top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
High Performance Computing [[electronic resource] ] : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers / / edited by Michèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
High Performance Computing [[electronic resource] ] : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers / / edited by Michèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXV, 659 p. 402 illus., 239 illus. in color.)
Disciplina 004.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Computer engineering
Computer networks
Software engineering
Computers
Computer Engineering and Networks
Software Engineering
Computer Hardware
Computing Milieux
ISBN 3-030-34356-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Short Papers -- Preface to the First International Workshop on Legacy Software Refactoring for Performance -- P^3MA Workshop 2019 -- 4th International Workshop on In Situ Visualization (WOIV'19) -- Contents -- On the Use of Kernel Bypass Mechanisms for High-Performance Inter-container Communications -- 1 Introduction -- 2 Overview of Compared Solutions -- 3 Experimental Results -- 4 Related Work -- 5 Conclusions and Future Work -- References -- Continuous-Action Reinforcement Learning for Memory Allocation in Virtualized Servers -- 1 Introduction -- 2 Background -- 2.1 Memory Management in Virtualized Nodes -- 2.2 Reinforcement Learning: Markov Decision Process -- 3 CAVMem: Algorithm for Virtualized Memory Management -- 3.1 Decentralized Strategy for Memory Management -- 3.2 Formulating the Problem as an MDP -- 4 Experimental Framework -- 5 Results for Evaluation -- 5.1 Results for Scenario 1 -- 5.2 Results for Scenario 2 -- 5.3 Results for Scenario 3 -- 5.4 Discussion -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Container Orchestration on HPC Clusters -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Kubernetes -- 3.2 Kubernetes Deployment -- 4 Implementation -- 4.1 General Approach -- 4.2 Kubernetes Cluster Deployment -- 4.3 HPC Worker Node Software Prerequisites -- 4.4 Networking -- 4.5 GE Worker Setup and Tear down -- 4.6 Kubernetes Cluster Configuration -- 5 Evaluation -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Data Pallets: Containerizing Storage for Reproducibility and Traceability -- 1 Introduction -- 2 Related Work -- 3 Design -- 3.1 Design and Implementation Challenges -- 3.2 Design and Implementation Details -- 3.3 Integration with Sandia Analysis Workbench (SAW) -- 4 Measurements -- 4.1 Time Overheads -- 4.2 Space Overheads -- 4.3 Discussion.
5 Integration with Sandia Analysis Workbench -- 6 Conclusions and Future Work -- References -- Sarus: Highly Scalable Docker Containers for HPC Systems -- 1 Introduction -- 2 Related Work -- 3 Sarus -- 3.1 Sarus Architecture -- 3.2 Container Creation -- 4 Extending Sarus with OCI Hooks -- 4.1 Native MPICH-Based MPI Support (H1) -- 4.2 NVIDIA GPU Support (H2) -- 4.3 SSH Connection Within Containers (H3) -- 4.4 Slurm Scheduler Synchronization (H4) -- 5 Performance Evaluation -- 5.1 Scientific Applications -- 6 Conclusions -- References -- Singularity GPU Containers Execution on HPC Cluster -- 1 Introduction -- 2 Singularity GPU Containers Building and Running -- 3 Benchmark -- 3.1 Systems Description -- 3.2 Test Case 1: Containerized Tensorflow Execution on GALILEO Versus Official Tensorflow Performance Data -- 3.3 Test Case 2: Containerized Versus Bare Metal Execution on GALILEO -- 4 Conclusion -- References -- A Multitenant Container Platform with OKD, Harbor Registry and ELK -- 1 Introduction -- 2 Past -- 2.1 Background -- 2.2 Challenges -- 3 Present -- 3.1 Evaluation of Container Orchestration Frameworks -- 3.2 Observability: Logging and OKD -- 3.3 Observability: Monitoring and OKD -- 4 Future -- 4.1 Monitoring -- 4.2 Container Policy and OKD -- 4.3 Gitops gitops and OKD -- 4.4 Continuous Delivery in OKD -- 4.5 OKD in the Cloud -- 5 Conclusion -- References -- Enabling GPU-Enhanced Computer Vision and Machine Learning Research Using Containers -- 1 Introduction -- 2 Defining the Base Container -- 2.1 System Setup: Ubuntu, CUDA, Docker, Nvidia-Docker -- 2.2 Docker and Container Runtime -- 2.3 TensorFlow -- 2.4 OpenCV -- 2.5 Cuda_tensorflow_opencv -- 3 Using the Base Container -- 3.1 Testing Code from a Bash Terminal -- 3.2 Integrating Darknet and Yolo V3 Python Bindings -- 4 Conclusion -- References.
