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1. CyberGIS-compute for enabling computationally intensive geospatial research

   https://doi.org/10.1145/3486189.3490017  

   Padmanabhan, Anand; Ziao, Ximo; Vandewalle, Rebecca C.; Baig, Furqan; Michel, Alexander; Li, Zhiyu; Wang, Shaowen (November 2021, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science (SpatialAPI’21))

   Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGISJupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework. 

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2. Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations

   https://doi.org/https://doi.org/10.1145/3311790.3396642  

   Chalker, Alan; Hillegas, Curtis W.; Sill, Alan; Broude Geva, Sharon; Stewart, Craig A. (July 2020, PEARC '20: Practice and Experience in Advanced Research Computing)

   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. 

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3. Cyberinfrastructure deployments on public research clouds enable accessible Environmental Data Science education

   https://doi.org/10.1145/3569951.3597606  

   McIntosh, Tyler L; Verleye, Erick; Balch, Jennifer K; Cattau, Megan E; Ilangakoon, Nayani T; Korinek, Nathan; Nagy, R. Chelsea; Sanovia, James; Skidmore, Edwin; Swetnam, Tyson L; et al (July 2023, ACM)

   Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline. 

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4. Parallel Framework for Data-Intensive Computing with XSEDE

   https://doi.org/10.1145/3332186.3338097  

   Subramanian, Ranjini; Zhang, Hui (July 2019, PEARC '19 Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning))

   With the increase in data-driven analytics, the demand for high performing computing resources has risen. There are many high-performance computing centers providing cyberinfrastructure (CI) for academic research. However, there exists access barriers in bringing these resources to a broad range of users. Users who are new to data analytics field are not yet equipped to take advantage of the tools offered by CI. In this paper, we propose a framework to lower the access barriers that exist in bringing the high-performance computing resources to users that do not have the training to utilize the capability of CI. The framework uses divide-and-conquer (DC) paradigm for data-intensive computing tasks. It consists of three major components - user interface (UI), parallel scripts generator (PSG) and underlying cyberinfrastructure (CI). The goal of the framework is to provide a user-friendly method for parallelizing data-intensive computing tasks with minimal user intervention. Some of the key design goals are usability, scalability and reproducibility. The users can focus on their problem and leave the parallelization details to the framework. 

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5. Overview of ACCESS allocated cyberinfrastructure usage

   https://doi.org/10.1145/3626203.3670521  

   White, Joseph Patrick; Weeden, Aaron; Deleon, Robert; Furlani, Thomas; Jones, Matthew D (July 2024, ACM)

   ACCESS is a program established and funded by the National Sci- ence Foundation to help researchers and educators use the NSF na- tional advanced computing systems and services. Here we present an analysis of the usage of ACCESS allocated cyberinfrastructure over the first 16 months of the ACCESS program, September 2022 through December 2023. For historical context, we include analyses of ACCESS and XSEDE, its NSF funded predecessor, for the ten-year period from January 2014 through December 2023. The analyses in- clude batch compute resource usage, cloud resource usage, science gateways, allocations, and users. 

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