With the rapid growth of accumulated data from various scientific domains, traditional data management systems face challenges in supporting complicated queries, such as pattern search, on massive amounts of data. To serve sophisticated queries through capturing precise features from data, recent data management systems seek to use artificial intelligence (AI) within the querying process. However, the characteristic of AI inference workflow within the querying process, such as intensive computation and expensive requirements for computing resources, becomes a bottleneck of the AI-powered query systems.In this paper, we provide a generalization of AI inference workflow in the context of AI-powered data discovery and we introduce three different caching strategies corresponding to each stage in the AI inference workflow. We provide in-depth performance evaluation on the impact of these caching strategies through a series of strong scaling experiments. Our experimental results show that the AI-powered data querying performance can be significantly improved by applying different caching strategies.

This paper presents results from a rapid-response industry-academia collaboration for virtual screening of chemical, natural and virtual drug ligands towards identifying potential therapeutics for COVID-19. Compared to resource-intensive traditional approaches of either conducting high- throughput screening in a lab or in-silico molecular dynamics simulations on supercomputers, we have developed an open- source framework that leverages artificial intelligence (AI) to accurately and quickly predict the binding potential of a drug ligand with a target protein. We have trained a novel molecular-highway graph neural network architecture using the entirety of the BindingDB database to predict the probability of a drug ligand binding to a protein target. Our approach achieves a prodigious 98.3% accuracy with its predictions. Through this paper, we disseminate our source code and use the AI model to screen both public (ChEMBL, DrugBank) and proprietary databases. Compared to other AI-based methods, our approach outperforms the state-of-the-art on the following metrics - (i) number of molecules currently undergoing active clinical trials, (ii) number of antiviral drugs correctly identified, (iii) accuracy despite not needing active-site priors, and (iv) ability to screen more compounds in unit time.

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. The novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). Based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.

Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-of-use, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.

Efficient and scalable matrix operations are being highly demanding in the recent era of Machine Learning, Deep Learning, and Big Data Analytics. The two commonly used matrix-matrix operations in the Basic Linear Algebra Subprograms (BLAS) specification are General Matrix-Matrix multiplication (GEMM) and Symmetric Rank-k update (SYRK). The SYRK routine is a specialization of the GEMM routine, where half of the multiplications are skipped as the resultant matrix is known to be symmetric. Fortunately, several linear algebra libraries implement these BLAS routines quite efficiently. The libraries usually partition the input matrices into blocks and place them in processor caches, thus improving performance by leveraging the caches. However, the contemporary libraries are highly optimized for squarish matrices, but the performance degrades significantly for the matrices with edge case (strictly thin or strictly fat shapes) in the multicore machine. The primary reason is that the current state-of-the-art libraries make fixed block shapes based on a processor architecture, and do not consider the shape of the input matrices. In this paper, we propose a new blocking approach, we name it Flexible-blocking, to mitigate the scalability issues. In contrast to the contemporary libraries, our approach formulates the blocks of the input matrices based on the shapes of the matrices as well as the number of threads used in the implementation. Our proposed technique shows noticeable performance improvement on multicore shared-memory machines for the edge case matrices.

Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection techniques are better suited for multi-spectral images and time series based techniques are more suited for analyzing multi-temporal images. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of three components: hierarchical clustering tree construction, nearest neighbor based classification, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.

The construction of data analysis infrastructures that handle continuously accumulating data is quickly becoming an essential requirement for many organizations such as the U.S. Department of Energy (DOE). While DOE supports some of the largest computing facilities in the world, new analysis infrastructures like Apache Spark are difficult to implement. In this paper, we propose an on-demand Spark service that mitigates these difficulties, allowing facility users to flexibly create Spark instances quickly and easily. We define a systematic approach for creating these Spark instances and validate that optimal performance benefits are maintained. Using a series of benchmarks for algorithms that are commonly used in scientific workflows, we compared the behavior of Spark tasks using facility resources with that of an open research cloud that has a dedicated Spark infrastructure deployed. Finally, we leveraged a scientific use case from the Center of Nanophase Materials at the Oak Ridge National Laboratory to demonstrate the utility of using Spark in the computing facility.