Software and Hardware Co-design for Low-Power HPC Platforms -- 1 Introduction -- 2 Network Interface Primitives -- 3 HPC Prototype -- 4 User-Level Communication Library -- 5 MPI Implementation over the Proposed Architecture -- 6 Conclusions and Future Work -- References -- Modernizing Titan2D, a Parallel AMR Geophysical Flow Code to Support Multiple Rheologies and Extendability -- 1 Introduction -- 2 Titan2D and Benchmark Problem -- 3 Refactoring Strategies -- 3.1 Adopting a Python Interface -- 3.2 Merging Multiple Forks -- 3.3 Changing Data Layout to for Modern CPU Architectures -- 3.4 Efficient Indexing for Elements/Nodes Addressing -- 3.5 Introducing OpenMP and Hybrid OpenMP/MPI Parallelization -- 4 Performance Improvement Evaluation -- 5 Conclusions and Future Plans -- References -- Asynchronous AMR on Multi-GPUs -- 1 Introduction -- 2 Execution on Heterogeneous Architectures -- 2.1 Data Model and CPU-GPU Communication -- 2.2 Scheduling on Heterogeneous Architectures -- 2.3 API -- 2.4 Multi-GPU Support -- 3 Evaluation -- 4 Conclusions -- References -- Batch Solution of Small PDEs with the OPS DSL -- 1 Introduction -- 2 The OPS DSL -- 3 Batching Support in OPS -- 3.1 Extending the Abstraction -- 3.2 Execution Schedule Transformation -- 3.3 Data Layout Transformation -- 3.4 Alternating Direction Implicit Solver -- 4 Evaluation -- 4.1 The Application -- 4.2 Experimental Set-Up -- 4.3 Results -- 5 Conclusions -- References -- Scalable Parallelization of Stencils Using MODA -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 MODA and User-Defined Indices -- 3.2 Using GGDML Indices -- 3.3 Communication Identification -- 4 Evaluation -- 4.1 Test Application -- 4.2 Test System -- 4.3 Experiments -- 5 Summary -- References -- Comparing High Performance Computing Accelerator Programming Models -- 1 Introduction -- 2 Motivation -- 3 Related Work.
4 Analysis -- 5 Discussion -- 5.1 BT Benchmark -- 5.2 SP Benchmark -- 5.3 LBM Benchmark -- 5.4 LBDC Benchmark -- 6 Conclusion -- References -- Tracking User-Perceived I/O Slowdown via Probing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Probing -- 3.2 Data Reduction Using Statistics -- 3.3 Computing the Slowdown -- 4 Evaluation -- 4.1 Test Systems -- 4.2 Probing Tool -- 4.3 Timeseries of Individual Measurements -- 4.4 Host Variability -- 4.5 Understanding Application Behavior - The IO-500 -- 4.6 Long-Period -- 4.7 Slowdown -- 5 Conclusion -- References -- A Quantitative Approach to Architecting All-Flash Lustre File Systems -- 1 Introduction -- 2 Methods -- 3 File System Capacity -- 4 Drive Endurance -- 5 Metadata Configuration -- 5.1 MDT Capacity Required by DOM -- 5.2 MDT Capacity Required for Inodes -- 5.3 Overall MDT Capacity -- 6 Conclusion -- References -- MBWU: Benefit Quantification for Data Access Function Offloading -- 1 Introduction -- 2 The MBWU-Based Methodology -- 2.1 Background -- 2.2 What Is MBWU -- 2.3 How to Measure MBWU(s) -- 2.4 Evaluation Prototype -- 3 Evaluation -- 3.1 Infrastructure -- 3.2 Test Setup and Results -- 4 Related Work -- 5 Conclusion -- References -- Footprinting Parallel I/O - Machine Learning to Classify Application's I/O Behavior -- 1 Introduction -- 2 Related Work -- 3 DKRZ Monitoring -- 3.1 Metrics -- 4 Methodology -- 5 Test Data -- 5.1 Data Preparation -- 6 Evaluation -- 6.1 I/O Behavior Classification -- 6.2 Footprinting -- 7 Manual Identification of I/O Intensive Jobs -- 8 Summary and Conclusion -- References -- Adventures in NoSQL for Metadata Management -- 1 Introduction -- 2 Related Work -- 3 Metadata Model -- 3.1 Basic Metadata -- 3.2 Custom Metadata -- 4 Design -- 4.1 What Has the Right Features to Be Worth Testing? -- 4.2 What Is It Going to Take to Get It All Working at All?.
