09371nam 2200493 450 99650356420331620231110234138.03-031-23821-4(MiAaPQ)EBC7161277(Au-PeEL)EBL7161277(CKB)25793102900041(EXLCZ)992579310290004120230421d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierHigh performance computing 9th Latin American conference, CARLA 2022, Porto Alegre, Brazil, September 26-30, 2022 : revised selected papers /edited by Philippe Navaux [and three others]Cham, Switzerland :Springer,[2022]©20221 online resource (246 pages)Communications in Computer and Information Science ;v.1660Print version: Navaux, Philippe High Performance Computing Cham : Springer International Publishing AG,c2022 9783031238208 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- A Comparative Evaluation of Parallel Programming Python Tools for Particle-in-Cell on Symmetric Multiprocessors -- 1 Introduction -- 2 Background -- 2.1 Particle-in-Cell -- 2.2 Python Parallel Programming -- 2.3 Related Work -- 3 Implementation -- 3.1 Profiling -- 3.2 Code Transformation -- 4 Experimental Results -- 4.1 Setup -- 4.2 Experiments -- 5 Discussion -- 6 Final Remarks -- References -- Accelerating GNN Training on CPU+Multi-FPGA Heterogeneous Platform -- 1 Introduction -- 2 Background -- 2.1 GNN Models -- 2.2 Mini-Batch GNN Training -- 2.3 Related Work -- 3 GNN Training on CPU+Multi-FPGA Platform -- 4 Optimizations -- 4.1 Graph Partitioning and Workload Balancing -- 4.2 Optimized GNN Kernels -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Hardware Parameter Selection and Resource Utilization -- 5.3 Performance Metrics -- 5.4 Comparison with Multi-GPU Platform -- 5.5 Scalability -- 5.6 Impact of Optimizations -- 6 Conclusion -- References -- Implementing a GPU-Portable Field Line Tracing Application with OpenMP Offload -- 1 Introduction -- 2 Background -- 2.1 Directive-Based Programming for Accelerators with OpenMP -- 2.2 Simulating Plasma Confinement in Stellarator Devices -- 2.3 Related Work -- 3 Directive-Based GPU Offloading Implementation -- 3.1 Breakdown of the Execution Flow -- 3.2 Data Management for Offloading -- 3.3 Parallelism Implementation -- 4 Results -- 4.1 Experimental Setup -- 4.2 Baseline Comparison: Single CPU Node Versus Single GPU -- 4.3 Multi-GPU Scalability -- 4.4 Economic Analysis -- 5 Conclusions -- References -- Quantitative Characterization of Scientific Computing Clusters -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Cluster Overhead and Coupling -- 3.2 Cluster Performance Profile -- 4 Performance Evaluation -- 4.1 Experimental Setup.4.2 Threats to Validity -- 4.3 Results -- 4.4 Clusters Performance Profiles -- 5 Discussion -- 6 Conclusion -- References -- Towards Parameter-Based Profiling for MARE2DEM Performance Modeling -- 1 Introduction -- 2 Dataset and Application Background -- 2.1 CSEM Data -- 2.2 MARE2DEM -- 2.3 Refinement Groups -- 3 Methodology and Experimental Context -- 4 Results -- 4.1 Performance Characterization of the Microkernels -- 4.2 Iterations and Refinement Groups -- 5 Conclusion -- References -- Time-Power-Energy Balance of BLAS Kernels in Modern FPGAs -- 1 Introduction -- 2 FPGAs and NLA -- 2.1 BLAS -- 2.2 FPGAs -- 3 Evaluated Kernels -- 3.1 Vitis Libraries -- 3.2 Matrix-Matrix Multiplication (MMM) -- 4 Experimental Evaluation -- 4.1 Setup -- 4.2 Experimental Results and Discussion -- 5 Conclusions -- References -- Improving Boundary Layer Predictions Using Parametric Physics-Aware Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Boundary Layer Problem -- 3.2 Architecture Design -- 4 Experimental Results -- 4.1 First Setting: Reaction-Diffusion Problem -- 