Ohio Supercomputer Center

Introduction: Peripheral artery disease (PAD) is characterized by atherosclerosis of lower extremity arteries that promotes reduced muscle perfusion and skeletal muscle atrophy, which contributes to functional impairment in PAD patients. Computed tomography (CT) imaging offers a non-invasive method for quantifying peripheral muscle characteristics; however, manual image segmentation of muscles is time-consuming, and regional muscle analysis remains understudied in PAD. Therefore, we sought to develop and validate a deep learning approach for calf muscle segmentation to assess serial regional changes in muscle density of PAD patients over an 18-month study period. Methods: Patients with PAD (n=89) were prospectively enrolled for lower extremity non-contrast CT imaging. A subset of patients (n=45) was recruited for an additional follow-up CT scan 18 months later. Calf muscle groups, including the gastrocnemius, soleus, and tibialis anterior, were manually segmented from axial CT images of each patient’s symptomatic limb, and mean muscle densities for each muscle were quantified based on CT Hounsfield units. Manual segmentations were used to train an nnU-Net deep learning model. Data augmentation techniques were performed to enhance generalization. The dataset was split into an 80/20 ratio for the training and test sets. Dice coefficients were calculated to evaluate the overlap/agreement between manual and deep learning segmentations. Paired t-tests were performed to assess the differences in muscle densities between the baseline and 18-month follow-up measures. Results: The deep learning model achieved high segmentation performance, with dice scores of 0.90 ± 0.02 for the gastrocnemius and soleus, and 0.89 ± 0.02 for the tibialis anterior. Deep learning-guided serial CT image analysis detected a significant reduction in muscle densities across all muscle groups at 18-month follow-up when compared to baseline measurements (p<0.05). Conclusion: Deep learning analysis can enable rapid, regional analysis of lower extremity skeletal muscle characteristics, reducing the time for muscle-by-muscle analysis in PAD patients from hours to seconds. Future implementation of regional muscle analysis in PAD may assist vascular medicine specialists with identifying regional muscle wasting that is related to patient walking impairment and/or symptoms as well as aid in serial monitoring of PAD patients following revascularization or supervised exercise for claudication.

Abstract Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPC computing continuum, plug‐and‐play next‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.

The U.S. Department of Energy (DOE) is a long-standing leader in research and development of high-performance computing (HPC) in the pursuit of science. However, we face daunting challenges in fostering a robust and diverse HPC workforce. Basic HPC is

Distributed cyberinfrastructures (CI) pose opportunities and challenges for the execution of scientific workflows, especially in the context of Earth science applications. They provide heterogeneous resources that can meet the needs of the applications that are part of the scientific workflows and provide the necessary performance and scalability to achieve scientific goals. However, the challenge with distributed CI is that it is difficult to find the right resources for the applications and to orchestrate the workflow execution from resource provisioning to job execution to delivering the final results. In some cases, poor choice of resources may result in slow execution or outright failure. In this paper, we present Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) Pegasus, a CI solution built as part of the U.S. National Science Foundation ACCESS program that provides automated execution of scientific applications. We demonstrate Pegasus's capabilities with SOil MOisture SPatial Inference Engine (SOMOSPIE), an earth science multi-component application for fine-grained soil moisture predictions. We identify a roadmap to migrate applications such as SOMOSPIE on ACCESS resources with the support of ACCESS Pegasus, outlining both strengths and weaknesses of this approach.

poster Share on ACCESS Pegasus: Bringing Workflows to the ACCESS Masses Authors: Mats Rynge USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0002-1779-7189View Profile , Karan Vahi USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0001-8622-2082View Profile , Mohammad Zaiyan Alam USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0009-0006-5705-2639View Profile , Ewa Deelman USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0001-5106-503XView Profile , Todd Miller University of Wisconsin-Madison, USA University of Wisconsin-Madison, USA 0009-0003-0683-3062View Profile , Miron Livny University of Wisconsin-Madison, USA University of Wisconsin-Madison, USA 0000-0001-5444-7439View Profile , Shelley Knuth Research Computing, University of Colorado, USA Research Computing, University of Colorado, USA 0000-0001-7249-4773View Profile , James Griffioen University of Kentucky, USA University of Kentucky, USA 0000-0002-5105-096XView Profile , John Goodhue Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0009-0003-8344-9945View Profile , David Hudak Ohio Supercomputer Center, USA Ohio Supercomputer Center, USA 0000-0002-9043-0850View Profile , Julie Ma Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0000-0001-6220-4314View Profile , Andrew Pasquale Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0000-0002-7501-367XView Profile , Lissie Fein Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0009-0000-9419-9150View Profile Authors Info & Claims PEARC '23: Practice and Experience in Advanced Research ComputingJuly 2023Pages 478–480https://doi.org/10.1145/3569951.3597590Published:10 September 2023Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Patterns of genetic diversity within species contain information the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta‐analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time‐consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready‐to‐analyse sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR: large meta‐analyses of thousands of species that can address classic questions in evolutionary biology and ecology, and student‐ or citizen‐ science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web‐based tools that enable these data to be recycled and reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources.

