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High Performance Computing: Isc High Performance Digital 2021 International Workshops, Frankfurt Am Main, Germany, June 24 - July 2, 2021, Revised

ISBN-13: 9783030905385 / Angielski / Miękka / 2021 / 532 str.

Jagode, Heike
High Performance Computing: Isc High Performance Digital 2021 International Workshops, Frankfurt Am Main, Germany, June 24 - July 2, 2021, Revised Jagode, Heike 9783030905385 Springer International Publishing - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

High Performance Computing: Isc High Performance Digital 2021 International Workshops, Frankfurt Am Main, Germany, June 24 - July 2, 2021, Revised

ISBN-13: 9783030905385 / Angielski / Miękka / 2021 / 532 str.

Jagode, Heike
cena 402,53
(netto: 383,36 VAT:  5%)

Najniższa cena z 30 dni: 385,52
Termin realizacji zamówienia:
ok. 22 dni roboczych.

Darmowa dostawa!

This book constitutes the refereed post-conference proceedings of 9 workshops held at the 35th International ISC High Performance 2021 Conference, in Frankfurt, Germany, in June-July 2021:Second International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics and Solid Mechanics Simulations and Analysis; HPC-IODC: HPC I/O in the Data Center Workshop; Compiler-assisted Correctness Checking and Performance Optimization for HPC; Machine Learning on HPC Systems;4th International Workshop on Interoperability of Supercomputing and Cloud Technologies;2nd International Workshop on Monitoring and Operational Data Analytics;16th Workshop on Virtualization in High­-Performance Cloud Computing; Deep Learning on Supercomputers; 5th International Workshop on In Situ Visualization. The 35 papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include high-performance computing (HPC), computer architecture and hardware, programming models, system software, performance analysis and modeling, compiler analysis and optimization techniques, software sustainability, scientific applications, deep learning. 

Kategorie:
Informatyka, Bazy danych
Kategorie BISAC:
Computers > Networking - General
Computers > Logic Design
Computers > Computer Architecture
Wydawca:
Springer International Publishing
Seria wydawnicza:
Theoretical Computer Science and General Issues
Język:
Angielski
ISBN-13:
9783030905385
Rok wydania:
2021
Ilość stron:
532
Waga:
0.73 kg
Wymiary:
23.39 x 15.6 x 2.72
Oprawa:
Miękka
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

Second International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics and Solid Mechanics Simulations and Analysis.- Machine-Learning-Based Control of Perturbed and Heated Channel Flows.- Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows.- Lettuce: PyTorch-based Lattice Boltzmann Framework.- Reservoir computing in reduced order modeling for chaotic dynamical systems.- Film cooling prediction and optimization based on deconvolution neural network.- Turbomachinery Blade Surrogate Modeling using Deep Learning.- A Data-driven Wall-shear Stress Model for LES using Gradient Boosted Decision Trees.- Nonlinear mode decomposition and reduced-order modeling for three-dimensional cylinder flow by distributed learning on Fugaku.- Using physics-informed enhanced super-resolution generative adversarial networks to reconstruct mixture fraction statistics of turbulent jet flows.- HPC I/O in the Data Center.- Toward a Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysis.- H3: An Application-Level, Low-Overhead Object Store.- Compiler-assisted Correctness Checking and Performance Optimization for HPC.- Automatic partitioning of MPI operations in MPI+OpenMP applications.- heimdallr: Improving Compile Time Correctness Checking for Message Passing with Rust.- Potential of Interpreter Specialization for Data Analysis.- Refactoring for Performance with Semantic Patching: Case Study with Recipes.- Negative Perceptions About the Applicability of Source-to-Source Compilers in HPC: A Literature Review.- Machine Learning on HPC Systems.- Automatic Tuning of Tensorflow's CPU Backend using Gradient-Free Optimization Algorithms.- MSM: Multi-Stage Multicuts for Scalable Image Clustering.- OmniOpt - a tool for hyperparameter optimization on HPC.- Parallel/distributed intelligent hyperparameters search for GANs.- Machine learning for generic energy models of high performance computing resources.- Fourth International Workshop on Interoperability of Supercomputing and Cloud Technologies.- Automation for Data-Driven Research with the NERSC Superfacility API.- A Middleware Supporting Data Movement inComplex and Software-Defined Storage andMemory Architectures.- Second International Workshop on Monitoring and Operational Data Analytics.- An Operational Data Collecting and Monitoring Platform for Fugaku: System Overviews and Case Studies in the Prelaunch Service Period.- An Explainable Model for Fault Detection in HPC Systems.- Sixteenth Workshop on Virtualization in High­-Performance Cloud Computing.- A Scalable Cloud Deployment Architecture for High-Performance Real-Time Online Interactive Applications.- Leveraging HW approximation for exploiting performance-energy trade-offs within the edge-cloud computing continuum.- Datashim and its applications in Bioinformatics.- FaaS and Curious: Performance implications of serverless functions on edge computing platforms.- Differentiated performance in NoSQL database access for hybrid Cloud-HPC workloads.- Deep Learning on Supercomputers.- JUWELS Booster - A Supercomputer for Large-Scale AI Research.- Fifth International Workshop on In Situ Visualization.- In Situ Visualization of WRF Data using Universal Data Junction.- Catalyst Revised: Rethinking the ParaView In Situ Analysis and Visualization API.- Fides: A General Purpose Data Model Library for Streaming Data.-Including in-situ visualization and analysis in PDI.-

Chapter “Machine-Learning-Based Control of Perturbed and Heated Channel Flows” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.



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