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Towards Tangible Algorithms: Exploring the Experiences of Tangible Interactions with Movie Recommender Algorithms

proceedings of the acm on Human-Computer Interaction 2022年第2 CSCW期6卷 1-30页

作者： Alvarado, Oscar Vanden Abeele, Vero Geerts, David Verbert, Katrien KU Leuven Leuven Belgium

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. Therefore, centering on transparency and control, we analyzed how 18 users of movie recommender systems perceived four different TUIs using individual co-design sessions and post-interview questionnaires. Through thematic analysis, we identified seven design considerations while designing TUIs to interact with algorithmic movie recommender systems: (1) Distinctions between TUIs and GUIs;(2) TUIs replacing predominant interfaces;(3) Preference for single-device TUIs;(4) The relevance of granular control for TUIs;(5) Apparent transparency limitations of TUIs;(6) TUIs and algorithmic social computing;and (7) Overview of specific design choices, including advantages and disadvantages of soft, hard, rounded, cubic, and humanoid interfaces. These findings inspired Recffy: the first functional TUI designed to enhance awareness and control in personalized movie recommendations. Based on this study, we propose the concept of Tangible Algorithms: TUIs dedicated to enhancing the interaction of algorithmic systems and their profiling processes or decisions in a specific context. Furthermore, we describe the relevance of tangible algorithms and design guidelines to promote them in diverse AI contexts. Finally, we invite the HCI and CSCW community to continue exploring tangible algorithms to address the interaction with algorithmic systems, including the collaborative and social computing dynamics they can promote in diverse AI contexts. © 2022 acm.

关键词： Recommender systems

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Towards Edge-Based Data Lake Architecture for Intelligent Transportation System 20

Towards Edge-Based Data Lake Architecture for Intelligent Tr...

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20th acm International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN)

作者： Fernandes, Danilo Moura, Douglas L. L. Santos, Gean Ramos, Geymerson S. Queiroz, Fabiane Aquino, Andre L. L. Univ Fed Alagoas Maceio Alagoas Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil Univ Lyon INRIA INSA Lyon CITI Villeurbanne France

ISBN: (纸本)9798400703706

The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications.

关键词： Logical Data Lake Edge computing Intelligent Transportation systems

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Safe Shared State in Dataflow systems 18

Safe Shared State in Dataflow Systems

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18th International Conference on Distributed and Event-Based systems (DEBS)

作者： De Martini, Luca Margara, Alessandro Politecn Milan Milan Italy

ISBN: (纸本)9798400704437

The complexity of implementing scalable data processing tasks in parallel and distributed computing environments has pushed the adoption of restricted programming models that simplify expressing such tasks. In this context, the dataflow model has emerged as the standard for building distributed data processing systems. The key insight of this model is to express computations as sequences of operations that do not share any state and can thus be deployed and executed independently on the same or on different machines. however entirely excluding state sharing may be detrimental for performance, as it prevents operations executed on the same machine from accessing common resources. The effects of these limitations become more and more evident as the computational and memory resources of individual hosts increase, and even large-scale data analysis tasks can be performed with few well-equipped machines. Moving from this observation, in this paper we present an extension to the classic dataflow model that enables disciplined state sharing across operators to improve performance. We implement the model in Renoir, a dataflow system written in Rust, and we exploit the characteristics of this programming language to ensure sate access to shared state by design. We present several use cases where our model may be beneficial, and we use them to evaluate the proposed model in terms of performance and code complexity. We show that our model empowers developers to better exploit the resources of machines, resulting in substantial performance improvements in several use cases. Programs written with our extended model may even be more concise and simpler than in the classic dataflow model.

关键词： dataflow shared state distributed programming Rust memory safety data processing

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"I Want to Be Unique From Other Robots": Positioning Girls as Co-creators of Social Robots in Culturally-Responsive computing Education 23

"I Want to Be Unique From Other Robots": Positioning Girls a...

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CHI conference on Human Factors in computing systems (CHI)

作者： Li, Yinmiao Nwogu, Jennifer Buddemeyer, Amanda Solyst, Jaemarie Lee, Jina Walker, Erin Ogan, Amy Stewart, Angela E. B. Northwestern Univ Evanston IL USA Univ Pittsburgh Pittsburgh PA USA Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9781450394215

Robot technologies have been introduced to computing education to engage learners. This study introduces the concept of co-creation with a robot agent into culturally-responsive computing (CRC). Co-creation with computer agents has previously focused on creating external artifacts. Our work differs by making the robot agent itself the co-created product. Through participatory design activities, we positioned adolescent girls and an agentic social robot as co-creators of the robot's identity. Taking a thematic analysis approach, we examined how girls embody the role of creator and co-creator in this space. We identified themes surrounding who has the power to make decisions, what decisions are made, and how to maintain social relationship. Our findings suggest that co-creation with robot technology is a promising implementation vehicle for realizing CRC.

