Josep Arnal

Parallel Programming Techniques Applied to Water Pump Scheduling Problems

AbstractMost of the energy consumed by a water company is used to operate pumping systems. Identifying the optimal schedule for such systems in near real time will drastically reduce energy costs. The pump scheduling problem comprises three main elements: the pumping system, the tank, and the water demand to be satisfied. In this paper, a mathematical programming model and techniques used to solve this problem are presented. This study analyzed a parallel programming paradigm to solve this problem by introducing stochastic programming techniques (scenario tree evaluation) and multisite problems. Numerical experiments were designed and completed on parallel computers combining classical mathematical programming techniques and parallel tools. As a result, the parallel programming strategy was experimentally proven to be a useful technique for near-real-time pump scheduling applications.

Non-stationary Parallel Newton Iterative Methods for Nonlinear Problems

Parallel algorithms for solving nonlinear systems are studied. Non-stationary parallel algorithms based on the Newton method are considered. Convergence properties of these methods are studied when the matrix in question is either monotone or an H-matrix. In order to illustrate the behavior of these methods, we implemented these algorithms on two distributed memory multiprocessors. The first platform is an Ethernet network of five 120 MHz Pentiums. The second platform is an IBM RS/6000 with 8 nodes. Several versions of these algorithms are tested. Experiments show that these algorithms can solve the nonlinear system in substantially less time that the current (stationary or non-stationary) parallel nonlinear algorithms based on the multisplitting technique.

A Parallel Method for Impulsive Image Noise Removal on Hybrid CPU/GPU Systems

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a comparison of the implementation parallelised in multi-core, GPUs and the combination of both is conducted. A performance analysis with large images is conducted in order to identify the amount of pixels to allocate in the CPU and GPU. The observed time shows that both devices must have work to do, leaving the most to the GPU. Results show that parallel implementations of denoising filters on GPUs and multi-cores are very advisable, and they open the door to use such algorithms for real-time processing.

A Fuzzy Metric in GPUs: Fast and Efficient Method for the Impulsive Image Noise Removal

The implementation of image correction algorithms on the CUDA platform is a relatively new field. Although the platform is easy to program, it is not easy to optimize the applications due to the number of decisions that have to be made. This paper reports an optimization study on the use of the CUDA platform to remove impulsive noise in images using fuzzy metric and the concept of peer group. The texture memory is used to speed up the access to data. In order to get the maximum bandwidth on the GPU memory, a strategy based on storing each pixel in 4 bytes is proposed.

Parallel relaxed and extrapolated algorithms for computing PageRank

In this paper, parallel Relaxed and Extrapolated algorithms based on the Power method for accelerating the PageRank computation are presented. Different parallel implementations of the Power method and the proposed variants are analyzed using different data distribution strategies. The reported experiments show the behavior and effectiveness of the designed algorithms for realistic test data using either OpenMP, MPI or an hybrid OpenMP/MPI approach to exploit the benefits of shared memory inside the nodes of current SMP supercomputers.

Synchronous and Asynchronous Parallel Algorithms with Overlap for Almost Linear Systems

Parallel algorithms for solving almost linear systems are studied. A non-stationary parallel algorithm based on the multi-splitting technique and its extension to an asynchronous model are considered. Convergence properties of these methods are studied for M-matrices and H-matrices. We implemented these algorithms on two distributed memory multiprocessors, where we studied their performance in relation to overlapping of the splittings at each iteration.

Image Noise Removal on Heterogeneous CPU-GPU Configurations.

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.

Parallel newton iterative methods based on incomplete LU factorizations for solving nonlinear systems

Parallel iterative algorithms based on the Newton method and on two of its variations, the Shamanskii method and the Chord method, for solving nonlinear systems are proposed. These algorithms also use techniques from the non–stationary multisplitting methods. Concretely, in order to construct the multisplitting, ILU factorizations are considered. Convergence properties of these parallel methods are studied for H–matrices. Computational results, on a distributed multiprocessor IBM RS/6000 SP, that show the effectiveness of these methods are included to illustrate the theoretical results. Topics: Numerical methods (nonlinear algebra), Parallel and distributed computing.

Parallel Newton two‐stage methods based on ILU factorizations for nonlinear systems

Parallel iterative algorithms based on the Newton method and on two of its variants, the Shamanskii method and the Chord method, for solving nonlinear systems are proposed. These algorithms are based on two-stage multisplitting methods where incomplete LU factorizations are considered as a mean of constructing the inner splittings. Convergence properties of these parallel methods are studied for H-matrices. Computational results of these methods on two parallel computing systems are discussed. The reported experiments show the effectiveness of these methods. Copyright

CT Medical Imaging Reconstruction Using Direct Algebraic Methods with Few Projections

In the field of CT medical image reconstruction, there are two approaches you can take to reconstruct the images: the analytical methods, or the algebraic methods, which can be divided into iterative or direct.