The ELIHE High-Performance Cluster

Remote method invocation (RMI) is available in the current Java language design and implementation, providing the much-needed capability of allowing objects running in different Java processes to collaborate using a variation on the popular remote procedure call (RPC). Although RMI provides features which are desirable for high-performance distributed computing, its design and implementation are deficient in key areas of importance to the high-performance computing community in general. This paper addresses the key deficiencies of RMI and how these deficiencies affect the design and implementation of distributed object applications. Reflective RMI (RRMI) is an open RMI implementation which makes better use of the object-oriented features of Java. RRMI is so-called reflective because it directly employs the reflection capabilities of the current Java language to invoke methods remotely. RRMI makes use of the dynamic class loader (a class called NetClassLoader) to allow client/server applications to be built for high-performance computing systems without having all of the .class files present on all nodes in a parallel computation. Among other features discussed are support for asynchronous remote method invocations with deferred reply and exception semantics. © 1998 John Wiley & Sons, Ltd.

As cluster-based architectures are becoming a well-established platform to the development and execution of parallel applications which demand for high performance, new requirements are imposed to the software used in such architectures. Within this context, we have developed DECK, an environment conceived to allow the development of parallel applications on top of this new architecture. This work presents the internal structure and the functionalities of DECK, as well as a performance evaluation of its two available implementations.

The demand for processing power has been ever increasing with the growth of high-performance computing (HPC) applications and so have the constraints restricting the solutions to such requirements. High-performance distributed and parallel computing and custom-built, hardware-based computing have attempted to address this problem with some success but at a substantial cost. Recently, systems augmented with Field-Programmable Gate Arrays (FPGAs) offering a fusion of traditional parallel and distributed machines with customizable and dynamically reconfigurable hardware have emerged as a cost-effective alternative to traditional systems. However, providing a robust runtime environment for such systems to which HPC users have become accustomed has been fraught with numerous challenges. Dynamic scheduling of large-scale HPC applications in such parallel reconfigurable computing (RC) environments is one such challenge and has not been sufficiently studied to our knowledge. In this paper, we simulatively analyze the performance of several common HPC scheduling heuristics that can be used by an automated job management service to schedule application tasks on a parallel RC system. We also present a performance prediction model which the scheduling heuristics employ to schedule several common HPC applications on a collection of typical FPGA processing platforms.

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.

In the phase-field simulation of dendrite growth during the solidification of an alloy, the computational cost becomes extremely high when the diffusion length is significantly larger than the curvature radius of a dendrite tip. In such cases, the adaptive mesh refinement (AMR) method is effective for improving the computational performance. In this study, we perform a three-dimensional dendrite growth phase-field simulation in which AMR is implemented via parallel computing using multiple graphics processing units (GPUs), which provide high parallel computation performance. In the parallel GPU computation, we apply dynamic load balancing to parallel computing to equalize the computational cost per GPU. The accuracy of an AMR refinement condition is confirmed through the single-GPU computations of columnar dendrite growth during the directional solidification of a binary alloy. Next, we evaluate the efficiency of dynamic load balancing by performing multiple-GPU parallel computations for three different directional solidification simulations using a moving frame algorithm. Finally, weak scaling tests are performed to confirm the parallel efficiency of the developed code.

This Research to Practice Full Paper presents the experience of implementing micro HPC (μHPC) clusters in the education context. In developing countries, access to and experience of High Performance Computing (HPC) facilities (which are important for the economic development of those countries) has presented challenges to academic institutions due to prohibitive costs of acquisition and maintenance of HPC systems. These challenges affect the delivery of courses, which include HPC and parallel computation in their syllabi. However, due to advancements in integrated circuit technologies, credit-card-sized personal computers (PCs) have emerged with the capabilities of a fully-fledged PC. These credit-card-sized PCs offer an opportunity for system integrators to build micro HPC (μHPC) clusters which, apart from their efficiency, scalability and availability, have a number of attributes which are not shared with conventional data-center based HPC clusters These include their low power consumption, portability, diskless nodes, cost-effectiveness and excellent price to performance ratios. μHPC clusters are, therefore, ideally suited to support curricula in all educational disciplines that demand parallel computation. The purpose of this study is to investigate the implementation of μHPC to find out from students how ease of use of a μHPC system is to acquire skills and knowledge of HPC. We used focus groups and surveys to gather data in the framework of the design science research paradigm. The implementation of μHPC clusters involved a demonstration of the system in the problem context that provided valuable lessons. The results indicate that μHPC is the ease of use HPC artifact for learning about management, integration, deployment, installation, downloading, and running of parallel programs.

As high performance computing becomes more important and supercomputers grow bigger and faster, the I/O part of applications can become a real problem in regard to overall execution times. While use of new and specialized hardware and tuning of parallel file systems help a lot in the struggle to minimize I/O times, adjustment of the execution environment is not the only option to improve overall application behavior. It is not always possible to run applications on parallel computers which are especially adapted to that kind of applications and one has to use the system settings provided. Not only the system administrator and the developers of hardware or software components can help to reduce application execution times but also the application programmer can help by making use of non-blocking I/O operations. To see if and how much performance gains can be achieved by making use of non-blocking I/O operations this thesis discusses non-blocking I/O operations in detail, proposes a benchmark to measure performance gains when switching from blocking I/O operations to their non-blocking counter parts and presents the results of different series of test runs.