4.3 Can We Make Our Queries Work with Any Performance? -- 4.4 Battle Scars and Lessons for Our Next Battle Against Scale Out Computing Tools -- 5 Evaluation -- 5.1 Insert Time -- 5.2 Query Time -- 6 Conclusion and Future Work -- References -- Towards High Performance Data Analytics for Climate Change -- 1 Introduction -- 2 Main Challenges -- 3 The Ophidia Project -- 3.1 Multi-dimensional Storage Model -- 3.2 Array-Based Primitives and Parallel Operators -- 4 Benchmark and Experimental Results -- 4.1 Benchmark Definition -- 4.2 Test Environment -- 4.3 Experimental Results and Discussion -- 5 Related Work -- 6 Conclusions -- References -- An Architecture for High Performance Computing and Data Systems Using Byte-Addressable Persistent Memory -- 1 Introduction -- 2 Persistent Memory -- 2.1 Data Access -- 2.2 B-APM Modes of Operation -- 2.3 Non-volatile Memory Software Ecosystem -- 3 Opportunities for Exploiting B-APM for Computational Simulations and Data Analytics -- 3.1 Potential Caveats -- 4 Systemware Architecture -- 4.1 Job Scheduler -- 4.2 Data Scheduler -- 5 Performance Evaluation -- 6 Related Work -- 7 Summary -- References -- Mediating Data Center Storage Diversity in HPC Applications with FAODEL -- 1 Introduction -- 2 FAODEL Background -- 2.1 Kelpie -- 2.2 I/O Management (IOM) Modules -- 3 Mediating Storage Using Kelpie Object Naming -- 3.1 Kelpie Architectural Considerations -- 3.2 Annotating the Kelpie Namespace -- 3.3 Service-Initiated Mediation -- 3.4 Performance Considerations -- 4 Related Work -- 5 Conclusion -- References -- Predicting File Lifetimes with Machine Learning -- 1 Introduction -- 2 Specifying the Problem and Building the Models -- 2.1 Problem Specification -- 2.2 Dataset -- 2.3 Data Preprocessing -- 2.4 Models -- 3 Results -- 3.1 Evaluation Methodology -- 3.2 Training Times and Model Sizes -- 3.3 Accuracy.
3.4 Error and Accuracy Distribution.
Record Nr. UNISA-996466292803316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
High Performance Computing [[electronic resource] ] : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers / / edited by Michèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
High Performance Computing [[electronic resource] ] : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers / / edited by Michèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXV, 659 p. 402 illus., 239 illus. in color.)
Disciplina 004.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Computer engineering
Computer networks
Software engineering
Computers
Computer Engineering and Networks
Software Engineering
Computer Hardware
Computing Milieux
ISBN 3-030-34356-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Short Papers -- Preface to the First International Workshop on Legacy Software Refactoring for Performance -- P^3MA Workshop 2019 -- 4th International Workshop on In Situ Visualization (WOIV'19) -- Contents -- On the Use of Kernel Bypass Mechanisms for High-Performance Inter-container Communications -- 1 Introduction -- 2 Overview of Compared Solutions -- 3 Experimental Results -- 4 Related Work -- 5 Conclusions and Future Work -- References -- Continuous-Action Reinforcement Learning for Memory Allocation in Virtualized Servers -- 1 Introduction -- 2 Background -- 2.1 Memory Management in Virtualized Nodes -- 2.2 Reinforcement Learning: Markov Decision Process -- 3 CAVMem: Algorithm for Virtualized Memory Management -- 3.1 Decentralized Strategy for Memory Management -- 3.2 Formulating the Problem as an MDP -- 4 Experimental Framework -- 5 Results for Evaluation -- 5.1 Results for Scenario 1 -- 5.2 Results for Scenario 2 -- 5.3 Results for Scenario 3 -- 5.4 Discussion -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Container Orchestration on HPC Clusters -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Kubernetes -- 3.2 Kubernetes Deployment -- 4 Implementation -- 4.1 General Approach -- 4.2 Kubernetes Cluster Deployment -- 4.3 HPC Worker Node Software Prerequisites -- 4.4 Networking -- 4.5 GE Worker Setup and Tear down -- 4.6 Kubernetes Cluster Configuration -- 5 Evaluation -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Data Pallets: Containerizing Storage for Reproducibility and Traceability -- 1 Introduction -- 2 Related Work -- 3 Design -- 3.1 Design and Implementation Challenges -- 3.2 Design and Implementation Details -- 3.3 Integration with Sandia Analysis Workbench (SAW) -- 4 Measurements -- 4.1 Time Overheads -- 4.2 Space Overheads -- 4.3 Discussion.
