Numerical simulation of supersonic wake flow with parallel computers

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information.

Supersonic flows about conical bodies

The paper presents the processing of the results of numerical simulation of the vortex flow in the wing wake, the purpose of which is to search for the vortex core center. It is based on the results of numerical simulation of the vortex flow in the wake behind of the rectangular wing, performed at the Keldysh Institute of Applied Mathematics RAS. The center of the vortex core was determined in several ways: by the minima of the total and static pressure, the axial Mach number, mass flow rate, the maximum of axial vorticity, and as the center of mass of the axial vorticity distribution. A comparison of the results of determining the position of the vortex core center showed good quantitative agreement. As a result dependencies of vortex core center coordinates on longitudinal coordinate were obtained.

The knapsack problem is a typical one of NPC problems, which is easy to be described but difficult to be solved. It is very important in theory and practice to study it. Nowadays there is a variety of research in algorithm for solving it. As the parallel processing technologies develop, the research of effective parallel algorithms for this problem attracts much attention. To run those algorithms needs high level hardware and high performance parallel computers. Using mobile agent technologies, a more effective model to solve the complex distributed problems can be established. Combines mobile agent with the traditional parallel algorithm, the process in a parallel computer can be evolved into the one performed by some ordinary computers. This can avoid the limitation of experiment conditions and provides convenience in practice. In this paper, a distributed algorithm is proposed for the 0-1 knapsack problem based on the mobile agent, and it is feasible and effective in theoretical analysis.

This edited book aims to present the state of the art in research and development of the convergence of high-performance computing and parallel programming for various engineering and scientific applications. The book has consolidated algorithms, techniques, and methodologies to bridge the gap between the theoretical foundations of academia and implementation for research, which might be used in business and other real-time applications in the future.The book outlines techniques and tools used for emergent areas and domains, which include acceleration of large-scale electronic structure simulations with heterogeneous parallel computing, characterizing power and energy efficiency of a data-centric high-performance computing runtime and applications, security applications of GPUs, parallel implementation of multiprocessors on MPI using FDTD, particle-based fused rendering, design and implementation of particle systems for mesh-free methods with high performance, and evolving topics on heterogeneous computing. In the coming days the need to converge HPC, IoT, cloud-based applications will be felt and this volume tries to bridge that gap.

The ideal supersonic ow eld around unyawed right circular cones is unique amongst the various analytical uid dynamic solutions about simple bodies. In two-dimensional ow, two distinct solution regimes exist behind the attached weak shock wave: the subsonic and the supersonic, the former being a small locus in the attached shock solution region. For conical ow however, three distinct regimes occur behind the weak attached shock solution: entirely supersonic, entirely subsonic, and a regime in which a sonic line must be crossed at some point between the cone shock and body. These regimes, their limits and boundaries, and the conditions which de ne them are presented in detail. Based on robust, accurate, and e ciently-computed numerical solutions, conical ow properties are provided in standard formats with each regime clearly indicated. Due to the widespread consideration of conical bodies for compression in supersonic airbreathing propulsion systems, both historically and for contemporary and future concepts, the impact and potential uses of these three regimes for supersonic inlet di users are examined. For mixed-compression and fully external compression applications, only the fully supersonic regime is recommended for any initial conical shock-generating body. Mixed-compression axisymmetric inlet designs may be transiently subjected to mixed supersonic-subsonic regime captured ow from their conical forebodies, and the e ect of such an entrance ow on their internal di users should be considered by their designers. The subsonic shockedow regime merits some consideration in the context of an internal conical body generating the terminal shock in a mixed-compression inlet feeding a gas turbine or subsonic combustion ramjet.

Summary form only given. In this poster, we present our experience in implementing a high performance computing cluster for teaching parallel computing theory and development of parallel applications. In teaching parallel and high performance computing, there is often a gap between potential performance taught in the lectures and those practically experienced in exercises in the laboratory. The development of the ELIHE cluster provides us with an opportunity take a hands on approach in teaching programming environments, tools, and libraries for development of parallel applications, parallel computation, architectures, message passing and shared memory paradigms using MPI and OpenMP, etc, at both undergraduate and graduate level. The ELIHE HP cluster consists of 9 computational nodes and a master node. All the nodes in the cluster are commodity systems - PCs, running commodity software - Linux, and CLIC Mandrake. Creating the ELIHE cluster has fulfilled two important goals: to design and implement a HP cluster for teaching parallel computing architectures in the School of Science and Technology, and to promote the use of high performance computer technology for research to faculty members and students