4.2 Second Setting: Advection-Diffusion Problem -- 5 Summary and Outlook -- References -- Towards Fire Identification Model in Satellite Images Using HPC Embedded Systems and AI -- 1 Introduction -- 2 Related Works -- 2.1 Satellite Imagery Multiscale Rapid Detection With Windowed Networks -- 2.2 Lapped Convolutional Neural Networks for Embedded Systems -- 3 Workflow -- 3.1 Dataset Elaboration -- 3.2 Algorithm Selection -- 4 Results -- 4.1 Artificial Learning -- 4.2 Evaluation Metrics -- 5 Conclusion -- 6 Future Work -- References -- A Machine Learning-Based Missing Data Imputation with FHIR Interoperability Approach in Sepsis Prediction -- 1 Introduction -- 2 State of the Art -- 2.1 Machine Learning on Clinical Features for Sepsis Prediction.2.2 Interoperability of Healthcare Information Systems -- 3 Materials and Methods -- 3.1 Study Design -- 3.2 Dataset Early Prediction of Sepsis from Clinical Data -- 3.3 Processing and Transformation of Clinical Data to the FHIR Standard -- 3.4 Data Distribution - Hospitals A and B -- 3.5 Preprocessing of Data -- 3.6 Experiment Dataset -- 3.7 Creation of Train Test -- 3.8 Implementation of Classifiers -- 4 Experiments and Results -- 4.1 Experiment Results -- 5 Conclusions -- References -- Understanding the Energy Consumption of HPC Scale Artificial Intelligence -- 1 Introduction -- 2 Related Work -- 2.1 AI and Climate Change -- 2.2 Energy-Aware AI -- 2.3 AI Benchmarks -- 2.4 Energy Measurement Tools -- 2.5 Positioning of This Paper -- 3 Background -- 4 Benchmark Tracker -- 5 Results -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusion and Future Work -- 6.1 Future Work -- References -- Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 General System Architecture -- 4 Experiments -- 5 Results -- 6 Conclusions -- References -- Multi-GPU 3-D Reverse Time Migration with Minimum I/O -- 1 Introduction -- 2 Reverse Time Migration -- 3 Computational Implementation and Optimizations -- 3.1 Classical Reverse Time Migration -- 3.2 Reverse Time Migration with Wavefield Reconstruction -- 3.3 Hybrid OpenACC/MPI Implementation -- 4 Numerical Experiments -- 5 Conclusions -- References -- ParslRNA-Seq: An Efficient and Scalable RNAseq Analysis Workflow for Studies of Differentiated Gene Expression -- 1 Introduction -- 2 Related Works -- 3 Background on Differential Gene Expression Analysis -- 4 ParslRNA-Seq: Workflow for DGE Analysis -- 4.1 Improvements in the Previous Implementation of the Workflow.4.2 Multithreading and Multiprocessing -- 4.3 The Current Implementation of the ParslRNA-Seq Workflow -- 5 Methods and Infrastructure -- 5.1 Experiment Dataset -- 5.2 Experiment Setup -- 5.3 Computational Environment Setup -- 6 Experimental Results -- 6.1 Performance and Scalability Analyses -- 6.2 I/O Performance Results Using Darshan -- 6.3 Performance Results Using SSD -- 6.4 Biological Results of RNA-Seq Data -- 7 Conclusion -- References -- Refactoring an Electric-Market Simulation Software for Massively Parallel Computations -- 1 Introduction -- 2 The SimSEE and Previous Results -- 3 Proposal -- 3.1 Loading the Playrooms for Massively-Parallel Trajectories, naive -- 3.2 Improving the Playrooms Replication, base -- 3.3 Sharing References to Avoid Memory Allocations, RefCat -- 3.4 Enhancing the Access to Shared References in the Simulation, RefDicc -- 4 Experimental Evaluation -- 4.1 Test Cases -- 4.2 Runtime Environment -- 4.3 Experimental Results -- 5 Conclusion and Future Work -- References -- Nearly Quantum Computing by Simulation -- 1 Introduction -- 2 Quantum Computing Modelling -- 