Parallel Processing and Applied Mathematics

14th International Conference, PPAM 2022, Gdansk, Poland, September 11–14, 2022, Revised Selected Papers, Part I

AvRoman Wyrzykowski,Jack Dongarra

Häftad, Engelska, 2023

Del 13826 i serien Lecture Notes in Computer Science

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Beskrivning

This two-volume set, LNCS 13826 and LNCS 13827, constitutes the proceedings of the 14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022, held in Gdansk, Poland, in September 2022.The 77 regular papers presented in these volumes were selected from 132 submissions.

Produktinformation

Utgivningsdatum:2023-04-28
Mått:155 x 235 x 28 mm
Vikt:756 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:480
Förlag:Springer International Publishing AG
ISBN:9783031304415

Utforska kategorier

Innehållsförteckning

Numerical Algorithms and Parallel Scientific Computing.- How accurate does Newton have to be?.- General framework for deriving reproducible Krylov subspace algorithms: BiCGStab case.- A generalized parallel prefix sums algorithm for arbitrary size array.- Infinite-Precision Inner Product and Sparse Matrix-Vector Multiplication Using Ozaki Scheme with Dot2 on Manycore Processors.- Advanced Stochastic Approaches for Applied Computing in Environmental Modeling.- Parallel Non-numerical Algorithms.- Parallel Suffix Sorting for Large String Analytics.- Parallel Extremely Randomized Decision Forests on Graphics Processors for Text Classification.- RDBMS speculative support improvement by the use of the query hypergraph representation.- GPU Computing.- Mixed Precision Algebraic Multigrid on GPUs.- Compact in-memory representation of decision trees in GPU-accelerated evolutionary induction.- Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs.- Performance Analysis and Prediction in HPC Systems.- Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications.- Cost and Performance Analysis of MPI-based SaaS on the Private Cloud Infrastructure.- Building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations.- Evaluation of machine learning techniques for predicting run times of scientific workflow jobs.- Smart clustering of HPC applications using similar job detection methods.- Scheduling for Parallel Computing.- Distributed Work Stealing in a Task-Based Dataflow Runtime.- Task Scheduler for Heterogeneous Data Centres based on Deep Reinforcement Learning.- Shisha: Online scheduling of CNN pipelines on heterogeneous architectures.- Proactive Task Offloading for Load Balancing in Iterative Applications.- Environments and Frameworks for Parallel/Cloud Computing.- Language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices.- High Performance Dataframes from Parallel Processing Patterns.- Global Access to Legacy Data-Sets in Multi-Cloud Applications with Onedata.- Applications of Parallel and Distributed Computing.- MD-Bench: A generic proxy-app toolbox for state-of-the-art molecular dynamics algorithms.- Breaking Down the Parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software.- GPU-based Molecular Dynamics of Turbulent Liquid Flows with OpenMM.- A novel parallel approach for modeling the dynamics of aerodynamically interacting particles in turbulent flows.- Reliable energy measurement on heterogeneous Systems–on–Chip based environments.- Distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans.- Soft Computing with Applications.- GPU4SNN: GPU-based Acceleration for Spiking Neural Network Simulations.- Ant System Inspired Heuristic Optimization of UAVs Deployment for k-Coverage Problem.- Dataset related experimental investigation of chess position evaluation using a deep neural network.- Using AI-based edge processing in monitoring the pedestrian crossing.- Special Session on Parallel EVD/SVD and its Application in Matrix Computations.- Automatic code selection for the dense symmetric generalized eigenvalue problem using ATMathCoreLib.- On Relative Accuracy of the One-Sided Block-Jacobi SVD Algorithm.