In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal component analysis and non-negative matrix factorization on an image-analysis use case at the Oak Ridge Leadership Computing Facility (OLCF). Based on experience with the use-case, we conclude that piecing scalable analytic kernels into user-defined analytic workflows are a flexible, modular and agile way to enable architecture-portable productivity for the data-intensive sciences.

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating ‘Big Data’ from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the ‘Big Data’ hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented ‘Mini-Apps’ of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Data driven science, accompanied by the explosion of petabytes of data, has called into need dedicated analytics computing resources. Dedicated analytics clusters require large capital outlays due to their expensive hardware requirements. Additionally, if such resources are located far from the data they analyze, they also incur substantial data transfer, which has both cost and latency implications. In this paper, we benchmark a variety of high-performance computing (HPC) architectures for classic data science algorithms, as well as conduct a cost analysis of these architectures. Additionally, we compare algorithms across analytic frameworks, as well as explore hidden costs in the form of queuing mechanisms. We observe that node architectures with large memory and high memory bandwidth are better suited for big data analytics on HPC hardware. We also conclude that cloud computing is more cost effective for small or experimental data workloads, but HPC is more cost effective at scale. Additionally, we quantify the hidden costs of queuing and how it relates to data science workloads. Finally, we observe that software developed for the cloud, such as Spark, performs significantly worse than pbdR when run in HPC environments.

Diabetic retinopathy (DR) is a leading cause of blindness and common complication of diabetes. Many diabetic patients take antihypertensive drugs to prevent cardiovascular problems, but these drugs may have unintended consequences on eyesight. Six common classes of antihypertensive drug are angiotensin converting enzyme (ACE) inhibitors, alpha blockers, angiotensin receptor blockers (ARBs), β-blockers, calcium channel blockers, and diuretics. Analysis of medical history data might indicate which of these drugs provide safe blood pressure control, and a literature review is often used to guide such analyses. Beyond manual reading of relevant publications, we sought to identify quantitative trends in literature from the biomedical database PubMed to compare with quantitative trends in the clinical data. By recording and analyzing PubMed search results, we found wide variation in the prevalence of each antihypertensive drug in DR literature. Drug classes developed more recently such as ACE inhibitors and ARBs were most prevalent. We also identified instances of change-over-time in publication patterns. We then compared these literature trends to a dataset of 500 diabetic patients from the UT Hamilton Eye Institute. Data for each patient included class of antihypertensive drug, presence and severity of DR. Graphical comparison revealed that older drug classes such as diuretics, calcium channel blockers, and β-blockers were much more prevalent in the clinical data than in the DR and antihypertensive literature. Finally, quantitative analysis of the dataset revealed that patients taking β-blockers were statistically more likely to have DR than patients taking other medications, controlling for presence of hypertension and year of diabetes onset. This finding was concerning given the prevalence of β-blockers in the clinical data. We determined that clinical use of β-blockers should be minimized in diabetic patients to prevent retinal damage.

In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers that have developed use de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multithreaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray's Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing de Bruin graphs as RDF graphs and propose an iterative querying approach for searching cycles to find Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are data mining algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security, healthcare and manufacturing to suggest our efforts be focused along the following axes: (i) the `data science' challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the ` science of data' challenge - the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the `scalable predictive functions' challenge - the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

he role of big data in addressing the needs of the present healthcare system in US and rest of the world has been echoed by government, private, and academic sectors. There has been a growing emphasis to explore the promise of big data analytics in tapping the potential of the massive healthcare data emanating from private and government health insurance providers. While the domain implications of such collaboration are well known, this type of data has been explored to a limited extent in the data mining community. The objective of this paper is two fold: first, we introduce the emerging domain of "big" healthcare claims data to the KDD community, and second, we describe the success and challenges that we encountered in analyzing this data using state of art analytics for massive data. Specifically, we translate the problem of analyzing healthcare data into some of the most well-known analysis problems in the data mining community, social network analysis, text mining, and temporal analysis and higher order feature construction, and describe how advances within each of these areas can be leveraged to understand the domain of healthcare. Each case study illustrates a unique intersection of data mining and healthcare with a common objective of improving the cost-care ratio by mining for opportunities to improve healthcare operations and reducing what seems to fall under fraud, waste, and abuse.