標題: Titlebook: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI; 17th Smoky Mountains Jeffrey Nichols,Becky [打印本頁] 作者: 螺絲刀 時間: 2025-3-21 19:19
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI影響因子(影響力)
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI影響因子(影響力)學科排名
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI網(wǎng)絡(luò)公開度
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI網(wǎng)絡(luò)公開度學科排名
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI被引頻次
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI被引頻次學科排名
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI年度引用
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI年度引用學科排名
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI讀者反饋
書目名稱Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI讀者反饋學科排名
作者: 盤旋 時間: 2025-3-21 22:29
Large-Scale Neural Solvers for Partial Differential EquationsHowever, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. Scanning the parameters of the underlying model significantly increases the runtime as the simulations have to be cold-started for each parame作者: 不幸的人 時間: 2025-3-22 01:43
Integrating Deep Learning in Domain Sciences at Exascalerformance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogene作者: antidepressant 時間: 2025-3-22 06:37 作者: 敵手 時間: 2025-3-22 11:18 作者: adequate-intake 時間: 2025-3-22 15:32 作者: adequate-intake 時間: 2025-3-22 19:05
Fulfilling the Promises of Lossy Compression for Scientific Applicationssion has been identified as one solution and has been tested for many use-cases: reducing streaming intensity (instruments), reducing storage and memory footprints, accelerating computation and accelerating data access and transfer. Ultimately, users’ trust in lossy compression relies on the preserv作者: Texture 時間: 2025-3-22 23:13
DataStates: Towards Lightweight Data Models for Deep Learningarge number of alternative training and/or inference paths. However, with increasing model complexity and new training approaches that mix data, model, pipeline and layer-wise parallelism, this pattern is challenging to address in a scalable and efficient manner. To this end, this position paper adv作者: 難解 時間: 2025-3-23 02:26 作者: deciduous 時間: 2025-3-23 08:25 作者: 飲料 時間: 2025-3-23 13:33 作者: 指令 時間: 2025-3-23 14:34 作者: 外貌 時間: 2025-3-23 19:53 作者: 籠子 時間: 2025-3-24 00:26
Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysirvations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision 作者: corporate 時間: 2025-3-24 05:17 作者: 惰性氣體 時間: 2025-3-24 06:47
Unsupervised Anomaly Detection in Daily WAN Traffic Patternss of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic ana作者: 一小塊 時間: 2025-3-24 12:26
1865-0929 tation: on the road to a converged ecosystem;?scientific data challenges..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-63392-9978-3-030-63393-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: AGOG 時間: 2025-3-24 17:29 作者: 牌帶來 時間: 2025-3-24 22:03
Christoph H?nnige,Sascha Kneip,Astrid Lorenzdata pipelines from multiple months of network flow records. Once trained, individual classifiers quickly observe and flag alerts in hourly behaviors. Our work describes building the data pipeline as well as addressing issues of false positives and workflow integration.作者: 只有 時間: 2025-3-24 23:12
Performance Improvements on SNS and HFIR Instrument Data Reduction Workflows Using Mantidduction workflows. We propose a more disruptive domain-specific solution: the No Cost Input Output (NCIO) framework, we provide an overview, the risks and challenges in NCIO’s adoption by HFIR and SNS stakeholders.作者: Vertebra 時間: 2025-3-25 06:40 作者: 貪婪的人 時間: 2025-3-25 07:57
1865-0929 eld in Oak Ridge, TN, USA*, in August 2020..The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of?computational applications: converged HPC and artificial intelligence; system software: data作者: Dendritic-Cells 時間: 2025-3-25 13:57 作者: 菊花 時間: 2025-3-25 16:26
Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows ration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.作者: 卜聞 時間: 2025-3-25 22:48 作者: 意見一致 時間: 2025-3-26 04:05 作者: 裙帶關(guān)系 時間: 2025-3-26 04:59
Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI978-3-030-63393-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 騷擾 時間: 2025-3-26 11:47
https://doi.org/10.1007/978-3-030-63393-6artificial intelligence; cloud computing; computer hardware; computer networks; computer systems; compute作者: detach 時間: 2025-3-26 16:11
978-3-030-63392-9Springer Nature Switzerland AG 2020作者: 職業(yè)拳擊手 時間: 2025-3-26 20:14
https://doi.org/10.1007/978-3-531-92916-3ion using the finite difference, the finite element methods and spectral element methods to solve the wave equations numerically. The paper presents a new method to improve the performance of the seismic wave simulation and inversion by integrating the deep learning software platform and deep learni作者: 埋伏 時間: 2025-3-26 22:45
Die Rede von der Vertrauenskrise in ChinaHowever, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. Scanning the parameters of the underlying model significantly increases the runtime as the simulations have to be cold-started for each parame作者: 植物群 時間: 2025-3-27 01:34
Die Rede von der Vertrauenskrise in Chinarformance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogene作者: NEEDY 時間: 2025-3-27 06:02 作者: 滔滔不絕地講 時間: 2025-3-27 09:46 作者: 苦笑 時間: 2025-3-27 17:19
