Order statistics on a hypercube

Order statistics on optically interconnected multiprocessor systems

We present an O((log log N)/sup 2/) -time algorithm for computing the distance transform of an N /spl times/ N binary image. Our algorithm is designed for the common concurrent read concurrent write parallel random access machine (CRCW PRAM) and requires O(N/sup 2+/spl epsi///log log N) processors, for any /spl epsi/ such that 0 < /spl epsi/ < 1. Our algorithm is based on a novel deterministic sampling scheme and can be used for computing distance transforms for a very general class of distance functions. We also present a scalable version of our algorithm when the number of processors is available p/sup 2+/spl epsi///log log p for some p < N. In this case, our algorithm runs in O((N/sup 2//p/sup 2/)+(N/p) log log p + (log log p)/sup 2/) time. This scalable algorithm is more practical since usually the number of available processors is much less than the size of the image.

A recursive doubling algorithm for solution of tridiagonal systems on hypercube multiprocessors

Optimal speed-up algorithms for template matching on SIMD hypercube multiprocessors with restricted local memory

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization’s profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

The development of computing systems with large numbers of processors has been motivated primarily by the need to solve large, complex problems more quickly than is possible with uniprocessor systems. Traditionally, multiprocessor systems have been uniprogrammed, i.e., dedicated to the execution of a single set of related processes, since this approach provides the fastest response for an individual program once it begins execution. However, if the goal of a multiprocessor system is to minimize average response time or to maximize throughput, then multiprogramming must be considered.In this paper, a model of a simple multiprocessor system with a two‐program workload is reviewed; the model is then applied to an Intel iPSC/2 hypercube multiprocessor with a workload consisting of parallel wavefront algorithms for solving triangular systems of linear equations. Throughputs predicted by the model are compared with throughputs obtained experimentally from an actual system. The results provide validation for the model and indicate that significant performance improvements for multiprocessor systems are possible through multiprogramming.

The effect of multiprocessor radius on scaling

We study scheduling algorithms in WDM optical interconnects with recirculating buffering. The interconnect we consider has wavelength conversion capabilities. We focus on limited range wavelength conversion while considering full range wavelength conversion as a special case. We formalize the problem of maximizing throughput and minimizing packet delay in such an interconnect as a matching problem in a bipartite graph and give an optimal parallel algorithm that runs in O(Bk/sup 2/), as compared to O((N+B)/sup 3/k/sup 3/) time if directly applying other existing matching algorithms, where N is the number of input/output fibers, B is the number of fiber delay lines and k is the number of wavelengths per fiber.

The task allocation problem with constant communication

On Efficient Parallel Algorithms for Solving Set Recurrence Equations

Data partitioning algorithms aiming to minimize the execution time and the energy of computations in self-adaptable data-parallel applications on modern extreme-scale multicore platforms must address two critical challenges. First, they must take into account the new complexities inherent in these platforms such as severe resource contention and non-uniform memory access. Second, they must have low practical runtime and memory costs. The sequential data partitioning algorithms addressing the first challenge have a theoretical time complexity of O( $m * m * p * p$ ) where $m$ is the number of points in the discrete speed/energy function and $p$ is the number of available processors. They, however, exhibit high practical runtime cost and excessive memory footprint, therefore, rendering them impracticable for employment in self-adaptable applications executing on extreme-scale multicore platforms. We present, in this paper, the parallel data partitioning algorithms that address both the challenges. They take as input the functional models of performance and energy consumption against problem size and output workload distributions, which are globally optimal solutions. They have a low time complexity of O( $m * m * p$ ) thereby providing a linear speedup of O( $p$ ) and low memory complexity of O( $n$ ) where $n$ is the workload size expressed as a multiple of granularity. They employ dynamic programming approach, which also facilitates the easier integration of performance and energy models of communications. We experimentally study the practical cost of application of our algorithms in two data-parallel applications, matrix multiplication and fast Fourier transform, on a cluster in Grid’5000 platform. We demonstrate that their practical runtime and memory costs are low making them ideal for employment in self-adaptable applications. We also show that the parallel algorithms exhibit tremendous speedups over the sequential algorithms. Finally, using theoretical analysis for a forecast exascale platform, we demonstrate that the parallel algorithms have negligible execution times compared to the matrix multiplication application executing on the platform.

We investigate the effect of global parallelism using a master slave approach, on the behavior of a steady state genetic algorithm for design optimization. Empirical results in several engineering design domains demonstrate that this simple form of parallelism which has the potential for almost linear speedup, does not significantly disturb the convergence pattern of the GA, even when the number of processors is comparable to the size of the population.

Scalable algorithms for the solution of Navier's equations of elasticity

We propose to construct electron correlation methods that are scalable in both molecule size and aggregated parallel computational power, in the sense that the total elapsed time of a calculation becomes nearly independent of the molecular size when the number of processors grows linearly with the molecular size. This is shown to be possible by exploiting a combination of local approximations and parallel algorithms. The concept is demonstrated with a linear scaling pair natural orbital local second-order Møller-Plesset perturbation theory (PNO-LMP2) method. In this method, both the wave function manifold and the integrals are transformed incrementally from projected atomic orbitals (PAOs) first to orbital-specific virtuals (OSVs) and finally to pair natural orbitals (PNOs), which allow for minimum domain sizes and fine-grained accuracy control using very few parameters. A parallel algorithm design is discussed, which is efficient for both small and large molecules, and numbers of processors, although true inverse-linear scaling with compute power is not yet reached in all cases. Initial applications to reactions involving large molecules reveal surprisingly large effects of dispersion energy contributions as well as large intramolecular basis set superposition errors in canonical MP2 calculations. In order to account for the dispersion effects, the usual selection of PNOs on the basis of natural occupation numbers turns out to be insufficient, and a new energy-based criterion is proposed. If explicitly correlated (F12) terms are included, fast convergence to the MP2 complete basis set (CBS) limit is achieved. For the studied reactions, the PNO-LMP2-F12 results deviate from the canonical MP2/CBS and MP2-F12 values by <1 kJ mol(-1), using triple-ζ (VTZ-F12) basis sets.

This paper develops a parallel evolutionary programming based optimal power flow solution algorithm. The proposed approach is less sensitive to the choice of starting points and types of generator cost curves. To improve the robustness and speed of convergence of the algorithm, population and gradient acceleration techniques are incorporated. The developed algorithm is implemented on a thirty-six-processor Beowulf cluster. The proposed approach has been tested on the IEEE 118-bus system under master-slave, dual-direction ring and 2D-mesh topologies. Computational speedup and generation costs for each parallel topology with different number of processors are then compared to those of the sequential EP approach.