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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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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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Portable and Scalable All-Electron Quantum Perturbation Simulations on Exascale Supercomputers 23

Portable and Scalable All-Electron Quantum Perturbation Simu...

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

作者： Wu, Zhikun Wu, Yangjun Liu, Ying Shang, Honghui Gao, Yingxiang Zhang, Zhongcheng Zhang, Yuyang Long, Yingchi Feng, Xiaobing Cui, Huimin University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China National Supercomputer Center in Tianjin Tianjin China

ISBN: (纸本)9798400701092

Quantum perturbation theory is pivotal in determining the critical physical properties of materials. The first-principles computations of these properties have yielded profound and quantitative insights in diverse domains of chemistry and physics. In this work, we propose a portable and scalable OpenCL implementation for quantum perturbation theory, which can be generalized across various high-performance computing (HPC) systems. Optimal portability is realized through the utilization of a cross-platform unified interface and a collection of performance-portable heterogeneous optimizations. Exceptional scalability is attained by addressing major constraints on memory and communication, employing a locality-enhancing task mapping strategy and a packed hierarchical collective communication scheme. Experiments on two advanced supercomputers demonstrate that our implementation exhibits remarkably performance on various material systems, scaling the system to 200,000 atoms with all-electron precision. This research enables all-electron quantum perturbation simulations on substantially larger molecular scales, with a potentially significant impact on progress in material sciences. © 2023 acm.

关键词： Scalability

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FairRecKit: AWeb-based analysis Software for Recommender Evaluations 8

FairRecKit: AWeb-based Analysis Software for Recommender Eva...

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8th Conference on Human Information Interaction and Retrieval

作者： Bauer, Christine Chung, Lennard Cornelissen, Aleksej van Driessel, Isabelle van der Hoorn, Diede de Jong, Yme Le, Lan Tabriz, Sanaz Najiyan Spaans, Roderick Thijsen, Casper Verbeeten, Robert Wesseling, Vos Wieland, Fern Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9798400700354

FairRecKit is aweb-based analysis software that supports researchers in performing, analyzing, and understanding recommendation computations. The idea behind FairRecKit is to facilitate the in-depth analysis of recommendation outcomes considering fairness aspects. With (nested) filters on user or item attributes, metrics can easily be compared across user and item subgroups. Further, (nested) filters can be used on the dataset level;this way, recommendation outcomes can be compared across several sub-datasets to analyze for differences considering fairness aspects. The software currently features five datasets, 11 metrics, and 21 recommendation algorithms to be used in computational experimentation. It is open source and developed in a modular manner to facilitate extension. The analysis software consists of two components: A software package (Fair-RecKitLib) for running recommendation algorithms on the available datasets and a web-based user interface (FairRecKitApp) to start experiments, retrieve results of previous experiments, and analyze details. The application also comes with extensive documentation and options for result customization, which makes for a flexible tool that supports in-depth analysis.

关键词： resource evaluation analysis recommender systems toolkit webbased music movies

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Thorough Characterization and analysis of Large Transformer Model Training At-Scale 24

Thorough Characterization and Analysis of Large Transformer ...

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2024 acm SIGMETRICS/IFIP Performance Conference on measurement and Modeling of Computer systems, SIGMETRICS/PERFORMANCE 2024

作者： Cheng, Scott Lin, Jun-Liang Emani, Murali Raskar, Siddhisanket Foreman, Sam Xie, Zhen Vishwanath, Venkatram Kandemir, Mahmut T. The Pennsylvania State University University Park United States Argonne National Laboratory United States Binghamton University United States

ISBN: (纸本)9798400706240

Large transformer models have recently achieved great success across various domains. With a growing number of model parameters, a large transformer model training today typically involves model sharding, data parallelism, and model parallelism. Thus, the throughput of large-scale model training depends heavily on the network bandwidth since a combination of model sharding and multiple parallelism strategies incurs various costs. However, prior characterizations of transformer models on high-bandwidth DGX machines that use TFLOPS as a metric may not reflect the performance of a system with lower bandwidth. Furthermore, data and model parallelism reveal significantly distinct training profiles on different system bandwidths at scale and, thus, need a thorough study. In this paper, we provide a bottom-up breakdown of training throughput into compute and communication time, and quantitatively analyze their respective influences on overall end-To-end training scaling. Our evaluation involves an in-depth exploration of data parallelism, scaling up to 512 GPUs with limited bandwidth, and examines three model sharding strategies among six model sizes. We also evaluate three combinations of model parallelism on both high and low bandwidth supercomputing systems. Overall, our work provides a broader perspective on large-scale transformer model training, and our analysis and evaluation yield practical insights for predicting training scaling, shaping the future development of supercomputing system design. © 2024 Copyright held by the owner/author(s).

关键词： Bandwidth

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Mobile_FL : A streamlined FL framework for process optimisation via client clustering using rough c-means algorithm 10

Mobile_FL : A streamlined FL framework for process optimisat...

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10th acm Cyber-Physical System Security Workshop (acm CPSS)

作者： Nair, Akarsh K. Sahoo, Jayakrushna Cenkeramaddi, Linga Reddy Jaswal, Gaurav Raj, Ebin Deni Indian Inst Informat Technolgy Kottayam Kottayam Kerala India Univ Agder Grimstad Norway IIT Mandi IHub HCI Fdn Mandi Himachal Prades India

ISBN: (纸本)9798400704208

Currently, Federated Learning is one of the most widely accepted distributed learning frameworks for privacy-sensitive applications. Despite the popularity gained, FL frameworks struggle to perform well in terms of accuracy and model convergence for scenarios with diverse clients and data distributions. For tackling such issues, the article presents a clustered FL framework, Mobile_FL exploiting model similarities between clients to group them into clusters with collaboratively trainable private datasets. The Mobile_FL framework employs a multi-tier clustering approach with intermediate aggregation ensuring streamlined convergence and model performance compared to existing models. The intermediate servers are termed virtual servers and clustering is performed based on a strategy inspired from the classical rough c means clustering approach. The rough c means ensures computational efficiency with an effective complexity-system performance trade-off compared to complex algorithms. The intermediate layers also act as a deterrent against curious servers, enhancing both system privacy and user anonymity for clients. The framework is also ideally suited for generating personalized FL models for a subgroup of clients, especially in mobile environments with dynamic client populations. The performance of the model is verified through experiments on standard datasets and also subjected to theoretic analysis.

关键词： Federated Learning Edge computing Cyber Physical systems Mobile IoT Clustering

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