5 Integration with Sandia Analysis Workbench -- 6 Conclusions and Future Work -- References -- Sarus: Highly Scalable Docker Containers for HPC Systems -- 1 Introduction -- 2 Related Work -- 3 Sarus -- 3.1 Sarus Architecture -- 3.2 Container Creation -- 4 Extending Sarus with OCI Hooks -- 4.1 Native MPICH-Based MPI Support (H1) -- 4.2 NVIDIA GPU Support (H2) -- 4.3 SSH Connection Within Containers (H3) -- 4.4 Slurm Scheduler Synchronization (H4) -- 5 Performance Evaluation -- 5.1 Scientific Applications -- 6 Conclusions -- References -- Singularity GPU Containers Execution on HPC Cluster -- 1 Introduction -- 2 Singularity GPU Containers Building and Running -- 3 Benchmark -- 3.1 Systems Description -- 3.2 Test Case 1: Containerized Tensorflow Execution on GALILEO Versus Official Tensorflow Performance Data -- 3.3 Test Case 2: Containerized Versus Bare Metal Execution on GALILEO -- 4 Conclusion -- References -- A Multitenant Container Platform with OKD, Harbor Registry and ELK -- 1 Introduction -- 2 Past -- 2.1 Background -- 2.2 Challenges -- 3 Present -- 3.1 Evaluation of Container Orchestration Frameworks -- 3.2 Observability: Logging and OKD -- 3.3 Observability: Monitoring and OKD -- 4 Future -- 4.1 Monitoring -- 4.2 Container Policy and OKD -- 4.3 Gitops gitops and OKD -- 4.4 Continuous Delivery in OKD -- 4.5 OKD in the Cloud -- 5 Conclusion -- References -- Enabling GPU-Enhanced Computer Vision and Machine Learning Research Using Containers -- 1 Introduction -- 2 Defining the Base Container -- 2.1 System Setup: Ubuntu, CUDA, Docker, Nvidia-Docker -- 2.2 Docker and Container Runtime -- 2.3 TensorFlow -- 2.4 OpenCV -- 2.5 Cuda_tensorflow_opencv -- 3 Using the Base Container -- 3.1 Testing Code from a Bash Terminal -- 3.2 Integrating Darknet and Yolo V3 Python Bindings -- 4 Conclusion -- References.
Software and Hardware Co-design for Low-Power HPC Platforms -- 1 Introduction -- 2 Network Interface Primitives -- 3 HPC Prototype -- 4 User-Level Communication Library -- 5 MPI Implementation over the Proposed Architecture -- 6 Conclusions and Future Work -- References -- Modernizing Titan2D, a Parallel AMR Geophysical Flow Code to Support Multiple Rheologies and Extendability -- 1 Introduction -- 2 Titan2D and Benchmark Problem -- 3 Refactoring Strategies -- 3.1 Adopting a Python Interface -- 3.2 Merging Multiple Forks -- 3.3 Changing Data Layout to for Modern CPU Architectures -- 3.4 Efficient Indexing for Elements/Nodes Addressing -- 3.5 Introducing OpenMP and Hybrid OpenMP/MPI Parallelization -- 4 Performance Improvement Evaluation -- 5 Conclusions and Future Plans -- References -- Asynchronous AMR on Multi-GPUs -- 1 Introduction -- 2 Execution on Heterogeneous Architectures -- 2.1 Data Model and CPU-GPU Communication -- 2.2 Scheduling on Heterogeneous Architectures -- 2.3 API -- 2.4 Multi-GPU Support -- 3 Evaluation -- 4 Conclusions -- References -- Batch Solution of Small PDEs with the OPS DSL -- 1 Introduction -- 2 The OPS DSL -- 3 Batching Support in OPS -- 3.1 Extending the Abstraction -- 3.2 Execution Schedule Transformation -- 3.3 Data Layout Transformation -- 3.4 Alternating Direction Implicit Solver -- 4 Evaluation -- 4.1 The Application -- 4.2 Experimental Set-Up -- 4.3 Results -- 5 Conclusions -- References -- Scalable Parallelization of Stencils Using MODA -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 MODA and User-Defined Indices -- 3.2 Using GGDML Indices -- 3.3 Communication Identification -- 4 Evaluation -- 4.1 Test Application -- 4.2 Test System -- 4.3 Experiments -- 5 Summary -- References -- Comparing High Performance Computing Accelerator Programming Models -- 1 Introduction -- 2 Motivation -- 3 Related Work.
