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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Pavanatto, Leonardo Giovannelli, Alexander Giera, Brian Bremer, Timo Miao, Haichao Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798331536459

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding. © 2025 IEEE.

关键词： Virtual reality

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Testing GPU Numerics: Finding Numerical Differences Between ...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Zahid, Anwar Hossain Laguna, Ignacio Le, Wei Iowa State University Department of Computer Science AmesIA United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing LivermoreCA United States

ISBN: (纸本)9798350355543

As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs;then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD's HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY. © 2024 IEEE.

关键词： CUDA Differential testing floating-point HIP random program generation

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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Learning Channels to Support Interaction and Collaboration in CourseMapper 14

Learning Channels to Support Interaction and Collaboration i...

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14th International Conference on Education Technology and computers, ICETC 2022

作者： Ain, Qurat Ul Chatti, Mohamed Amine Joarder, Shoeb Nassif, Ilia Wobiwo Teda, Benjamine Stella Guesmi, Mouadh Alatrash, Rawaa Social Computing Group Department of Computer Science and Applied Cognitive Science University of Duisburg-Esse Duisburg Germany

ISBN: (纸本)9781450397766

Massive Open Online Courses (MOOCs) have proved to be revolutionary in the development and widespread of educational technology over the past few years. It has been identified that these systems, despite all the benefits, lack interaction and collaboration around learning materials. This paper explores how to leverage learning channels to support interaction and collaboration in MOOC environments. We present the design, implementation, and evaluation details of learning channels in CourseMapper, a MOOC platform which supports collaboration and interaction around learning materials using collaborative Pdf and video annotations. More specifically, we report on the application and results of a human-centered design (HCD) approach used to systematically design learning channels. Three design iterations with five new users in each iteration (N=15) were conducted from an initial user interview to the final design. After the development, user experience with the system was evaluated with end users (N=10) based on the Technology Acceptance Model (TAM). The evaluation results show that the introduction of learning channels provides a valuable mechanism to support effective interaction and collaboration in a MOOC environment. © 2022 ACM.

关键词： Curricula

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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Web service location-allocation using discrete NSGA-II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized computing 2023年第10期14卷 14163-14187页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science VSB-Technical University of Ostrava Ostrava70800 Czech Republic

Web Service Location-Allocation Problem (WSLAP) is an important and challenging NP-hard combinatorial optimization problem where multiple objectives affecting the quality of service (QoS) are to be optimized simultaneously. Most of the existing research articles mainly focus on selecting web service locations that minimize two QoS: total cost and total response time. However, another QoS factor throughput also needs attention when selecting sites for allocating web services. Therefore, we have constructed a new WSLAP model that minimizes the total cost and the total negative quality factor. This negative quality factor considers throughput along with latency to strengthen the QoS of the located web services. Further, three NSGA-II variants, NSGA-II.V1, NSGA-II.V2, and NSGA-II.V3, with a novel matrix-based chromosome representation, are developed to deal with the proposed bi-objective WSLAP model. These algorithms involve matrix-based genetic operations for better exploration and a repair mechanism for tackling infeasibility. The proposed model and algorithms are validated on 24 test instances, out of which 14 are existing, and 10 are newly created with increased web services, user centres and service locations. The experimental results claim that the proposed WSLAP model succeeded in improving the throughput with a comparatively lower total cost. Further, the proposed algorithms outperformed the compared algorithms in terms of hypervolume, inverted generational distance and computational time on 89% of instances. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Quality of service

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HPC-Coder: Modeling Parallel Programs using Large Language Models 39

HPC-Coder: Modeling Parallel Programs using Large Language M...

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39th International Conference on High Performance computing, ISC High Performance 2024

作者： Nichols, Daniel Marathe, Aniruddha Menon, Harshitha Gamblin, Todd Bhatele, Abhinav Department of Computer Science University of Maryland College ParkMD United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Livermore Computing Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9783982633602

Parallel programs in high performance computing (HPC) continue to grow in complexity and scale in the exascale era. The diversity in hardware and parallel programming models make developing, optimizing, and maintaining parallel software even more burdensome for developers. One way to alleviate some of these burdens is with automated development and analysis tools. Such tools can perform complex and/or remedial tasks for developers that increase their productivity and decrease the chance for error. Until recently, such tools for code development and performance analysis have been limited in the complexity of tasks they can perform, especially for parallel programs. However, with recent advancements in language modeling, and the availability of large amounts of open-source code related data, these tools have started to utilize predictive language models to automate more complex tasks. In this paper, we show how large language models (LLMs) can be applied to tasks specific to high performance and scientific codes. We introduce a new dataset of HPC and scientific codes and use it to fine-tune several pre-trained models. We compare several pre-trained LLMs on HPC-related tasks and introduce a new model, HPC-Coder, fine-tuned on parallel codes. In our experiments, we show that this model can auto-complete HPC functions where generic models cannot, decorate for loops with OpenMP pragmas, and model performance changes in scientific application repositories as well as programming competition solutions. © 2024 Research Paper Proceedings of the ISC High Performance 2024. All rights reserved.

关键词： Modeling languages

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