Scalability studies and large grid computations for surface combatant using CFDShip-Iowa

This chapter discusses the construction of rational cubic Ball (cubic/quadratic) which consists of three parameters with C1 continuity. These three parameters can be used to refine the interpolating curve as well as to obtain the best approximation in error estimation. This scheme was tested by interpolate data from the true function. To improve the accuracy, a new method to estimate the first derivative is proposed as it is significant in curve interpolation. Based on error measurement such as Maximum Absolute Error (\(e_{max}\)), Coefficient of Determination (\(R^2 )\), Root Mean Square Error (\(RMSE\)) and Central Processing Unit (CPU) time in second, the proposed scheme is better than existing schemes. Notably, we achieved \(e_{max} =\) 0.01331105, \(R^2 =\) 0.91398405, \(RMSE =\) 0.00448758 and CPU time = 0.00009958 by using the new derivative estimation. Thus, the proposed scheme with construction of a new derivative estimation has improved the existing rational interpolant.KeywordsRational interpolationCurve interpolationFree parameters \(e_{max}\) R 2 RMSECPU time

The stationary point of optimization problems can be obtained via conjugate gradient (CG) methods without the second derivative. Many researchers have used this method to solve applications in various fields, such as neural networks and image restoration. In this study, we construct a three-term CG method that fulfills convergence analysis and a descent property. Next, in the second term, we employ a Hestenses-Stiefel CG formula with some restrictions to be positive. The third term includes a negative gradient used as a search direction multiplied by an accelerating expression. We also provide some numerical results collected using a strong Wolfe line search with different sigma values over 166 optimization functions from the CUTEr library. The result shows the proposed approach is far more efficient than alternative prevalent CG methods regarding central processing unit (CPU) time, number of iterations, number of function evaluations, and gradient evaluations. Moreover, we present some applications for the proposed three-term search direction in image restoration, and we compare the results with well-known CG methods with respect to the number of iterations, CPU time, as well as root-mean-square error (RMSE). Finally, we present three applications in regression analysis, image restoration, and electrical engineering.

With processor speeds no longer doubling every 18—24 months owing to the exponential increase in power consumption and heat dissipation, modern high-end computing systems tend to rely less on the performance of single processing units and instead rely on achieving high performance by using the parallelism of a massive number of low-frequency/low-power processing cores. Using such low-frequency cores, however, puts a premium on end-host pre- and post-communication processing required within communication stacks, such as the Message Passing Interface (MPI) implementation. Similarly, small amounts of serialization within the communication stack that were acceptable on small/medium systems can be brutal on massively parallel systems. Thus, in this paper, we study the different non-data-communication overheads within the MPI implementation on the IBM Blue Gene/P system. Specifically, we analyze various aspects of MPI, including the MPI stack overhead itself, overhead of allocating and queueing requests, queue searches within the MPI stack, multi-request operations, and various others. Our experiments, that scale up to 131,072 cores of the largest Blue Gene/P system in the world (80% of the total system size), reveal several insights into overheads in the MPI stack, which were not previously considered significant, but can have a substantial impact on such massive systems.

A new algorithm for massively parallel calculations of electron correlation energy of large molecules based on the resolution of identity second-order Møller-Plesset perturbation (RI-MP2) technique is developed and implemented into the quantum chemistry software NTChem. In this algorithm, a Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) hybrid parallel programming model is applied to attain efficient parallel performance on massively parallel supercomputers. An in-core storage scheme of intermediate data of three-center electron repulsion integrals utilizing the distributed memory is developed to eliminate input/output (I/O) overhead. The parallel performance of the algorithm is tested on massively parallel supercomputers such as the K computer (using up to 45 992 central processing unit (CPU) cores) and a commodity Intel Xeon cluster (using up to 8192 CPU cores). The parallel RI-MP2/cc-pVTZ calculation of two-layer nanographene sheets (C150H30)2 (number of atomic orbitals is 9640) is performed using 8991 node and 71 288 CPU cores of the K computer.

