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1. Parallel R Computing on the Web

   https://doi.org/10.1109/BigData47090.2019.9006311  

   Subramanian, Ranjini; Zhang, Hui (December 2019, 2019 IEEE International Conference on Big Data (Big Data))

   R is the preferred language for Data analytics due to its open source development and high extensibility. Exponential growth in data has caused longer processing times leading to the rise in parallel computing technologies for analysis. Using R together with high performance computing resources is a cumbersome task. This paper proposes a framework that provides users with access to high-performance computing resources and simplifies the configuration, programming, uploading data and job scheduling through a web user interface. In addition to that, it provides two modes of parallelization of data-intensive computing tasks, catering to a wide range of users. The case studies emphasize the utility and efficiency of the framework. The framework provides better performance, ease of use and high scalability. 

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2. The Impact of Operation Conditions on Potentiation, Depression and Endurance Dynamics of Tantalum Oxide RRAMs

   https://doi.org/10.1109/IIRW59383.2023.10477696  

   Moazzeni, Alireza; Tawsif Rahman Chowdhury, Md; Tutuncuoglu, Gozde (October 2023, IEEE International Integrated Reliability Workshop)

   Resistive Random Access Memory (RRAM) devices hold promise as a key enabler technology for energy-efficient, in-memory, and brain-inspired computing paradigms, with the potential to significantly enhance high-performance computing applications. However, the widespread adoption of RRAM technology in high-performance computing applications is hindered by non-ideal device metrics and various reliability challenges. RRAM devices are reported to exhibit critical device-to-device (D2D) and cycle-to-cycle (C2C) variability. In this paper, we investigate D2D and C2C variabilities of Tantalum Oxide RRAM devices and explore potentiation, depression, and endurance dynamics under varying operation conditions. Our ultimate goal is to address performance and reliability issues associated with the oxide-based RRAM device technology and facilitate its broader implementation in future computing applications. 

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3. Incorporating Complexity in Computing Camps for High School Students – A Report on the Summer Computing Academy Program at Texas A&M University

   https://doi.org/10.22369/issn.2153-4136/11/1/3  

   Chakravorty, D; Pennings, M; Liu, H.; Thomas, X.; Rodriguez, D.; and Perez, M. (January 2020, Journal of computational science education)

   Summer computing camps for high school students are rapidly becoming a staple at High Performance Computing (HPC) centers and Computer Science departments around the country. Developing complexity in education in these camps remains a challenge. Here, we present a report about the implementation of such a program. The Summer Computing Academy (SCA) at is a weeklong cybertraining1 program offered to high school students by High Performance Research Computing (HPRC) at Texas A&M University (Texas A&M; TAMU). The Summer Computing Academy effectively uses cloud computing paradigms, artificial intelligence technologies coupled with Raspberry Pi micro-controllers and sensors to demonstrate “computational thinking”. The program is steeped in well- reviewed pedagogy; the refinement of the educational methods based on constant assessment is a critical factor that has contributed to its success. The hands-on exercises included in the program have received rave reviews from parents and students alike. The camp program is financially self-sufficient and has successfully broadened participation of underrepresented groups in computing by including diverse groups of students. Modules from the SCA program may be implemented at other institutions with relative ease and promote cybertraining efforts nationwide. 

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4. Multiobjective optimization of the variability of the high-performance LINPACK solver.

   https://doi.org/10.5555/3466184.3466538  

   Tyler H. Chang, Jeffrey Larson (January 2020, WSC '20: Proceedings of the Winter Simulation Conference)

   null (Ed.)

   Variability in the execution time of computing tasks can cause load imbalance in high-performance computing (HPC) systems. When configuring system- and application-level parameters, engineers traditionally seek configurations that will maximize the mean computational throughput. In an HPC setting, however, high-throughput configurations that do not account for performance variability could result in poor load balancing. In order to determine the effects of performance variance on computationally expensive numerical simulations, the High-Performance LINPACK solver is optimized by using multiobjective optimization to maximize the mean and minimize the standard deviation of the computational throughput on the High-Performance LINPACK benchmark. We show that specific configurations of the solver can be used to control for variability at a small sacrifice in mean throughput. We also identify configurations that result in a relatively high mean throughput, but also result in a high throughput variability. 

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5. 4 ^th Workshop on Education for High Performance Computing

   https://doi.org/10.1109/HiPCW57629.2022.00016  

   Prasad, Sushil K.; Ghafoor, Sheikh; Kuvelkar, Ashish; Malakar, Preeti (December 2022, IEEE)

   Welcome to the 4 th Workshop on Education for High Performance Computing (EduHiPC 2022). The EduHiPC 2022 workshop, held in conjunction with the IEEE International Conference on High Performance Computing Data & Analytics (HiPC 2022), is devoted to the development and assessment of educational and curricular innovations and resources for undergraduate and graduate education in Parallel and Distributed Computing (PDC) and High Performance Computing (HPC). EduHiPC brings together individuals from academia, industry, and other educational and research institutes to explore new ideas, challenges, and experiences related to PDC pedagogy and curricula. The workshop is designed in coordination with the IEEE TCPP curriculum initiative on parallel and distributed computing ( hitps://tcpp.cs.gsu .edu/curriculum/) for undergraduates majoring in computer science and computer engineering. It is supported by C-DAC, India and the US National Science Foundation (NSF) supported Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). Details for attending the workshop are available on the HiPC webpage (HiPC). The effect of pandemic on academic and research community seems now to be globally receding as was evident from the enthusiastic in-person participation of conference delegates. Please visit the EduHiPC-22 webpage for the complete online proceedings, including copies of papers and presentation slides: EduHiPC 2022 | NSF/IEEE-TCPP Curriculum Initiative. 

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