces excellent clustering indicators. In a series of comparative experiments, FrOKShape also exhibits a comparable result to the existing better shape-based clustering algorithm KShape.