Fully implicit finite differences methods for two-dimensional diffusion with a non-local boundary condition

The advancement of multicore systems demands applications with more threads. In order to facilitate this demand, parallel programming models such as message passing interface (MPI) are developed. By using such models, the execution time and the power consumption can be reduced significantly. However, the performance of MPI programming depends on the total number of threads and the number of processing cores in the system. In this work, we experimentally study the impact of Open MPI and POSIX Thread (Pthread) implementations on performance and power consumption of multicore systems. Data dependent (like heat conduction on 2D surface) and data independent (like matrix multiplication) applications are used with high performance hardware in the experiments. Simulation results suggest that both implementations of more threads running in a system with more cores have potential to reduce the execution time with negligible or little increase in total power consumption. It is observed that the performance of MPI implementation varies (due to the dynamic communication overhead among the processing cores).

OpenMP vs. MPI on a shared memory multiprocessor

A message passing interface (MPI)-based implementation (Autodock4.lga.MPI) of the grid-based docking program Autodock4 has been developed to allow simultaneous and independent docking of multiple compounds on up to thousands of central processing units (CPUs) using the Lamarkian genetic algorithm. The MPI version reads a single binary file containing precalculated grids that represent the protein-ligand interactions, i.e., van der Waals, electrostatic, and desolvation potentials, and needs only two input parameter files for the entire docking run. In comparison, the serial version of Autodock4 reads ASCII grid files and requires one parameter file per compound. The modifications performed result in significantly reduced input/output activity compared with the serial version. Autodock4.lga.MPI scales up to 8192 CPUs with a maximal overhead of 16.3%, of which two thirds is due to input/output operations and one third originates from MPI operations. The optimal docking strategy, which minimizes docking CPU time without lowering the quality of the database enrichments, comprises the docking of ligands preordered from the most to the least flexible and the assignment of the number of energy evaluations as a function of the number of rotatable bounds. In 24 h, on 8192 high-performance computing CPUs, the present MPI version would allow docking to a rigid protein of about 300K small flexible compounds or 11 million rigid compounds.

The implementation of time-domain Green’s functions (TDGFs) in the graphics processing unit (GPU) and the central processing unit (CPU) using a finite-difference scheme is shown. The TDGFs represent the transient electric scalar and magnetic vector potentials due to a horizontal electric dipole (HED) in open-layered media. The layered media is bounded with a perfectly matched layer (PML), symmetry axis, and perfect electric conductor (PEC). We adopted four different parallel approaches as follows: 1) open multiprocessing (OpenMP) CPU implementation; 2) message passing interface (MPI) CPU implementation; 3) open accelerators (OpenACC) GPU implementation; and 4) compute unified device architecture (CUDA) GPU implementation. The accuracy and efficiency of the utilized programming models are validated by comparing and verifying the obtained results using a sequential CPU implementation. Compared to single threaded CPU implementation, speed-ups obtained by the OpenMP, MPI, OpenACC, and CUDA programming models are $4.8\times $ , $6.12\times $ , $45.97\times $ , and $96.53\times $ higher, respectively. The final result shows that GPU implementation leads to a considerable speed-up while the solution’s accuracy is fixed.

A numerical method is developed to study the flow structures in the aortic arch. The method solves the incompressible Navier–Stokes equations. It uses a third‐order upwind scheme for the convective terms and the second‐order central scheme for the viscous terms. A DDADI time integration is used for achieving fast convergence. For the unsteady solutions, the second‐order Crank–Nicolson method coupled with the diagonalized diagonal dominated alternating direction implicit scheme (DDADI) time integration are used. The numerical results show that the method is about 2.5‐order accuracy in space and 1.8‐order accuracy in time. Then the method is used to investigate the vortex structure and strength of secondary flows in the aortic arch. Four different arch geometries are constructed to see the effect of arch configuration. Many flow properties such as pressure drop, vortex strength and separation are computed and compared among the four arch models. Copyright © 2002 John Wiley & Sons, Ltd.