In this paper we benchmark the performance of the Cray T3D, IBM 9076 SP/1 and Intel Paragon XP/S parallel computers, using implementations of parallel algorithms for the computation of the vector outer-product A = uvT operation. The vector outer-product operation, although very simple in nature, requires the computation of a large number of floating-point operations and its parallelization induces a great level of communication between the processors. It is thus suited to measure the relative speed of the processor, memory subsystem and network capabilities of a parallel computer. It should not be considered a ‘toy problem’, since it arises in numerical methods in the context of the solution of systems of non-linear equations – still a difficult problem to solve. We present algorithms for both the explicit shared-memory and message-passing programming models together with theoretical computation models for those algorithms. Actual experiments were run on those computers, using Fortran 77 implementations of the algorithms. The results obtained with these experiments show that due to the high degree of communication between the processors one needs a parallel computer with fast communications and carefully implemented data exchange routines. The theoretical computation model allows prediction of the speed-up to be obtained for some problem size on a given number of processors. © 1997 John Wiley & Sons, Ltd.

Computational performance evaluation of a limited area meteorological model by using the earth simulator

The goal of the SDSC effort described here is to evaluate the performance potential of the Oberhuber isopycnal (OPYC) ocean global circulation model on the 64-node iPSC/860 parallel computer at SDSC and its near term successor, the Intel Paragon, relative to that of a single vector processor of a CRAY Y-MP and its near term successor, the CRAY C90. This effort is in support of a larger joint project with researchers at Scripps Institution of Oceanography to study the properties of long (10 to 100-year scale), computationally intensive integrations of ocean global circulation models. Generally, performance of the OPYC model on the iPSC/860 has proved quite disappointing, with a simplified version of the entire model running at approximately 1.4 Mflops on a single i860 node in its original form and after extensive optimization, including coding in assembler, at 3.2 Mflops. The author estimates overall performance to be limited to 75 Mflops or less on the 64-node machine, as compared to 180 Mflops for a single CRAY Y-MP processor and 500 Mflops for a single CRAY C90 processor. Similarly, he believes an implementation on an Intel Paragon, even with several hundred nodes, will not be competitive with a single processor C90 implementation. Additionally, the Hamburg Large Scale Geostrophic (LSG) model has been implemented on high-performance workstations and evaluated for its computational potential as an alternative to OPYC for the long term integrations.

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid.

A review of ONERA aerodynamic research in support of a future supersonic transport aircraft

A novel fast-running model is developed to predict the three-dimensional (3-D) distribution of turbulent kinetic energy (TKE) in axisymmetric wake flows. This is achieved by mathematically solving the partial differential equation of the TKE transport using the Green's function method. The developed solution reduces to a double integral that can be computed numerically for a wake prescribed by any arbitrary velocity profile. It is shown that the solution can be further simplified to a single integral for wakes with Gaussian-like velocity-deficit profiles. Wind tunnel experiments were performed to compare model results against detailed 3-D laser Doppler anemometry data measured within the wake flow of a porous disk subject to a uniform free-stream flow. Furthermore, the new model is used to estimate the TKE distribution at the hub-height level of the rotating non-axisymmetric wake of a model wind turbine immersed in a rough-wall boundary layer. Our results show the important impact of operating conditions on TKE generation in wake flows, an effect not fully captured by existing empirical models. The wind-tunnel data also provide insights into the evolution of important turbulent flow quantities such as turbulent viscosity, mixing length and the TKE dissipation rate in wake flows. Both mixing length and turbulent viscosity are found to increase with the streamwise distance. The turbulent viscosity, however, reaches a plateau in the far-wake region. Consistent with the non-equilibrium theory, it is also observed that the normalised energy dissipation rate is not constant, and it increases with the streamwise distance.

The production of realistic image generated by computer requires a huge amount of computation and a large memory capacity. The use of highly parallel computers allows this process to be performed faster. Distributed memory parallel computers (DMPCs), such as hypercubes or transputer-based machines, offer an attractive performance/cost ratio when the load balancing has been balance and the partition of the data domain has been performed. This paper presents a parallel ray tracing algorithm for DMPC using a Shared Virtual Memory (SVM) which solves these two classical problems. This algorithm has been implemented on a hypercube iPSC/2 and results are given.