
High Performance Computing
Description
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This book constitutes the proceedings of the Second Latin American Conference on High Performance Computing, CARLA 2015, a joint conference of the High-Performance Computing Latin America Community, HPCLATAM, and the Conferencia Latino Americana de Computación de Alto Rendimiento, CLCAR, held in Petrópolis, Brazil, in August 2015.
The 11 papers presented in this volume were carefully reviewed and selected from 17 submissions. They were organized in topical sections named: grid and cloud computing; GPU & MIC Computing: methods, libraries and applications; and scientific computing applications.
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Content
- Intro
- Preface
- Organization
- Contents
- Grid and Cloud Computing
- Running Multi-relational Data Mining Processes in the Cloud: A Practical Approach for Social Networks
- Abstract
- 1 Introduction
- 2 Background Knowledge
- 3 A Cloud-Based Approach for Running MRDM Analysis in Parallel
- 4 Experimental Evaluation
- 5 Conclusions
- Acknowledgments
- References
- Methods for Job Scheduling on Computational Grids: Review and Comparison
- Abstract
- 1 Introduction
- 2 Overview of Job Scheduling Problem in Grid Systems
- 2.1 Scheduling Problem in Grid Systems
- 2.2 ETC Computational Model
- 2.3 Energy Model
- 3 Heuristic and Metaheuristic Methods for Job Scheduling in Grids
- 3.1 Heuristics of Job Scheduling
- 3.2 Metaheuristics of Job Scheduling
- 4 Comparison of Scheduling Algorithms
- 4.1 Comparative Analysis
- 4.2 Analysis of the Highlighted Algorithms
- 5 Conclusions and Future Works
- Acknowledgments
- References
- Cloud Computing for Fluorescence Correlation Spectroscopy Simulations
- 1 Introduction
- 2 Biological Problem Description
- 2.1 Fluorescence Correlation Spectroscopy
- 2.2 Software Components
- 2.3 Related Work
- 3 System Architecture for the Cloud
- 3.1 Architecture Design and Application Flow
- 3.2 Storage
- 3.3 Fault Tolerance
- 4 Implementation Details
- 4.1 Web Role
- 4.2 Message Processing Worker Role
- 4.3 Job Creation Worker Role
- 4.4 HDInsight Cluster
- 4.5 MapReduce
- 4.6 Load Balancer
- 5 Validation and Analysis
- 5.1 Load Balancing
- 5.2 Parameter Sweep Simulations
- 5.3 Fault Tolerance Analysis
- 5.4 Autoscaling
- 5.5 Experimental Evaluation of a Realistic Simulation
- 6 Conclusions
- References
- Porting a Numerical Atmospheric Model to a Cloud Service
- 1 Introduction
- 2 Related Work
- 3 Brazilian Regional Atmospheric Modeling System (BRAMS)
- 4 Porting BRAMS to the Cloud
- 4.1 Challenges and Solutions
- 4.2 System Architecture of BRAMS in the Cloud
- 5 Experimental Methodology and Environment
- 5.1 Machines
- 6 Evaluation Results
- 6.1 Performance
- 6.2 Network Latency
- 6.3 Cloud Operations
- 7 Conclusions
- References
- Determining the Real Capacity of a Desktop Cloud
- Abstract
- 1 Introduction
- 2 Background
- 2.1 Desktop Grid/Cloud
- 2.2 UnaCloud
- 2.3 Performance
- 2.4 Cloud Monitoring
- 2.5 Metrics
- 2.6 SIGAR API
- 2.7 Modern Processor Technologies
- 3 Related Work
- 4 Methodology
- 4.1 Monitoring Component Developed
- 4.2 Hardware Testbed
- 4.3 Data Gathered
- 4.4 Data Analysis
- 4.5 Limitations
- 5 Experimental Setup
- 6 Experimental Results
- 7 Conclusion and Future Work
- References
- Improvements to Super-Peer Policy Communication Mechanisms
- 1 Introduction
- 2 Materials and Methods
- 3 Results
- 3.1 N-SP: N-Random Super Peer
- 3.2 A2A-SP: All-to-All Super Peer
- 4 Conclusions
- References
- GPU and MIC Computing: Methods, Libraries and Applications
- Asynchronous in Situ Processing with Gromacs: Taking Advantage of GPUs
- 1 Introduction
- 2 Related Work
- 2.1 In Situ Systems
- 2.2 Treatments with GPU
- 3 Framework Description
- 3.1 Gromacs
- 3.2 FlowVR
- 3.3 Gromacs-FlowVR Interaction
- 3.4 Benchmark Framework Description
- 3.5 Benchmarks
- 4 Experiments
- 4.1 Experimental Context
- 4.2 Gromacs Native
- 4.3 Gromacs Instrumented with FlowVR
- 4.4 Gromacs with CPU Analytics
- 4.5 Gromacs with GPU Analytics
- 4.6 Discussion
- 5 Conclusion
- References
- Solving Linear Systems on the Intel Xeon-Phi Accelerator via the Gauss-Huard Algorithm
- 1 Introduction
- 2 Solution of Linear Systems
- 2.1 The LU Factorization
- 2.2 The Gauss-Huard Algorithm
- 3 Efficient Implementation of GHA on the Intel Xeon Phi Co-processor
- 3.1 Experimental Setup
- 3.2 Baseline Implementation
- 3.3 Improving via Matrix Transposition, GaussHuard_T
- 3.4 Using OpenMP, GaussHuard_O
- 3.5 Global Evaluation
- 4 Concluding Remarks and Future Work
- References
- On a Dynamic Scheduling Approach to Execute OpenCL Jobs on APUs
- 1 Introduction
- 2 Related Work
- 3 OpenCL
- 4 Dynamic Scheduling
- 5 Numerical Results
- 5.1 Benchmark
- 5.2 Computational Platform
- 5.3 Results
- 6 Conclusion and Future Works
- References
- Scientific Computing Applications
- Fine-Tuning Xeon Architecture Vectorization and Parallelization of a Numerical Method for Convection-Diffusion Equations
- 1 Introduction
- 2 HOPMOC Method
- 2.1 1-D HOPMOC Algorithm
- 3 Computing Environment
- 4 Optimization
- 4.1 1-D HOPMOC Algorithm Vectorization
- 4.2 Parallelization
- 4.3 Data Parallelization
- 4.4 DATA Parallelization Performance Evaluation
- 4.5 Work Parallelization
- 5 Conclusion and Future Work
- References
- Parallel Performance Analysis of a Regional Numerical Weather Prediction Model in a Petascale Machine
- 1 Introduction
- 2 BRAMS Developments
- 3 Results
- 4 Final Remarks
- References
- Author Index
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