关键词： co-creation culturally responsive computing

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Filling the Void: Data-Driven Machine Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Filling the Void: Data-Driven Machine Learning-based Reconst...

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

作者： Biswas, Ayan Mishra, Aditi Majumder, Meghanto Hazarika, Subhashis Most, Alexander Castorena, Juan Bryan, Christopher Mccormick, Patrick Ahrens, James Lawrence, Earl Hagberg, Aric Los Alamos National Lab Los Alamos United States Arizona State University Tempe United States Fujitsu Research of America Inc Santa Clara United States

ISBN: (纸本)9798350355543

As high-performance computing systems continue to advance, the gap between computing performance and I/O capabilities is widening. This bottleneck limits the storage capabilities of increasingly large-scale simulations, which generate data at never-before-seen granularities while only being able to store a small subset of the raw data. Recently, strategies for data-driven sampling have been proposed to intelligently sample the data in a way that achieves high data reduction rates while preserving important regions or features with high fidelity. However, a thorough analysis of how such intelligent samples can be used for data reconstruction is lacking. We propose a data-driven machine learning approach based on training neural networks to reconstruct full-scale datasets based on a simulation's sampled output. Compared to current state-of-the-art reconstruction approaches such as Delaunay triangulation-based linear interpolation, we demonstrate that our machine learning-based reconstruction has several advantages, including reconstruction quality, time-to-reconstruct, and knowledge transfer to unseen timesteps and grid resolutions. We propose and evaluate strategies that balance the sampling rates with model training (pretraining and fine-tuning) and data reconstruction time to demonstrate how such machine learning approaches can be tailored for both speed and quality for the reconstruction of grid-based datasets. © 2024 IEEE.

关键词： computing methodologies data reduction machine learning machine learning approaches modeling and simulation neural networks reconstruction scientific visualization simulation types and techniques

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Leveraging the Compute Power of Two HPC systems for Higher-Dimensional Grid-Based Simulations with the Widely-Distributed Sparse Grid Combination Technique 23

Leveraging the Compute Power of Two HPC Systems for Higher-D...

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2023 International Conference for High Performance computing, Networking, Storage and analysis, SC 2023

作者： Pollinger, Theresa Van Craen, Alexander Niethammer, Christoph Breyer, Marcel Pflüger, Dirk Institute for Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany High Performance Computing Center Universität Stuttgart Stuttgart Germany

ISBN: (纸本)9798400701092

Grid-based simulations of hot fusion plasmas are often severely limited by computational and memory resources;the grids live in four- to six-dimensional space and thus suffer the curse of dimensionality. However, high resolutions are required to fully capture the physics of interest. The sparse grid combination technique is a multi-scale method in which many anisotropically coarse resolved grids are used to approximate a fine-scale solution - -and it alleviates the curse of *** paper presents the core concepts of the widely-distributed combination technique, which allows us to use the compute power and memory of more than one HPC system for the same simulation. We apply the sparse grid combination technique to a six-dimensional advection problem serving as a proxy for plasma simulations. The full-grid solution approximated by the combination technique would contain ≈ 5 ZB if computed with conventional grid-based methods. Even the combination technique simulation operates on ≈ 1 × 1011 double-precision degrees of freedom, or 988 GB, plus the supporting sparse grid data structures. We propose a new approach to divide the compute load between the two HPC systems, requiring only 76 GB to be exchanged. Based on this, we have realized the first synchronous combination technique simulation using two HPC systems, in our case the two German Tier-0 supercomputers HAWK and SuperMUC-NG. On two systems, the simulation can be computed at an average overhead of ≈ 35 % (108 s per combination step) for file I/O and transfer. The presented concepts apply to any pair of HPC systems if high-speed data transfer is possible. © 2023 acm.

关键词： Supercomputers

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IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on Truly Heterogeneous Runtime systems

IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on...

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

作者： Johnston, Beau De Boissiere, Thibault Monil, Mohammad Alaul Haque Miniskar, Narasinga Rao Young, Aaron Lee, Seyong Vetter, Jeffrey S. Oak Ridge National Laboratory Oak RidgeTN United States Canva Incorporated SydneyNSW Australia

ISBN: (纸本)9798350355543

The diversity of accelerators in computer systems poses significant challenges for software developers, such as managing vendor-specific compiler toolchains, code fragmentation requiring different kernel implementations, and performance portability issues. To address these, the Intelligent Runtime System (IRIS) was developed. IRIS works across various systems, from smartphones to supercomputers, enabling automatic performance scaling based on available accelerators. It introduces abstract tasks for seamless execution transitions between accelerators while ensuring memory consistency and task dependencies. Although IRIS simplifies system details, optimal dynamic scheduling still requires user input to understand workload structures. To address this, we introduce a new scheduling policy for IRIS, termed IRIS-GNN, which is the first IRIS hybrid policy that operates in conjunction with the dynamic policies. This policy employs a Graph-Neural Network (GNN) to conduct Graph Classification of any task graphs submitted to IRIS. This GNN analyzes the structure and attributes of the task graph, categorizing it as either locality, concurrency, or mixed. This classification subsequently guides the selection of the dynamic policy used by IRIS. We provide a comparison of the performance of IRIS-GNN against the complete spectrum of IRIS's dynamic policies, assess the overhead introduced by the GNN within this scheduling framework, and ultimately explore its practical application in real-world scenarios. © 2024 IEEE.

