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AMGSRN++: Improved Adaptive SRN for Scientific visualization

AMGSRN++: Improved Adaptive SRN for Scientific Visualization

引用

Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Skylar W. Wurster Han-Wei Shen Ohio State University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

We present AMGSRN++, which advances previous state of the art APMGSRN along three key directions. First, we implement efficient CUDA kernels to fuse the encoding operation into a single kernel, reducing VRAM requirement by over $50 \%$ improving throughput, enabling faster training and rendering with the availability for lower-end hardware to perform neural volume rendering efficiently. Second, we introduce a compression-aware training strategy for efficient feature grid compression when saving, reducing storage costs by $80 \%$. Lastly, we extend the method to time-varying data with a 3D+time approach, allowing parallel training with no dependence between timesteps for highly efficient model fitting. We extend the previously released rendering tool to support the new model, including seamless time-varying dataset visualization. As a result, time-varying datasets over 100 GB can be rendered in real time on consumer hardware with as little as 1 GB of VRAM, and using only 88 MB of storage space. Comparisons with state of the art compressors and other SRNs are provided, displaying continued strong representation capability and higher compressive capabilities. All code is released publicly at https://***/skywolf829/AMGSRN.

关键词： Training Human computer interaction Fuses Fitting graphics processing units Rendering (computer graphics) Throughput Hardware Real-time systems Kernel

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Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts 35

Adaptive Spatially Aware I/O for Multiresolution Particle Da...

引用

35th ieee International parallel and Distributed Processing symposium (IPDPS)

作者： Usher, Will Huang, Xuan Petruzza, Steve Kumar, Sidharth Slattery, Stuart R. Reeve, Sam T. Wang, Feng Johnson, Chris R. Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA Univ Alabama Birmingham Birmingham AL USA Oak Ridge Natl Lab Oak Ridge TN USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781665440660

Large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5v faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

关键词： parallel I/O Load Balancing

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Processing Time Evaluation of the Classification Step in the Adaptive Loop Filter of VVC under Multiple Programming Paradigms

Processing Time Evaluation of the Classification Step in the...

引用

ieee Latin American symposium on Circuits and Systems (LASCAS)

作者： Vitória Fabricio Iago Storch Daniel Palomino Video Technology Research Group Federal University of Pelotas Pelotas RS Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

Several tools were introduced by the Versatile Video Coding (VVC) standard to enhance compression, with the Adaptive Loop Filter (ALF) being one such tool that significantly enhances visual quality. Although it provides coding efficiency gains, the ALF also poses a substantial computational burden. To address this issue, this paper evaluates the processing time of the classification step in the ALF process of VVC encoders considering different programming paradigms. A sequential CPU implementation, a Single Instruction Multiple Data implementation, and a customized parallel implementation using CUDA to be executed in GPUs. The results showed that SIMD-optimized implementation significantly outperforms the fully-scalar implementation. Although the GPU paradigm is faster than fully-scalar, it remains slower than SIMD-optimized due to CPU-GPU communication overhead. With more tasks, the GPU could potentially surpass the SIMD-optimized processing time.

关键词： Video coding visualization Single instruction multiple data graphics processing units Adaptive filters Programming Encoding Central Processing Unit Computational efficiency Standards

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Scaling Up Study Area Size in Flood Susceptibility Mapping

Scaling Up Study Area Size in Flood Susceptibility Mapping

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Aswathi Mundayatt Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Flood susceptibility mapping (FSM) is crucial for preparedness and risk mitigation of floods, and machine learning (ML)-based FSM methods have been well-studied. However, due to computational constraints, geographical variations, and differential data availability, FSM is often confined to smaller regions, much less than 1,000 km 2 . To address the computational constraints of FSM generation in data-rich regions, we propose a data-parallel approach using PySpark with two levels of spatial partitioning, where they address the computational load and geographical variations, respectively. In our case study of the state of Kerala, India, we propose grid lattice partitioning and the Ecologically Sensitive Zone (ESZ) map for the two levels of spatial partitioning, respectively. The latter is used to train random forest classifier models. We investigate the influence of contiguity in training grids on FSM outcomes within different ESZs. Our quantitative and qualitative comparative analysis of observations demonstrate the importance of contiguous training grids, especially in capturing finer details in FSM. Our implementation demonstrates the efficiency of our approach in the study area of Kerala spanning 38,863 km 2 .

