Francisco F. Rivera

High performance genetic algorithm for land use planning

Abstract This study uses genetic algorithms to formulate and develop land use plans. The restrictions to be imposed and the variables to be optimized are selected based on current local and national legal rules and experts’ criteria. Other considerations can easily be incorporated in this approach. Two optimization criteria are applied: land suitability and the shape-regularity of the resulting land use patches. We consider the existing plots as the minimum units for land use allocation. As the number of affected plots can be large, the algorithm execution time is potentially high. The work thus focuses on implementing and analyzing different parallel paradigms: multi-core parallelism, cluster parallelism and the combination of both. Some tests were performed that show the suitability of genetic algorithms to land use planning problems.

Improving the locality of the sparse matrix-vector product on shared memory multiprocessors

We extend a model of locality and the subsequent process of locality improvement previously developed for the case of sparse algebra codes in monoprocessors to the case of NUMA shared memory multiprocessors (SMPs). In particular the product of a sparse matrix by a dense vector (SpM/spl times/V) is studied. In the model, locality is established at run-time considering parameters that describe the structure of the sparse matrix involved in the computations. The problem of increasing the locality is formulated as a graph problem, whose solution indicates some appropriate reordering of rows and columns of the sparse matrix. The reordering algorithms were tested for a broad set of matrices. We have also performed a comparison with other reordering algorithms. The results lead to general conclusions about improving SMP performance for other sparse algebra codes.

Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs

It is well-known that reordering techniques applied to sparse matrices are common strategies to improve the performance of sparse matrix operations, and particularly, the sparse matrix vector multiplication (SpMV) on CPUs. In this paper, we have evaluated some of the most successful reordering techniques on two different GPUs. In addition, in our study a number of sparse matrix storage formats were considered. Executions for both single and double precision arithmetics were also performed. We have found that SpMV is very sensitive to the application of reordering techniques on GPUs. In particular, several characteristics of the reordered matrices that have a big impact on the SpMV performance have been detected. In most of the cases, reordered matrices outperform the original ones, showing noticeable speedups up to 2.6x. We have also observed that there is no one storage format preferred over the others.

High performance air pollution modeling for a power plant environment

The aim of this work is to provide a high performance air quality simulation using the STEM-II (Sulphur Transport Eulerian Model 2) program, a large-scale pollution modeling application. First, we optimize the sequential program with the aim of increasing data locality. Then, we parallelized the program using OpenMP directives for shared memory systems, and the MPI library for distributed memory machines. Performance results are presented for a SGI O2000 multiproccessor, a Fujitsu AP3000 multicomputer and a Cluster of PCs. Experimental results show that the parallel versions of the code achieve important reductions in the CPU time needed by each simulation. This will allow us to obtain results with adequate speed and reliability for the industrial environment where it is intended to be applied.

Cluster validity based on the hard tendency of the fuzzy classification

Abstract We present two new fuzzy cluster validity functionals (minimum and mean hard tendencies), based on the analysis of the hard tendency of the fuzzy classification generated by the fuzzy c-means algorithm. We have used the bootstrap technique, to avoid the possible influence of local minimums, obtained by the fuzzy c-means algorithm.

Research Article: A GIS-embedded system to support land consolidation plans in Galicia

Land consolidation is a strategic instrument for rural planning and thus economic development in the Spanish region of Galicia. This paper describes an experimental system embedded in a GIS environment to aid rural engineers to develop land consolidation plans. The system supports all the stages of the plan and many functionalities are implemented as heuristic processes based on expert knowledge and advice. The overall aim is to overcome administrative and technical problems of traditional consolidation procedures. The system provides an integrated framework for the management of spatial and administrative consolidation information. It also includes optimization-based algorithms for the automated generation of multiple alternative parcel reallocations, as well as an environment to refine and objectively evaluate the proposed solutions. These key capabilities result in a powerful tool for decision making that dramatically reduces the time and cost of land consolidation plans. Pilot experiences in two consolidation zones of Galicia assess the feasibility and effectiveness of the system.

Parallel squared error clustering on hypercube arrays

Though new parallel algorithms are continually appearing in the literature, it is still quite rare for them to be capable of dealing with problems of sizes that do not conveniently fit the machine for which they are designed. This article presents the algorithm PSEC, a parallel squared error clustering algorithm for hypercube SIMD computers of arbitrary cube dimension with local memory. PSEC owes its flexibility to the association of each of the three dimensions of the problem (numbers of data points, features, and clusters) with a distinct subset of the dimensions of the hypercube.

Performance optimization of irregular codes based on the combination of reordering and blocking techniques

The combination of techniques based on reordering data with classic code restructuring techniques for increasing the locality in the execution of sparse algebra codes is studied in this paper. The reordering techniques are based on, first modeling the locality in run-time, and then applying a heuristic for increasing it. After this, a code restructuring technique specially tuned for sparse algebra codes called register blocking is applied. The product of a sparse matrix by a dense vector (SpMxV) is the code studied on different monoprocessors and distributed memory multiprocessors. The combination of both techniques was tested for a broad set of matrices from real problems and known repositories. The results expressed in terms of execution time show that an adequate reordering of the data improves the efficiency of applying register blocking, therefore, reducing the execution time for the sparse algebra code considered.

Modeling data locality for the sparse matrix-vector product using distance measures

Abstract In this work, we model the data locality in the execution of codes with irregular accesses. We focus on the product of a sparse matrix by a dense vector (SpM×V). In the model, locality is established taking into account pairs of rows or columns of sparse matrices. In order to evaluate this locality three functions are introduced based on two parameters: number of entry matches and number of block matches. The model is generalized considering windows of locality (groups of consecutive rows/columns of the matrix). We show results for a broad set of matrices measuring the goodness of our predictions of locality.

Modified Gram-Schmidt QR factorization on hypercube SIMD computers

Abstract QR factorization is a popular calculation method in matrix algebra due to its usefulness in the solution of problems such as estimating least squares and calculating eigenvalues. In this paper, we describe a parallel algorithm for the calculation of the QR factorization on a hypercube architecture of the SIMD type with distributed memory. We have chosen the modified Gram-Schmidt method with pivoting to determine the QR factorization as it is characterized by good numerical stability. As an application of the QR factorization, we analyze the problem of least squares, developing a complementary parallel algorithm for solving it. Both algorithms are general; they are not limited by the size of the problem or the dimension of the hypercube. Finally, we analyze the algorithmic complexities of both parallel algorithms.