A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

Parallel Adaptive Monte Carlo Integration with the Event Generator WHIZARD

We describe a new parallel approach to the evaluation of phase space for Monte-Carlo event generation, implemented within the framework of the WHIZARD package. The program realizes a twofold self-adaptive multi-channel parameterization of phase space and makes use of the standard OpenMP and MPI protocols for parallelization. The modern MPI3 feature of asynchronous communication is an essential ingredient of the computing model. Parallel numerical evaluation applies both to phase-space integration and to event generation, thus covering the most computing-intensive parts of physics simulation for a realistic collider environment.Comment: 28 pages, 4 figure

Overview of Parallel Platforms for Common High Performance Computing

The paper deals with various parallel platforms used for high performance computing in the signal processing domain. More precisely, the methods exploiting the multicores central processing units such as message passing interface and OpenMP are taken into account. The properties of the programming methods are experimentally proved in the application of a fast Fourier transform and a discrete cosine transform and they are compared with the possibilities of MATLAB's built-in functions and Texas Instruments digital signal processors with very long instruction word architectures. New FFT and DCT implementations were proposed and tested. The implementation phase was compared with CPU based computing methods and with possibilities of the Texas Instruments digital signal processing library on C6747 floating-point DSPs. The optimal combination of computing methods in the signal processing domain and new, fast routines' implementation is proposed as well

EVALUATION OF CLASSICAL INTER-PROCESS COMMUNICATION PROBLEMS IN PARALLEL PROGRAMMING LANGUAGES

It is generally believed for the past several years that parallel programming is the future of computing technology due to its incredible speed and vastly superior performance as compared to classic linear programming. However, how sure are we that this is the case? Despite its aforesaid average superiority, usually parallel-program implementations run in single-processor machines, making the parallelism almost virtual. In this case, does parallel programming still remain superior?The purpose of this document is to research and analyze the performance, in both storage and speed, of three parallel-programming language libraries: OpenMP, OpenMPI and PThreads, along with a few other hybrids obtained by combining two of these three libraries. These analyses will be applied to three classical multi-process synchronization problems: Dining Philosophers, Producers-Consumers and Sleeping Barbers

Analysis of Various Decentralized Load Balancing Techniques with Node Duplication

Experience in parallel computing is an increasingly necessary skill for today’s upcoming computer scientists as processors are hitting a serial execution performance barrier and turning to parallel execution for continued gains. The uniprocessor system has now reached its maximum speed limit and, there is very less scope to improve the speed of such type of system. To solve this problem multiprocessor system is used, which have more than one processor. Multiprocessor system improves the speed of the system but it again faces some problems like data dependency, control dependency, resource dependency and improper load balancing. So this paper presents a detailed analysis of various decentralized load balancing techniques with node duplication to reduce the proper execution time

Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biology

[Abstract] This paper describes and assesses a parallel multimethod hyperheuristic for the solution of complex global optimization problems. In a multimethod hyperheuristic, different metaheuristics cooperate to outperform the results obtained by any of them isolated. The results obtained show that the cooperation of individual parallel searches modifies the systemic properties of the hyperheuristic, achieving significant performance improvements versus the sequential and the non-cooperative parallel solutions. Here we present and evaluate a hybrid parallel scheme of the multimethod, using both message-passing (MPI) and shared memory (OpenMP) models. The hybrid parallelization allows to achieve a better trade-off between performance and computational resources, through a compromise between diversity (number of islands) and intensity (number of threads per island). For the performance evaluation, we considered the general problem of reverse engineering nonlinear dynamic models in systems biology, which yields very large mixed-integer dynamic optimization problems. In particular, three very challenging problems from the domain of dynamic modeling of cell signaling were used as case studies. In addition, experiments have been carried out in a local cluster, a large supercomputer and a public cloud, to show the suitability of the proposed solution in different execution platforms.Gobierno de España; DPI2017-82896-C2-2-RGobierno de España; TIN2016-75845-PXunta de Galicia; R2016/045Xunta de Galicia; ED431C 2017/0

Remote sensing big data computing: challenges and opportunities

As we have entered an era of high resolution earth observation, the RS data are undergoing an explosive growth. The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS ‘‘Big Data’’. Fortunately, we are witness the coming technological leapfrogging. In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data