Abstract Patterns of genetic diversity within species contain information about the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta-analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time-consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR)that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready-to-analyze sets of data for thousands of species. Two types of scientific research will be made easier byphylogatR, large meta-analyses of thousands of species that can address classic questions in evolutionary biology and ecology and student- or citizen-science based investigations that will introduce a broad range of people to the analysis of genetic data.phylogatRenhances the value of existing data via the creation of software and web-based tools that enable these data to be recycled and reanalyzed and increase accessibility to big data for research labs and classroom instructors with limited computational expertise and resources.

Abstract Software Defined Radio Laboratory Platform for Enhancing Undergraduate Communication and Networking Curricula Zhiqiang Wu1, Bin Wang1, Chi-Hao Cheng2, Deng Cao3 and Ashraf Yaseen3 1 Wright State University, 2Miami University and 3Central State UniversityThe advances of communication and networking have changed the world fundamentally.Communication and networking courses, especially wireless communication and networking courses,have become an integral part of the Electrical Engineering, Computer Science, and ComputerEngineering curricula. However, most of these courses are taught at many institutions without alaboratory. For those courses associated with labs, often special hardware based experiment systemsare used. These experiment systems are expensive so most schools cannot afford them. Moreimportantly, such systems lack the flexibility to evolve over time and adapt to different environments. Inour previous NSF funded CCLI project “Evolvable wireless laboratory design and implementation forenhancing undergraduate wireless engineering education”, we have developed and demonstrated thefirst nationwide example of evolvable software defined radio (SDR) based laboratories for three existingundergraduate courses. These laboratories have been well received by the students, and havesignificantly improved the learning outcomes of such courses. Furthermore, these labs have attractedstudents to these courses: the enrollment of these courses has increased drastically after theintroduction of these labs. Based on our success of this project, we are developing a suite ofexperiments and laboratories into a sequence of courses (ranging from freshmen year introductorycourse to senior year elective courses and capstone design projects) that vertically integrates the SDRbased experiment approach in this NSF TUES type II project. We are also equipped with hardware basedexperiment systems to evaluate and compare the teaching effectiveness of the novel SDR approach tothat of traditional hardware equipment approach. The novel SDR approach and laboratory suite will beimplemented, transferred and institutionalized at three participating institutions (Wright StateUniversity, Miami University (a mostly undergraduate serving institution), and Central State University(an HBCU)) to demonstrate the capability of enhancing student learning and easy adaptability by otherinstitutions.

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure. Critically important findings from this first survey include the following: many of the respondents are engaged in some form of analysis of return in research computing investments, but only a minority currently report the results of such analyses to their upper-level administration. Most respondents are experimenting with use of commercial cloud resources but no respondent indicated that they have found use of commercial cloud services to create financial benefits compared to their current methods. There is clear correlation between levels of investment in research cyberinfrastructure and the scale of both cpu core-hours delivered and the financial level of supported research grants. Also interesting is that almost every respondent indicated that they participate in some sort of national cooperative or nationally provided research computing infrastructure project and most were involved in academic computing-related organizations, indicating a high degree of engagement by institutions of higher education in building and maintaining national research computing ecosystems. Institutions continue to evaluate cloud-based HPC service models, despite having generally concluded that so far cloud HPC is too expensive to use compared to their current methods.

Regression testing of HPC systems is of crucial importance when it comes to ensure the quality of service offered to the end users. At the same time, it poses a great challenge to the systems and application engineers to continuously maintain regression tests that cover as many aspects as possible of the user experience. In this paper, we briefly present ReFrame, a framework for writing regression tests for HPC systems and how this is used by CSCS, NERSC and OSC to continuously test their systems. ReFrame is designed to abstract away the complexity of the interactions with the system and to separate the logic of a regression test from the low-level details, which pertain to the system configuration and setup. Regression tests in ReFrame are simple Python classes that specify the basic parameters of the test plus any additional logic. The framework will load the test and send it down a well-defined pipeline which will take care of its execution. ReFrame can be easily set up on any cluster and its straightforward invocation allows it to be easily integrated with common continuous integration/deployment (CI/CD) tools, in order to perform continuous testing of an HPC system. Finally, its ability to feed the collected performance data to well known log channels, such as Syslog, Graylog or, simply, parsable log files, make it also a powerful tool for continuously monitoring the health of the system from user’s perspective.