scholarly journals Adapting Map Resolution to Accomplish Execution Time Constraints in Wind Field Calculation

2015 ◽  
Vol 51 ◽  
pp. 2749-2753 ◽  
Author(s):  
Gemma Sanjuan ◽  
Tomàs Margalef ◽  
Ana Cortés
Keyword(s):  
Execution Time ◽  
Wind Field ◽  
Time Constraints ◽  
Field Calculation ◽  
Map Resolution

A Flow-Condition-Based Interpolation Method Combined with Splitting Algorithm for Wind Field Calculation

2013 ◽  
Vol 671-674 ◽  
pp. 1578-1582
Author(s):  
Bo Su ◽  
Xiang Ke Han
Keyword(s):  
Wind Field ◽  
Flow Condition ◽  
Interpolation Method ◽  
Solution Procedure ◽  
Field Calculation ◽  
Splitting Algorithm ◽  
One Dimensional ◽  
Advection Diffusion Equation ◽  
Finite Element Procedure ◽  
Interpolation Functions

Wind field calculation is a research focus for wind disaster prevention in Civil Engineering. A new finite element procedure using flow-condition-based interpolation method combined with splitting algorithm is proposed in the paper. It used the analytical solution of one-dimensional advection–diffusion equation, and naturally introduced upwind effect in element interpolation functions. Further, combined with splitting algorithm, the element interpolation functions of velocity and pressure have concise format without meet Babuska-Brezzi condition. A two dimension four-node bilateral fluid element was constructed using flow-condition-based interpolation method and a corresponding program was developed. The solution procedure was discussed in detail and the numerical example solution was given to illustrate the capabilities of the procedure

A Resource-Aware Dynamic Load-Balancing Parallelization Algorithm in a Farmer-Worker Environment

2012 ◽  
pp. 88-104
Author(s):  
M. Leeman
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Execution Time ◽  
Time Frame ◽  
Parallel Programs ◽  
Time Constraints ◽  
Digital Television ◽  
Television Content ◽  
Content Processing ◽  
Resource Aware

This paper describes an algorithm for dynamically assigning tasks to processing entities in a world where each task has a set of resource or service requirements and each processing entity a set of resources or service capabilities. A task needs to be assigned to a node that offers all required services and the set of tasks is finished within a minimal execution time frame. Dependability and adaptability are inherent to the algorithm so that it accounts for the varying execution time of each task or the failure of a processing node. The algorithm is based on a dependable technique for farmer-worker parallel programs and is enhanced for modeling the time constraints in combination with the required configuration set in a multidimensional resources model. This paper describes how the algorithm is used for dynamically load balancing and parallelizing the nightly tests of a digital television content-processing embedded device.

Building Ant System for Multi-Faceted Test Case Prioritization

2022 ◽  
pp. 671-686
Author(s):  
Manoj Kumar Pachariya
Keyword(s):  
Experimental Study ◽  
Empirical Study ◽  
Execution Time ◽  
Optimal Solution ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant System ◽  
Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

Building Ant System for Multi-Faceted Test Case Prioritization

2020 ◽  
Vol 8 (2) ◽  
pp. 23-37
Author(s):  
Manoj Kumar Pachariya
Keyword(s):  
Experimental Study ◽  
Empirical Study ◽  
Execution Time ◽  
Optimal Solution ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant System ◽  
Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

Test case prioritization and selection technique in continuous integration development environments: a case study

2018 ◽  
Vol 7 (2.28) ◽  
pp. 332 ◽  
Author(s):  
Lei Xiao ◽  
Huaikou Miao ◽  
Ying Zhong
Keyword(s):  
Execution Time ◽  
Regression Testing ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Selection Technique ◽  
Continuous Integration ◽  
Study Results

Regression testing is a very important activity in continuous integration development environments. Software engineers frequently integrate new or changed code that involves in a new regression testing. Furthermore, regression testing in continuous integration development environments is together with tight time constraints. It is also impossible to re-run all the test cases in regression testing. Test case prioritization and selection technique are often used to render continuous integration processes more cost-effective. According to multi objective optimization, we present a test case prioritization and selection technique, TCPSCI, to satisfy time constraints and achieve testing goals in continuous integration development environments. Based on historical failure data, testing coverage code size and testing execution time, we order and select test cases. The test cases of the maximize code coverage, the shorter execution time and revealing the latest faults have the higher priority in the same change request. The case study results show that using TCPSCI has a higher cost-effectiveness comparing to the manually prioritization.  

scholarly journals Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

2016 ◽  
Vol 73 (1) ◽  
pp. 240-258 ◽  
Author(s):  
Gemma Sanjuan ◽  
Carles Tena ◽  
Tomàs Margalef ◽  
Ana Cortés
Keyword(s):  
Wind Field ◽  
Sparse Matrix ◽  
Field Calculation
Sign in / Sign up

Export Citation Format

Share Document