关键词： Training Accuracy Computational modeling Biological system modeling Scalability Lattices Computational efficiency

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Unveiling the Dynamics of Video Out-of-Focus Blur: A Paradigm of Realistic Simulation and Novel Evaluation

Unveiling the Dynamics of Video Out-of-Focus Blur: A Paradig...

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International symposium on parallel and Distributed Processing with Applications, ISPA

作者： Jing Chen Zhao Qiu Zhiwei Zheng School of Computer Science and Technology Hainan University Haikou China School of Hotel Management China University of Labor Relations Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Video serves as the paramount medium through which humanity perceives the world via ocular faculties, spurring the development of myriad algorithms aimed at replicating the verisimilitude of the tangible realm. Among the numerous visual artifacts, out-of-focus blur remains a ubiquitous challenge, particularly prevalent in handheld camera cinematography. However, despite its common occurrence, the academic discourse on the underlying mechanisms of focal variance in video compositions is notably sparse, with a marked absence of publicly available datasets addressing real-world out-of-focus blur. In this study, we not only elucidate the phenomenon of video out-of-focus blur but also dissect the intrinsic dynamics governing focal fluctuations within authentic video sequences. We propose a novel framework for out-of-focus video simulation that faithfully replicates the nuanced characteristics of real-world scenarios. This framework involves segmenting a multitude of frames into discrete intervals through strategic keyframe selection, uniformly distributing focal planes across intervals, and meticulously rendering frames via a single-frame rendering methodology. Moreover, to evaluate the extent of out-of-focus blur in videos, we introduce an innovative metric that operates independently of ground truth data, thereby offering enhanced reliability compared to existing benchmarks.

关键词： Measurement visualization Computer vision Technological innovation Humanities Fluctuations Video sequences Cinematography Media Rendering (computer graphics)

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AutoMP: A Tool to Automate Performance Testing for Model Placement on GPUs

AutoMP: A Tool to Automate Performance Testing for Model Pla...

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International symposium on parallel and Distributed Processing with Applications, ISPA

作者： Zhe Yang Wei He Fenglong Cai Yonghui Xu Wei Guo Lizhen Cui School of Software Shandong University Jinan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

As AI applications are widely deployed in various fields, the computing cost of AI is skyrocketing. The high cost not only puts pressure on the environment but also poses challenges for researchers entering the field of deep learning. Green AI is gradually gaining attention, aiming to reduce computing costs and make AI application deployment more efficient and environmentally friendly. However, due to the wide variety of deep learning models, there are many challenges in efficiently deploying multiple models on GPUs. An unreasonable model deployment strategy will lead to insufficient utilization of GPU computing resources. We address some of these challenges through AutoMP, a tool that automates performance testing for model placement on GPUs. Through AutoMP, researchers can flexibly initiate a large number of experiments to study which models are suitable for inference tasks on the same GPU, thereby making GPU utilization more efficient. AutoMP provides a user-friendly visual interface and an API to meet users’ needs in different scenarios. AutoMP also provides a complete experimental analysis tool that generates visual charts of experimental data from multiple dimensions and gives experimental conclusions to assist researchers in making decisions. To date, AutoMP has been used by a large number of users for empirical research on deep learning model placement. The cumulative number of experiments has exceeded 10,000. The flexibility and extensibility of AutoMP and our own experience using it show that this tool plays a vital role in promoting Green AI.

关键词： Deep learning visualization Distributed processing Costs Computational modeling Green products graphics processing units Extensibility Artificial intelligence Testing

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Hypergraph data analysis with PAOHVis 29

Hypergraph data analysis with PAOHVis

引用

29th Italian symposium on Advanced Database Systems, SEBD 2021

作者： Buono, Paolo Ceriani, Miguel Costabile, Maria University of Bari via Orabona 4 Bari70125 Italy

Many data analysis activities exploit graphs to model complex relationships among data. Very often such relationships are better described using hypergraphs, whose edges can connect more than two nodes. Hypergraphs are suitable to model networks of business partners or co-authorship networks with multiple authors per article. This paper is about a technique that visualizes dynamic hypergraphs called PAOH (parallel Aggregated ordered Hypergraph). It represents vertices as parallel horizontal bars and hyperedges as parallel vertical lines and shows the evolution of hypergraphs representing them in discrete Time Slots. PAOH is described in details in an article published in 2021 in ieee Transaction on visualization and Computer graphics. It has been applied in several domains, such as ego-networks, co-authorship, as well as digital humanities, it is well-suited for medium-sized hypergraphs (50-500 vertices). PAOHVis is a tool, available online, that implements this technique. In this paper we briefly describe the tool and show the application of PAOH to the soccer domain, to support the analysis of players’ contributions to the goals of a team. © 2021 Copyright for this paper by its authors.