2.1 An Overview of Quantum Mechanics -- 2.2 Information Theory -- 2.3 Quantum Information Theory -- 3 Quantum Computing Parallelism and Simulation -- 3.1 Quantum Computing Simulators -- 3.2 Popular Open Source Quantum Computer Simulators -- 4 Discussion and Further Work -- References -- Functionality Testing in the Automation of Scientific Application Workflows in an HPC Environment -- 1 Introduction -- 2 Infrastructure Used -- 3 Tools Used -- 3.1 Slurm -- 3.2 Singularity -- 3.3 Snakemake -- 4 Design of the Processing Flow for Testing -- 5 Analysis of Possible Cases -- 5.1 Running Python Script -- 5.2 Running Python Script with SLURM and Singularity -- 5.3 Running a Singularity Container with Snakemake Using SLURM -- 5.4 Notes for Tables 2, 3 and 4.6 Results of Executions -- 6.1 Case 1: -- 6.2 Case 2: -- 6.3 Case 3: -- 7 Discussion of Results and Conclusions -- 7.1 Testing Time -- 7.2 Duration of Tests -- 7.3 What Limitations There Were -- 7.4 Learning -- 7.5 Conclusions and Benefits -- References -- Author Index.Communications in Computer and Information Science High performance computingHigh performance computingCongressesHigh performance computing.High performance computing004.11Navaux PhilippeMiAaPQMiAaPQMiAaPQBOOK996503564203316High Performance Computing3000244UNISA03330nam 22006495 450 991043790520332120251230065222.09783642371370364237137X10.1007/978-3-642-37137-0(CKB)3360000000455775(SSID)ssj0000878404(PQKBManifestationID)11469121(PQKBTitleCode)TC0000878404(PQKBWorkID)10815500(PQKB)11328571(DE-He213)978-3-642-37137-0(MiAaPQ)EBC3091921(PPN)258849932(PPN)168330563(EXLCZ)99336000000045577520130228d2013 u| 0engurnn|008mamaatxtccrAdvances in Biomedical Infrastructure 2013 Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013) /edited by Amandeep S. Sidhu, Sarinder K. Dhillon1st ed. 2013.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2013.1 online resource (VIII, 139 p. 51 illus.) Data, Semantics and Cloud Computing,2524-6607 ;477Bibliographic Level Mode of Issuance: Monograph9783642371363 3642371361 Includes bibliographical references and author index.From the Contents: Integrative approaches for Drug discovery – PPAR gamma as a case study -- Biomedical Informatics and the Future of Medicine -- Inferring E. coli SOS Response Pathway from Gene Expression Data Using IST-DBN with Time Lag Estimation -- Framework for Biodiversity Information Retrieval in Malaysia -- Using Ant Colony Optimization (ACO) on Kinetic Modeling of the Acetoin Production in Lactococcus lactis C7.Current Biomedical Databases are independently administered in geographically distinct locations, lending them almost ideally to adoption of intelligent data management approaches. This book focuses on research issues, problems and opportunities in Biomedical Data Infrastructure identifying new issues and directions for future research in Biomedical Data and Information Retrieval, Semantics in Biomedicine, and Biomedical Data Modeling and Analysis. The book will be a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development in biomedical data management.Data, Semantics and Cloud Computing,2524-6607 ;477Computational intelligenceBiomedical engineeringComputational IntelligenceBiomedical Engineering and BioengineeringComputational intelligence.Biomedical engineering.Computational Intelligence.Biomedical Engineering and Bioengineering.610.28Sidhu Amandeep Sedthttp://id.loc.gov/vocabulary/relators/edtDhillon Sarinder Kedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910437905203321Advances in Biomedical Infrastructure 20132539329UNINA