Gerhard Leitner,Rudolf Melcher,Martin Hitzy, the unavailability of equally advanced data management infrastructure has led to ad hoc practices that diminish scientific productivity and exacerbate the reproducibility crisis. We discuss a system-wide solution that supports management needs at every stage of the data lifecycle. At the center o作者: 議程 時間: 2025-3-27 19:52 作者: 不適 時間: 2025-3-27 23:58 作者: 縱火 時間: 2025-3-28 02:31 作者: 善于騙人 時間: 2025-3-28 10:06 作者: 剝削 時間: 2025-3-28 11:54
Kai-Uwe Hellmann,Thorsten Raabebservational facilities. The use of data across the entire lifetime ranging from real-time to post-hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. Over time, three sets of tools have emerged: one set for analysis, another for visual作者: eucalyptus 時間: 2025-3-28 15:29 作者: Melanoma 時間: 2025-3-28 20:46 作者: 全部逛商店 時間: 2025-3-29 01:59
Bettina Petersohn,Rainer-Olaf Schultzervations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision 作者: IDEAS 時間: 2025-3-29 04:43 作者: Handedness 時間: 2025-3-29 07:54
Christoph H?nnige,Sascha Kneip,Astrid Lorenzs of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic ana作者: 轉(zhuǎn)向 時間: 2025-3-29 13:47 作者: follicular-unit 時間: 2025-3-29 18:25
Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI17th Smoky Mountains作者: 喃喃訴苦 時間: 2025-3-29 21:04 作者: 返老還童 時間: 2025-3-30 03:56 作者: 摘要 時間: 2025-3-30 07:04
Integrating Deep Learning in Domain Sciences at Exascalen MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization m作者: 初次登臺 時間: 2025-3-30 10:13
Improving the Performance of the GMRES Method Using Mixed-Precision Techniquesram-Schmidt (MGS) and Classical Gram-Schmidt with Re-orthogonalization (CGSR). Furthermore, our mixed-precision GMRES, when restarted at least once, performed 19% and 24% faster on average than double-precision GMRES for MGS and CGSR, respectively. Our implementation uses generic programming techniq作者: 小丑 時間: 2025-3-30 15:12
On the Use of BLAS Libraries in Modern Scientific Codes at Scaleave additionally traced the training stage of the Convolutional Neural Network (CNN), AlexNet [.]. HPLinpack is also included as a reference, as it exhibits a well-understood BLAS usage pattern. Results from across all the codes show that, unlike HPLinpack, BLAS usage is never more than 25% of the t作者: CANE 時間: 2025-3-30 19:36
A Systemic Approach to Facilitating Reproducibility via Federated, End-to-End Data Managemente integrated into analytics platforms to easily, correctly, and reliably work with datasets to improve reproducibility of such workloads. We believe that this system can significantly alleviate the burden of data management and improve compliance with the Findable Accessible Interoperable, Reusable 作者: 護身符 時間: 2025-3-31 00:10
Fulfilling the Promises of Lossy Compression for Scientific Applicationsand accuracy, and current generic monolithic compressors are not responding well to this need for specialization. This situation calls for more research and development on the lossy compression technologies. This paper addresses the most pressing research needs regarding the application of lossy com作者: 制定法律 時間: 2025-3-31 03:29
DataStates: Towards Lightweight Data Models for Deep Learning metadata that expresses attributes and persistency/movement semantics. A high-performance runtime is the responsible to interpret the metadata and perform the necessary actions in the background, while offering a rich interface to find data states of interest. Using this approach has benefits at se作者: orthopedist 時間: 2025-3-31 07:21
Scalable Data-Intensive Geocomputation: A Design for Real-Time Continental Flood Inundation Mappingntify and eliminate computational bottlenecks that arise in a geocomputation workflow. Indeed, poor scalability at any of the workflow components is detrimental to the entire end-to-end pipeline. Here, we study a large geocomputation use case in flood inundation mapping that handles multiple nationa作者: Gesture 時間: 2025-3-31 09:11
Enabling Scientific Discovery at Next-Generation Light Sources with Advanced AI and HPCs will generate in the exabyte (EB) range of data, require tens to 1,000 PFLOPS of peak on-demand computing resources, and utilize billions of core hours per year. Scientific discovery on this scale will be enabled by data management and workflow tools that integrate user facility instruments with s作者: 補助 時間: 2025-3-31 14:09
Visualization as a Service for Scientific Datae interoperability for intelligent scheduling of workflow systems. This work results from a codesign process over analysis, visualization, and workflow tools to provide the flexibility required for production use. Finally, this paper describes a forward-looking research and development plan that cen作者: 無畏 時間: 2025-3-31 17:48 作者: 鳥籠 時間: 2025-4-1 01:23 作者: Instinctive 時間: 2025-4-1 04:56 作者: commute 時間: 2025-4-1 07:16
Die Rede von der Vertrauenskrise in Chinaitial/boundary values and validation points for training but no simulation data. The induced curse of dimensionality is approached by learning a domain decomposition that steers the number of neurons per unit volume and significantly improves runtime. Distributed training on large-scale cluster syst作者: 可能性 時間: 2025-4-1 12:59
