High Performance Parallel Computing.

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.

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.

Intelligent Internet Computing (IIC) is emerging rapidly including grid, cluster computing, and peer-topeer computing to provide computing and communication services any time and anywhere. IIC foresees the integration of communicating and computational devices and applications in our heterogeneous computing environments. In this paper, we focus on the task allocation, which is a typical problem in the area of high performance computing and has been extensively studied in the past. Efficient task allocation is critical for achieving secure and high performance in a heterogeneous computing (HC) environment. In addition, the problem of allocating these tasks to the machines of a HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Therefore, a task allocation algorithm based on discrete particle swarm optimization (PSO) algorithm in the HC environment is proposed in this paper. Firstly, a new structure of cluster system is designed, and a mathematical model is built for dynamic server alliance according to practical tasks and situations of servers. Then an efficient discrete PSO with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm's ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results compared with other algorithms show that this proposed algorithm produces better results in terms of both quality of solution and convergence speed.

article Free Access Share on Software support for heterogeneous computing Authors: Howard Jay Siegel Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , Henry G. Dietz Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , John K. Antonio Texas Tech University, Lubbock Texas Tech University, LubbockView Profile Authors Info & Claims ACM Computing SurveysVolume 28Issue 1March 1996 pp 237–239https://doi.org/10.1145/234313.234411Published:01 March 1996Publication History 21citation711DownloadsMetricsTotal Citations21Total Downloads711Last 12 Months67Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In Many/Multi-core processor architectures, hundreds and thousands of Intellectual Property (IP) cores are integrated to reinforce parallel processing and high performance computing. Integration of IP cores is effectively realized by a scalable communication framework, Network on Chip (NoC). NoC comprises of routers and interconnection links which aid transfer of information between IP cores. It is the router which dominants the performance of NoC. A baseline router incorporates the FIFO (First In First Out) buffers, the routing computation logic, the arbiter and the crossbar switch fabric. In this paper, we propose different techniques of merging arbitration and switching functionalities accomplished in wormhole NoC router. Proposed microarchitectures for merging these functionalities are Merged Arbitration and Switching (MAS) microarchitecture based on multiplexer reorganization, Pipelined Merged Arbitration and Switching (PMAS) microarchitecture based on Pipelining and Wave-pipelined Merged Arbitration and Switching (WMAS) microarchitecture based on Wave-pipelining. Synthesis results show that the MAS microarchitecture outperforms the Merged ARbiter and multipleXer (MARX) microarchitecture in area and power consumption by 21.8% and 39.5% respectively. Simulation results show that the PMAS and WMAS microarchitectures outperform MARX microarchitecture in throughput by 40% and 60% respectively at a marginal cost of area and power consumption. Therefore, the benefits of using MAS microarchitecture in wormhole NoC router is low area and power consumption and PMAS or WMAS microarchitecture is high throughput.

Load balancing is a key problem in parallel computing. Under the environment of increasing heterogeneous parallel systems, load balancing strategies facing new challenges. This paper is a survey of the developments of load balancing strategies on heterogeneous parallel system. First, we compare the differences between homogeneous and heterogeneous systems. Second, we review the status of research on related load balancing strategies and analyze existing load balancing algorithms. Finally, we discuss future challenges for load balancing in heterogeneous parallel computing.

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.

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.

Meta-computing for Fluid-Structure Coupled Simulation

Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.

Simulating a supersonic wake flow field behind a conical body is a computing intensive task. It requires a large number of computational cells to capture the dominant flow physics and a robust numerical algorithm to obtain a reliable solution. High performance parallel computers with unique distributed processing and data storage capability can provide this need. They have larger computational memory and faster computing time than conventional vector computers. We apply the PINCA Navier-Stokes code to simulate a wind-tunnel supersonic wake experiment on Intel Gamma, Intel Paragon, and IBM SP2 parallel computers. These simulations are performed to study the mean flow in the near wake region of a sharp, 7-degree half-angle, adiabatic cone at Mach number 4.3 and freestream Reynolds number of 40,600. Overall the numerical solutions capture the general features of the hypersonic laminar wake flow and compare favorably with the wind tunnel data. With a refined and clustering grid distribution in the recirculation zone, the calculated location of the rear stagnation point is consistent with the 2D axisymmetric and 3D experiments. In this study, we also demonstrate the importance of having a large local memory capacity within a computer node and the effective utilization of the number of computermore » nodes to achieve good parallel performance when simulating a complex, large-scale wake flow problem.« less

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

We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting Nvidia Graphics Processing Unit (GPU) accelerators. The work extends previous efforts of some of the authors in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BootCMatchG</i> library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the GPU kernels. Strong and weak scalability results of the new version of the library on well-known benchmark test cases are discussed. Comparisons with the Nvidia AmgX solution show a speedup, in the solve phase, up to 2.0x.

Summary form only given. Parallel computing for high performance scientific applications gained widespread adoption and deployment about two decades ago. Computer systems based on shared memory and message passing parallel architectures were soon followed by clusters and loosely coupled workstations, that afforded flexibility and good performance for many applications at a fractional cost of MPP. Such platforms, referred to as parallel distributed computing systems, have evolved considerably and are currently manifested as very sophisticated metacomputing and Grid systems. This paper traces the evolution of loosely coupled systems and highlights specific functional, as well as fundamental, differences between clusters and NOW of yesteryear versus metacomputing Grids of today. In particular, semantic differences between Grids and systems such as PVM and MPICH are explored. In addition, the recent trend in Grid frameworks to move away from conventional parallel programming models to a more service-oriented architecture is discussed. Exemplified by toolkits that follow the OGSA specification, these efforts attempt to unify aspects of Web-service technologies, high performance computing, and distributed systems in order to enable large scale, cross-domain sharing of compute, data, and service resources. The paper also presents specific examples of current metacomputing and Grid systems with respect to the above characteristics, and discusses the relative merits of different approaches.

This paper is a postprint of a paper submitted to and accepted for publication in CIRED 2021-The 26th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.