Parallel Hybrid Testing Techniques for the Dual-Programming Models-Based Programs

The importance of high-performance computing is increasing, and Exascale systems will be feasible in a few years. These systems can be achieved by enhancing the hardware’s ability as well as the parallelism in the application by integrating more than one programming model. One of the dual-programming model combinations is Message Passing Interface (MPI) + OpenACC, which has several features including increased system parallelism, support for different platforms with more performance, better productivity, and less programming effort. Several testing tools target parallel applications built by using programming models, but more effort is needed, especially for high-level Graphics Processing Unit (GPU)-related programming models. Owing to the integration of different programming models, errors will be more frequent and unpredictable. Testing techniques are required to detect these errors, especially runtime errors resulting from the integration of MPI and OpenACC; studying their behavior is also important, especially some OpenACC runtime errors that cannot be detected by any compiler. In this paper, we enhance the capabilities of ACC_TEST to test the programs built by using the dual-programming models MPI + OpenACC and detect their related errors. Our tool integrated both static and dynamic testing techniques to create ACC_TEST and allowed us to benefit from the advantages of both techniques reducing overheads, enhancing system execution time, and covering a wide range of errors. Finally, ACC_TEST is a parallel testing tool that creates testing threads based on the number of application threads for detecting runtime errors.

Optimal Solution for Hyper-Sphere Integral Classification Process of Big Data

This paper mainly discusses the optimal solution for hyper-sphere integral classification process of big data. The paper proposes an optimal calculation method for the target problem. Through statistics and analysis of big data, we get the constraint condition, and calculate a maximum value of data characteristic. Then, by the dual programming of Quadratic Programming, we obtain the optimal classification function for hyper-sphere integral classification process of big data. The experiment results show that the proposed algorithm can significantly improve the accuracy of the classification hyper-sphere integral for big data.

Topological Optimization of Plate Subjected to Linear Buckling Constraints Based on Independent Continuous Mapping Method

In this paper, a model of topology optimization for the lightest plate structures with linear buckling constraints is constructed based on Independent, Continuous and Mapping (ICM) method. Exponential function is adopted as filtering function of the element weight, the element stiffness matrix and the element geometric stiffness matrix. Based on the Taylor expansion and the filtering function, the buckling constraints were approximately expressed as explicit functions. The optimization model was translated into a dual programming and solved by the sequence second-order programming. Two numerical examples show that this method is validity and efficient.

GeodesicB-Preinvex Functions and Multiobjective Optimization Problems on Riemannian Manifolds

We introduce a class of functions called geodesicB-preinvex and geodesicB-invex functions on Riemannian manifolds and generalize the notions to the so-called geodesic quasi/pseudoB-preinvex and geodesic quasi/pseudoB-invex functions. We discuss the links among these functions under appropriate conditions and obtain results concerning extremum points of a nonsmooth geodesicB-preinvex function by using the proximal subdifferential. Moreover, we study a differentiable multiobjective optimization problem involving new classes of generalized geodesicB-invex functions and derive Kuhn-Tucker-type sufficient conditions for a feasible point to be an efficient or properly efficient solution. Finally, a Mond-Weir type duality is formulated and some duality results are given for the pair of primal and dual programming.