An official website of the United States government
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.
more »
« less
COMPLECS: COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure
The needs of cyberinfrastructure (CI) Users are different from those of CI Contributors. Typically, much of the training in advanced CI addresses developer topics such as MPI, OpenMP, CUDA and application profiling, leaving a gap in training for these users. To remedy this situation, we developed a new program: COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure (COMPLECS). COMPLECS focuses exclusively on helping CI Users acquire the skills and knowledge they need to efficiently accomplish their compute- and data-intensive research, covering topics such as parallel computing concepts, data management, batch computing, cybersecurity, HPC hardware overview, and high throughput computing.
more »
« less
- Award ID(s):
- 2320934
- PAR ID:
- 10533766
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704192
- Page Range / eLocation ID:
- 1 to 4
- Subject(s) / Keyword(s):
- Computing education workforce development advanced cyberinfrastructure
- Format(s):
- Medium: X
- Location:
- Providence RI USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Advances in the volume, diversity, and complexity of research data and associated workflows requires enhanced capabilities to access, secure, reuse, process, analyze, understand, curate, share, and preserve data. To address this need at the regional level the University of Colorado, Colorado State University, and the University of Utah formed a "Cyberteam" in 2017 to provide cyberinfrastructure (CI) support to researchers at institutions in the Rocky Mountain Advanced Computing Consortium (RMACC) encompassing states across the Intermountain West. The Cyberteam is comprised of CI professionals across the three institutions who collaborate closely, sharing expertise and resources. Since its establishment, the Cyberteam has worked to broaden accessibility and options for computing, storage, and data publishing for RMACC researchers; enhance training on data- and workflow-oriented topics; improve engagement with researchers using CI; and better understand user needs and challenges. One key accomplishment has been the development of a series of focus group and survey instruments to achieve better understanding the CI needs and challenges of researchers across a diverse spectrum of disciplines. This paper provides an overview of the RMACC Cyberteam's objectives, accomplishments, challenges, and future direction.more » « less
-
While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CI- training approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called “tech-labs,” assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.more » « less
-
As the volume and sophistication of cyber-attacks grow, cybersecurity researchers, engineers and practitioners rely on advanced cyberinfrastructure (CI) techniques like big data and machine learning, as well as advanced CI platforms, e.g., cloud and high-performance computing (HPC) to assess cyber risks, identify and mitigate threats, and achieve defense in depth. There is a training gap where current cybersecurity curricula at many universities do not introduce advanced CI techniques to future cybersecurity workforce. At Old Dominion University (ODU), we are bridging this gap through an innovative training program named DeapSECURE (Data-Enabled Advanced Training Program for Cyber Security Research and Education). We developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Each workshop includes a lecture providing the motivation and context for a CI technique, which is then examined during a hands-on session. The modules are delivered through (1) monthly workshops for ODU students, and (2) summer institutes for students from other universities and Research Experiences for Undergraduates participants. Future plan for the training program includes an online continuous learning community as an extension to the workshops, and all learning materials available as open educational resources, which will facilitate widespread adoption, adaptations, and contributions. The project leverages existing partnerships to ensure broad participation and adoption of advanced CI techniques in the cybersecurity community. We employ a rigorous evaluation plan rooted in diverse metrics of success to improve the curriculum and demonstrate its effectiveness.more » « less
-
Abstract Maintaining educational resources and training materials as timely, current, and aligned with the needs of students, practitioners, and other users of geospatial technologies is a persistent challenge. This is particularly problematic within CyberGIS, a subfield of Geographic Information Science and Technology (GIS&T) that involves high‐performance computing and advanced cyberinfrastructure to address computation‐ and data‐intensive problems. In this study, we analyzed and compared content from two open educational resources: (1) a popular online web resource that regularly covers CyberGIS‐related topics (GIS Stack Exchange) and (2) existing and proposed content in the GIS&T Body of Knowledge. While current curricula may build a student's conceptual understanding of CyberGIS, there is a noticeable lack of resources for practical implementation of CyberGIS tools. The results highlight discrepancies between the attention and frequency of CyberGIS topics according to a popular online help resource and the CyberGIS academic community.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626203.3670553