关键词： Data visualization

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ieee Pacific visualization symposium

IEEE Pacific Visualization Symposium

引用

13th ieee Pacific visualization symposium, PacificVis 2020

ISBN: (纸本)9781728156972

The proceedings contain 30 papers. The topics discussed include: towards rigorously designed preference visualizations for group decision making;a study of mental maps in immersive network visualization;quality metrics for symmetric graph drawings;BatchLayout: a batch-parallel force-directed graph layout algorithm in shared memory;ExplainExplore: visual exploration of machine learning explanations.;interactive attention model explorer for natural language processing tasks with unbalanced data sizes;visual interpretation of recurrent neural network on multi-dimensional time-series forecast;visualization of unsteady flow using heat kernel signatures;and tensor spines - a hyperstreamlines variant suitable for indefinite symmetric second-order tensors.

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Moving from Composable to Programmable

Moving from Composable to Programmable

引用

ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Zhongyi Chen Luc Renambot Lance Long Maxine Brown Andrew E. Johnson Electronic Visualization Lab Computer Science Univ Illinois Chicago Chicago Illinois USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

In today's Big Data era, data scientists require modern workflows to quickly analyze large-scale datasets using complex codes to maintain the rate of scientific progress. These scientists often rely on available campus resources or off-the-shelf computational systems for their applications. Unified infrastructure or over-provisioned servers can quickly become bottlenecks for specific tasks, wasting time and resources. Composable infrastructure helps solve these problems by providing users with new ways to increase resource utilization. Composable infrastructure disaggregates a computer's components - CPU, GPU (accelerators), storage and networking - into fluid pools of resources, but typically relies upon infrastructure engineers to architect individual machines. Infrastructure is either managed with specialized command-line utilities, user interfaces or specification files. These management models are cumbersome and difficult to incorporate into data-science workflows. We developed a high-level software API, Composastructure, which, when integrated into modern workflows, can be used by infrastructure engineers as well as data scientists to reorganize composable resources on demand. Composastructure enables infrastructures to be programmable, secure, persistent and reproducible. Our API composes machines, frees resources, supports multi-rack operations, and includes a Python module for Jupyter Notebooks.

关键词： Symbiosis Codes Shape graphics processing units Ethernet User interfaces Software

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Composable Infrastructures for an Academic Research Environment: Lessons Learned

Composable Infrastructures for an Academic Research Environm...

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Lance Long Timothy Bargo Luc Renambot Maxine Brown Andrew E. Johnson Electronic Visualization Lab Computer Science Univ Illinois Chicago Chicago Illinois USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Composable infrastructure holds the promise of accelerating the pace of academic research and discovery by enabling researchers to tailor the resources of a machine (e.g., GPUs, storage, NICs), on-demand, to address application needs. We were first introduced to composable infrastructure in 2018, and at the same time, there was growing demand among our College of Engineering faculty for GPU systems for data science, artificial intelligence / machine learning / deep learning, and visualization. Many purchased their own individual desktop or deskside systems, a few pursued more costly cloud and HPC solutions, and others looked to the College or campus computer center for GPU resources which, at the time, were scarce. After surveying the diverse needs of our faculty and studying product offerings by a few nascent startups in the composable infrastructure sector, we applied for and received a grant from the National Science Foundation in November 2019 to purchase a mid-scale system, configured to our specifications, for use by faculty and students for research and research training. This paper describes our composable infrastructure solution and implementation for our academic community. Given how modern workflows are progressively moving to containers and cloud frameworks (using Kubernetes) and to programming notebooks (primarily Jupyter), both for ease of use and for ensuring reproducible experiments, we initially adapted these tools for our system. We have since made it simpler to use our system, and now provide our users with a public facing JupyterHub server. We also added an expansion chassis to our system to enable composable co-location, which is a shared central architecture in which our researchers can insert and integrate specialized resources (GPUs, accelerators, networking cards, etc.) needed for their research. In February 2020, installation of our system was finalized and made operational and we began providing access to faculty in the College of Engineer

关键词： Training Industries Codes graphics processing units Metals Containers Writing

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