关键词： accelerators high-performance computing runtime systems scheduling

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NPAT - A Power analysis Tool at NERSC

NPAT - A Power Analysis Tool at NERSC

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2023 International Conference on High Performance computing, Network, Storage, and analysis, SC Workshops 2023

作者： Zhang, Andy Bhalachandra, Sridutt Deng, Siqi Zhao, Zhengji University of California Los Angeles BerkeleyCA United States Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9798400707858

Power has recently become a significant limiting factor in supercomputing. It is imperative that future high-performance computing (HPC) systems are energy-efficient. Efficient system designs necessitate understanding the power signature of current supercomputing workloads. Additionally, that understanding will enable power efficiency improvements in existing systems. With advancements in power measurement and data collection techniques, many computing centers, including NERSC, collect a vast amount of data every second, including power usage and other performance metrics. However, access is not straightforward, being a time-consuming process. In this paper, we propose NPAT, a power analysis tool that aims to provide easy access to power usage data on the web. It provides a quick and accessible way to view the power usage data of NERSC systems, jobs, and applications. Being implemented in PHP, Javascript, and Python with open-source libraries and modules, it promises effortless portability to other sites. © 2023 Owner/Author.

关键词： Python

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A Joint analysis of Input Resolution and Quantization Precision in Deep Learning 23

A Joint Analysis of Input Resolution and Quantization Precis...

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29th Annual International Conference on Mobile computing and Networking, MobiCom 2023

作者： Kim, Seonjun Kim, Minjae Lee, Youngki Seoul National University Seoul60013682 Korea Republic of

ISBN: (纸本)9781450399906

Deep learning models have become increasingly prevalent in various domains, necessitating their deployment on resource-constrained devices. Quantization is a promising way to reduce the model complexity in that it keeps model architecture intact and enables the model to operate on specialized hardwares(e.g., NPU, DSP). Input resolution is also essential in making a trade-off between accuracy and *** this paper, we conduct a joint analysis of input resolution and quantization precision on their influence on accuracy for three popular models: ResNet-18, ResNet-50, and MobileNet-V2. By exploring the combined configuration space, we found that better accuracy can be achieved by jointly optimizing the input resolution and quantization bit-width while maintaining the computational complexity. © 2023 acm.

关键词： Economic and social effects

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Beacons: An End-to-End Compiler Framework for Predicting and Utilizing Dynamic Loop Characteristics

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proceedings OF THE acm ON PROGRAMMING LANGUAGES-PacmPL 2023年第OOPSLA期7卷 173–203页

作者： Mururu, Girish Khan, Sharjeel Chatterjee, Bodhisatwa Chen, Chao Porter, Chris Gavrilovska, Ada Pande, Santosh Georgia Inst Technol Santa Clara CA 95053 USA Georgia Inst Technol Atlanta GA 30332 USA IBM Res Yorktown Hts NY USA

Efficient management of shared resources is a critical problem in high-performance computing (HPC) environments. Existing workload management systems often promote non-sharing of resources among different co-executing applications to achieve performance isolation. Such schemes lead to poor resource utilization and suboptimal process throughput, adversely affecting user productivity. Tackling this problem in a scalable fashion is extremely challenging, since it requires the workload scheduler to possess an in-depth knowledge about various application resource requirements and runtime phases at fine granularities within individual applications. In this work, we show that applications' resource requirements and execution phase behaviour can be captured in a scalable and lightweight manner at runtime by estimating important program artifacts termed as "dynamic loop characteristics". Specifically, we propose a solution to the problem of efficient workload scheduling by designing a compiler and runtime cooperative framework that leverages novel loop-based compiler analysis for resource allocation. We present Beacons Framework, an end-to-end compiler and scheduling framework, that estimates dynamic loop characteristics, encapsulates them in compiler-instrumented beacons in an application, and broadcasts them during application runtime, for proactive workload scheduling. We focus on estimating four important loop characteristics: loop trip-count, loop timing, loop memory footprint, and loop data-reuse behaviour, through a combination of compiler analysis and machine learning. The novelty of the Beacons Framework also lies in its ability to tackle irregular loops that exhibit complex control flow with indeterminate loop bounds involving structure fields, aliased variables and function calls, which are highly prevalent in modern workloads. At the backend, Beacons Framework entails a proactive workload scheduler that leverages the runtime information to orchestrate aggressive

关键词： ML-based Static analysis Loop Trip Count analysis Loop Timing analysis Loop Memory analysis Proactive Scheduling Compiler-Guided Scheduling Throughput Scheduling

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