This paper introduces the newly developed Infini- Band (IB) Management Queue capability, used by the Host Channel Adapter (HCA) to manage network task data flow dependancies, and progress the communications associated with such flows. These tasks include sends, receives, and the newly supported wait task, and are scheduled by the HCA based on a data dependency description provided by the user. This functionality is supported by the ConnectX-2 HCA, and provides the means for delegating collective communication management and progress to the HCA, also known as collective communication offload. This provides a means for overlapping collective communications managed by the HCA and computation on the Central Processing Unit (CPU), thus making it possible to reduce the impact of system noise on parallel applications using collective operations. This paper further describes how this new capability can be used to implement scalable Message Passing Interface (MPI) collective operations, describing the high level details of how this new capability is used to implement the MPI Barrier collective operation, focusing on the latency sensitive performance aspects of this new capability. This paper concludes with small scale bench- mark experiments comparing implementations of the barrier collective operation, using the new network offload capabilities, with established point-to-point based implementations of these same algorithms, which manage the data flow using the central processing unit. These early results demonstrate the promise this new capability provides to improve the scalability of high- performance applications using collective communications. The latency of the HCA based implementation of the barrier is similar to that of the best performing point-to-point based implementation managed by the central processing unit, starting to outperform these as the number of processes involved in the collective operation increases.

Explicit Runge–Kutta methods and implicit multistep methods utilizing a Newton–Krylov nonlinear solver are evaluated for a range of configurations of the shallow-water dynamical core of the spectral element community atmosphere model to evaluate their computational performance. These configurations are designed to explore the attributes of each method under different but relevant model usage scenarios including varied spectral order within an element, static regional refinement, and scaling to the largest problem sizes. This analysis is performed within the shallow-water dynamical core option of a full climate model code base to enable a wealth of simulations for study, with the aim of informing solver development within the more complete hydrostatic dynamical core used for climate research. The limitations and benefits to using explicit versus implicit methods, with different parameters and settings, are discussed in light of the trade-offs with Message Passing Interface (MPI) communication and memory and their inherent efficiency bottlenecks. Given the performance behavior across the configurations analyzed here, the recommendation for future work using the implicit solvers is conditional based on scale separation and the stiffness of the problem. For the regionally refined configurations, the implicit method has about the same efficiency as the explicit method, without considering efficiency gains from a preconditioner. The potential for improvement using a preconditioner is greatest for higher spectral order configurations, where more work is shifted to the linear solver. Initial simulations with OpenACC directives to utilize a Graphics Processing Unit (GPU) when performing function evaluations show improvements locally, and that overall gains are possible with adjustments to data exchanges.

In multi-criteria decision-making (MCDM) research, the criteria weights are crucial components that significantly impact the results. Many researchers have proposed numerous methods to establish the weights of the criterion. This paper provides a modified technique, the fuzzy method based on the removal effects of criteria (MEREC) by modifying the normalization technique and enhancing the logarithm function used to assess the entire performance of alternatives in the weighting process. Since MCDM problems intrinsically are ambiguous or complex, fuzzy theory is used to interpret the linguistic phrases into triangular fuzzy numbers. The comparative analyses were conducted through the case study of staff performance appraisal at a Malaysian academic institution and the simulation-based study is used to validate the effectiveness and stability of the presented method. The results of the fuzzy MEREC are compared with those from a few different objective weighting techniques based on the correlation coefficients, outlier tests and central processing unit (CPU) time. The results of the comparative analyses demonstrate that fuzzy MEREC weights are verified as the correlation coefficient values are consistent throughout the study. Furthermore, the simulation-based study demonstrates that even in the presence of outliers in the collection of alternatives, fuzzy MEREC is able to offer consistent weights for the criterion. The fuzzy MEREC also requires less CPU time compared to the existing MEREC techniques. Hence, the modified method is a suitable alternative and efficient for computing the objective criteria weights in the MCDM problems.

A comparison of the central processing unit (CPU) time and storage requirements for the parallel and deductive fault simulation techniques is presented. Versions of a parallel and deductive simulator were implemented and the comparison performed on an IBM System/360 Model 67 by simulating representative circuits including shift registers, sequencers, counters, two memory units, and a processor. The results indicate that the deductive technique requires less CPU time for "loosely sequential" circuits or circuits having large numbers of simulated faults (e.g., >1000). The parallel technique is faster for small (e.g., <500 gates) "highly sequential" circuits or for small numbers of simulated faults. The storage required for a parallel simulator, however, can always be less than that required for a deductive simulator. In general, if sufficient memory is available, the deductive simulator is the more cost-effective simulator when a wide range of circuits is to be simulated and only one type of simulator is available. A substantial savings in logic circuit development cost can be realized when the proper simulation technique is used for logic design verification, fault analysis, and the generation of diagnostic data.

A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of Operational Research, 174:492–508, 2006) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.