4 Analysis -- 5 Discussion -- 5.1 BT Benchmark -- 5.2 SP Benchmark -- 5.3 LBM Benchmark -- 5.4 LBDC Benchmark -- 6 Conclusion -- References -- Tracking User-Perceived I/O Slowdown via Probing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Probing -- 3.2 Data Reduction Using Statistics -- 3.3 Computing the Slowdown -- 4 Evaluation -- 4.1 Test Systems -- 4.2 Probing Tool -- 4.3 Timeseries of Individual Measurements -- 4.4 Host Variability -- 4.5 Understanding Application Behavior - The IO-500 -- 4.6 Long-Period -- 4.7 Slowdown -- 5 Conclusion -- References -- A Quantitative Approach to Architecting All-Flash Lustre File Systems -- 1 Introduction -- 2 Methods -- 3 File System Capacity -- 4 Drive Endurance -- 5 Metadata Configuration -- 5.1 MDT Capacity Required by DOM -- 5.2 MDT Capacity Required for Inodes -- 5.3 Overall MDT Capacity -- 6 Conclusion -- References -- MBWU: Benefit Quantification for Data Access Function Offloading -- 1 Introduction -- 2 The MBWU-Based Methodology -- 2.1 Background -- 2.2 What Is MBWU -- 2.3 How to Measure MBWU(s) -- 2.4 Evaluation Prototype -- 3 Evaluation -- 3.1 Infrastructure -- 3.2 Test Setup and Results -- 4 Related Work -- 5 Conclusion -- References -- Footprinting Parallel I/O - Machine Learning to Classify Application's I/O Behavior -- 1 Introduction -- 2 Related Work -- 3 DKRZ Monitoring -- 3.1 Metrics -- 4 Methodology -- 5 Test Data -- 5.1 Data Preparation -- 6 Evaluation -- 6.1 I/O Behavior Classification -- 6.2 Footprinting -- 7 Manual Identification of I/O Intensive Jobs -- 8 Summary and Conclusion -- References -- Adventures in NoSQL for Metadata Management -- 1 Introduction -- 2 Related Work -- 3 Metadata Model -- 3.1 Basic Metadata -- 3.2 Custom Metadata -- 4 Design -- 4.1 What Has the Right Features to Be Worth Testing? -- 4.2 What Is It Going to Take to Get It All Working at All?.
4.3 Can We Make Our Queries Work with Any Performance? -- 4.4 Battle Scars and Lessons for Our Next Battle Against Scale Out Computing Tools -- 5 Evaluation -- 5.1 Insert Time -- 5.2 Query Time -- 6 Conclusion and Future Work -- References -- Towards High Performance Data Analytics for Climate Change -- 1 Introduction -- 2 Main Challenges -- 3 The Ophidia Project -- 3.1 Multi-dimensional Storage Model -- 3.2 Array-Based Primitives and Parallel Operators -- 4 Benchmark and Experimental Results -- 4.1 Benchmark Definition -- 4.2 Test Environment -- 4.3 Experimental Results and Discussion -- 5 Related Work -- 6 Conclusions -- References -- An Architecture for High Performance Computing and Data Systems Using Byte-Addressable Persistent Memory -- 1 Introduction -- 2 Persistent Memory -- 2.1 Data Access -- 2.2 B-APM Modes of Operation -- 2.3 Non-volatile Memory Software Ecosystem -- 3 Opportunities for Exploiting B-APM for Computational Simulations and Data Analytics -- 3.1 Potential Caveats -- 4 Systemware Architecture -- 4.1 Job Scheduler -- 4.2 Data Scheduler -- 5 Performance Evaluation -- 6 Related Work -- 7 Summary -- References -- Mediating Data Center Storage Diversity in HPC Applications with FAODEL -- 1 Introduction -- 2 FAODEL Background -- 2.1 Kelpie -- 2.2 I/O Management (IOM) Modules -- 3 Mediating Storage Using Kelpie Object Naming -- 3.1 Kelpie Architectural Considerations -- 3.2 Annotating the Kelpie Namespace -- 3.3 Service-Initiated Mediation -- 3.4 Performance Considerations -- 4 Related Work -- 5 Conclusion -- References -- Predicting File Lifetimes with Machine Learning -- 1 Introduction -- 2 Specifying the Problem and Building the Models -- 2.1 Problem Specification -- 2.2 Dataset -- 2.3 Data Preprocessing -- 2.4 Models -- 3 Results -- 3.1 Evaluation Methodology -- 3.2 Training Times and Model Sizes -- 3.3 Accuracy.
3.4 Error and Accuracy Distribution.
Record Nr. UNINA-9910357842303321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui