286 results on '"Tomàs Margalef"'
Search Results
52. Online root-cause performance analysis of parallel applications.
53. Automatic Performance Tuning of Parallel Mathematical Libraries.
54. Injecting Dynamic Real-Time Data into a DDDAS for Forest Fire Behavior Prediction.
55. Computational Steering Strategy to Calibrate Input Variables in a Dynamic Data Driven Genetic Algorithm for Forest Fire Spread Prediction.
56. Performance models for dynamic tuning of parallel applications on Computational Grids.
57. Performance Model for Parallel Mathematical Libraries Based on Historical Knowledgebase.
58. On-Line Performance Modeling for MPI Applications.
59. Dynamic Pipeline Mapping (DPM).
60. An Adaptive System for Forest Fire Behavior Prediction.
61. Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction.
62. Automatic Generation of Dynamic Tuning Techniques.
63. Automatic Tuning in Computational Grids.
64. Search of Performance Inefficiencies in Message Passing Applications with KappaPI 2 Tool.
65. Improved Prediction Methods for Wildfires Using High Performance Computing: A Comparison.
66. Automatic Tuning of Master/Worker Applications.
67. Automatic Performance Analysis of Message Passing Applications Using the KappaPI 2 Tool.
68. Performance Analysis of Parallel Applications with KappaPI 2.
69. Wildland Fire Propagation Danger Maps Based on Factorial Experimentation.
70. S2F2M - Statistical System for Forest Fire Management.
71. Automatic Tuning of Data Distribution Using Factoring in Master/Worker Applications.
72. MATE: Dynamic Performance Tuning Environment.
73. Accelerating Wildland Fire Prediction on Cluster Systems.
74. Response time assessment in forest fire spread simulation: An integrated methodology for efficient exploitation of available prediction time.
75. Enhancing multi-model forest fire spread prediction by exploiting multi-core parallelism.
76. GMATE: Dynamic Tuning of Parallel Applications in Grid Environment.
77. Improving Wildland Fire Prediction on MPI Clusters.
78. Dynamic Performance Tuning of Distributed Programming Libraries.
79. Accelerating Optimization of Input Parameters in Wildland Fire Simulation.
80. Time aware genetic algorithm for forest fire propagation prediction: exploiting multi-core platforms.
81. Optimization of Fire Propagation Model Inputs: A Grand Challenge Application on Metacomputers (Research Note).
82. Web Remote Services Oriented Architecture for Cluster Management.
83. Evolutionary Optimization Techniques on Computational Grids.
84. Parameter calibration framework for environmental emergency models.
85. Dynamic Performance Tuning Environment.
86. Simulation of Forest Fire Propagation on Parallel & Distributed PVM Platforms.
87. Coupling Wind Dynamics into a DDDAS Forest Fire Propagation Prediction System.
88. On the Way of Applying Urgent Computing Solutions to Forest Fire Propagation Prediction.
89. Genetic Algorithm Characterization for the Quality Assessment of Forest Fire Spread Prediction.
90. Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction.
91. Load balancing in homogeneous pipeline based applications.
92. A methodology for transparent knowledge specification in a dynamic tuning environment.
93. Integrating Automatic Techniques in a Performance Analysis Session (Research Note).
94. Automatic Performance Analysis of Master/Worker PVM Applications with Kpi.
95. Prediction Time Assessment in a DDDAS for Natural Hazard Management: Forest Fire Study CaseI.
96. Knowledge-guided Genetic Algorithm for input parameter optimisation in environmental modelling.
97. Towards policies for data insertion in dynamic data driven application systems: a case study sudden changes in wildland fire.
98. Scalable dynamic Monitoring, Analysis and Tuning Environment for parallel applications.
99. Wildland fire growth prediction method based on Multiple Overlapping Solution.
100. Relating the Execution Behaviour with the Structure of the Application.
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