Parallel application design and development is a major area of interest in the domain of high-performance scientific and industrial computing. In fact, parallel computing is becoming an integral part in several major application domains such as space, medicine, cancer and genetic research, graphics and animation, image processing, to name a few. With the advent of fast interconnecting networks of workstations and PCs, it is now becoming increasingly possible to develop high-performance parallel applications using the combined computing powers of these networked resources, at no extra cost. Contrast this to the situation until the early 90s, where parallel computing was mostly confined only to special-purpose parallel computers that were unaffordable by small research institutions. Nowadays, high-speed networks and fast general-purpose computers are aiding in the mainstream adoption of parallel computing at a much more affordable cost.KeywordsParallel ComputingDesign PatternParallel ApplicationApplication CodeAbstract ModuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

with the development of parallel technology and application, the performance analysis and visualization of parallel computing is one of the most important parts. This paper presents a parallel computing performance visualization system which is developed to visualize and analyze the speedup and efficiency of parallel computing. We visualize and analyze the results of magnetotelluric parallel forward modeling program based on MPI which used finite element method. It succeeds in collecting the data of program execution time and communication time, visualizing and analyzing the speedup and efficiency.

Parallel computing and quantum chromodynamics

Parallel, distributed and network-based computing is a continuosly evolving field, driven by progress in microprocessor architecture and interconnection technology, as well as by the needs of computing- and data-intensive applications in science and engineering, and, more recently, in business. This field is currently undergoing a significant change, because of the development of multicore and manycore processors, GPUs, and FPGAs, which are the new building blocks of parallel architectures. At the other end of the parallel and distributed computing scenario, computational grids are far from being a mature infrastructure and are evolving toward cloud computing, to get a higher level of virtualization. The availability of programming models, algorithms and software tools capable of harnessing the processing power offered by the new technologies is a key issue to make them usable by application developers. This special issue provides a view of the efforts carried out in this direction. Barlas introduces an optimization approach for reducing data communication and load imbalance in medical image matching on Grids. Binzenhofer et al. present a distributed and scalable algorithm to monitor a p2p network. Cesario and Talia discuss the use of data mining models and services on Grid systems for analysis of large data repositories. Danelutto et al. describe a performance model for component-based applications with stream communication semantics running on Grids. Danese et al. describe a FPGA-based coprocessor to accelerate double precision floating point operations in high-performance applications. Gross and Marquardt introduce a graphical editor providing abstractions from base technology for user-friendly configuration of Ubiquitous Computing environments. The papers collected here are selected extended versions of papers presented at PDP 2007, the Fifteenth Euromicro Conference on Parallel, Distributed and Network-based Processing, held in Naples, Italy, in February 2007. The conference was organized by the Institute for High-Performance Computing and Networking (ICAR) of the Italian National Research Council (CNR) in collaboration with the Second University of Naples, the University of Naples Parthenope and the University of Naples Federico II. We thank the editors of Scalable Computing: Practice and Experience for providing us the opportunity of publishing this issue, the authors for their contributions, and the referees for their precious help in selecting good-quality papers. Pasqua D'Ambra Institute for High-Performance Computing and Networking (ICAR), CNR Naples, Italy pasqua.dambra@cnr.it Daniela di Serafino Department of Mathematics, Second University of Naples Caserta, Italy daniela.diserafino@unina2.it Mario Rosario Guarracino Institute for High-Performance Computing and Networking (ICAR), CNR Naples, Italy mario.guarracino@cnr.it Francesca Perla Department of Statistics and Mathematics for Economic Research, University of Naples Parthenope Naples, Italy francesca.perla@uniparthenope.it

Solutions for Time-Critical Remote Sensing Applications The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers have begun incorporating high performance computing (HPC) models in remote sensing missions. High Performance Computing in Remote Sensing is one of the first volumes to explore state-of-the-art HPC techniques in the context of remote sensing problems. It focuses on the computational complexity of algorithms that are designed for parallel computing and processing. A Diverse Collection of Parallel Computing Techniques and Architectures The book first addresses key computing concepts and developments in remote sensing. It also covers application areas not necessarily related to remote sensing, such as multimedia andvideo processing. Each subsequent chapter illustrate s a specific parallel computing paradigm, including multiprocessor (cluster-based) systems, large-scale and heterogeneous networks of computers, grid computing platforms, and specialized hardware architectures for remotely sensed data analysis and interpretation. An Interdisciplinary Forum to Encourage Novel Ideas The extensive reviews of current and future developments combined with thoughtful perspectives on the potential challenges of adapting HPC paradigms to remote sensing problems will undoubtedly foster collaboration and development among many fields.

Parallel computing has become fundamental in processing the massive data volumes generated in modern science and industry. This paper presents a comprehensive survey and practical review of parallel computing in data processing, examining key frameworks (MPI, Open MP, CUDA, Hadoop MapReduce, Apache Spark, etc.), implementations, and real- world case studies. We discuss the architectures underlying shared-memory and distributed-memory systems and illustrate how parallelism on CPUs and GPUs is exploited for high- performance computing (HPC) and big data analytics. We review theoretical foundations (including Amdahl’s law for speedup limits) and compare programming models in terms of design, performance, and fault tolerance. A two-column IEEE-style for- mat is used to present critical insights from scholarly literature, with diagrams illustrating parallel architectures and workflows, and tabular summaries of tools and results. Case studies—from scientific HPC simulations to large-scale data analytics—highlight practical parallelism on multicore CPUs, GPUs, and computing clusters. We conclude with a discussion on the convergence of HPC and big data paradigms, the challenges of heterogeneous computing, and emerging trends. This survey serves as a reference for PhD researchers and practitioners seeking to understand frameworks and real-world practices in parallel computing for data processing.

Space station static and dynamic analyses using parallel methods

Industrial simulation on parallel computers