High Performance Computing for Finite Element in Cloud

Computer and our life is increasingly dependent on computer technology, our life, learning has changed a lot. As a kind of important science and technology, computer information technology is at an unprecedented pace of development into people's lives step by step and is gradually applied to every field of human society. For nearly a century, with the unprecedented speed, computer technology has been widely applied to industrial production and other fields, in various fields plays an important history. This paper begins with the current situation of computer development, analyzes the reasons of the rapid development of computer technology, and then expounds the development of computer science and technology from two aspects: the intelligent supercomputer, the new high performance computer, and then summarizes the development trend of computer science and technology.

Big Data analyzes correlations from huge raw data and predicts outcomes. It has great impacts on scientific discoveries and value creation. High Performance Computing (HPC) uses parallel processing and advanced programs or software packages to complete complicated jobs quickly. Finite Element Method (FEM) is very powerful in scientific computation and engineering analysis. It has created huge values in almost every area of engineering. In a lot of applications, Finite Element Analysis (FEA) strongly relies on advanced computer technology and HPC. Big Data will play an important role in FEA and HPC. This paper presents confluences among Big Data, FEA and HPC.

Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems. The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs. Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific computing.

An architecture of high performance computer,called Hyper Parallel Processing(HPP),is proposed to satisfy the requirements of both High Performance Computing,and Utility Computing which will be the application model of data centers.HPP combines the benefits of the scalability of MPP,the communication efficiency of DSM,as well as the commodity of cluster.Comparisons of current main-stream high performance computer architectures show that none of them can satisfy both HPC and utility computing.The main features of HPP are Global Address Space(GAS) and Hyper Node with single Operating System image.HPP supports the distributed global address space including both memory and I/O,but without hardware cache coherence.A Hyper Node consists of a set of application specific CPUs and a(or more) OS specific CPU.The OS CPU maintains the single system image,while the application CPUs run only lightweight run-time software.Besides the GAS interconnect network for applications,a standard SAN connects all OS CPUs and I/O devices providing system management and storage service.HPP is able to provide many opportunities of innovative research in High Performance and Utility Computing areas,including communication,synchronization,programming model,node operating system,utility computing,fault isolation,CPU and system etc.According to HPP architecture,a prototype system of Dawning5000 HPC is implemented and the feasibility of HPP is proved.

How Classical, Super and Quantum Computers Work

A large percentage of colleges and universities in the world do not have a HPC culture existing on campus, where access to supercomputing resources (hardware) or supercomputing personnel (faculty and technical staff) and the need to work on grand challenge applications (GCA) is considerably absent. Incorporating courses related to HPC in such institutions presents multiple challenges - non-availability of HPC resources, academic staff trained in HPC related areas, inertia faced in changing the curriculum, absence of a tangible local industry, paucity of funds, lack of enthusiasm among academicians and perceived ignorance. This paper presents a classification of institutions based on three HPC related criteria and highlights the challenges faced in introducing HPC related courses into institutions that do not have a HPC culture. Experiences related to introducing HPC related topics in two institutions, PACE in India and KKU in Saudi Arabia, falling in two different regions in the classification, have been highlighted.

For decades, HPC has established itself as an essential tool for discoveries, innovations and new insights in science, research and development, engineering and business across a wide range of application areas in academia and industry. Today High-Performance Computing is also well recognized to be of strategic and economic value – HPC matters and is transforming industries. This article will discuss new emerging technologies that are being developed for all areas of HPC: compute/processing, memory and storage, interconnect fabric, I/O and software to address the ongoing challenges in HPC such as balanced architecture, energy efficient high-performance, density, reliability, sustainability, and last but not least ease-of-use. Of specific interest are the challenges and opportunities for the next frontier in HPC envisioned around the 2020 timeframe: ExaFlops computing. We will also outline the new and emerging area of High Performance Data Analytics, Big Data Analytics using HPC, and discuss the emerging new delivery mechanism for HPC - HPC in the Cloud.

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project.

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.

A 3D Free Surface Flow and Transport Model on Different High Performance Computation Architectures

R is a robust open-source programming language mainly used for statistical computing . Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel/concurrent computing. This paper presents an overview of techniques for parallel computing with R on ACI (a PSU Infrastructure) and benchmark it with C/C++. We review the scalabilty concern of R, and look at the simplicity of using R as a primary language in Coding for HPC. We will look at the various R packages for HPC like Rmpi, Rcpp, snow and snowfall. We utilize a series of algorithms to benchmark and will illustrate each benchmark with a representative graph for ease of understanding. The paper concludes with a better understanding of which language to use when in high performance computing .

HPC (High Performance Computing) is of great significance due to its excellent performance in computing acceleration. However, unlike other techniques in computer science, learning HPC requires advanced computing resources such as large-scale clusters which directly increase the cost of study for students. To help students to learn HPC programming easily, we design and develop a lightweight container-based experimental platform to provide students with easily accessible and customizable HPC practice environments. In our platform, we integrate multiple practical functional modules for students, teachers, and administrators respectively. It is convenient for a user to access high-performance computing resources via a web portal, use highly customizable basic environments and have nice graphical hands-on interactive HPC learning experiences from our platform.

In addition to the performance, power consumption has become a major concern in High Performance Computing (HPC) systems. Typically the cooling system and the IT loads are the major contributors to the power bills. Understanding the power consumption at different granular levels in the HPC system is a first step to quantify the problems in HPC system. By proper monitoring and effective utilization of the cooling system, the power requirements of the HPC facility can be effectively met. In this paper we present a system where node level power measurement and WSN based rack level temperature measurement are used to provide localized control of cold air supply. A Smart Power Monitoring and Distribution Unit (PMDU) is designed and developed, to replace the existing Power Distribution Unit (PDU) in HPC. This can measure and report the power consumption, to support power profiling of large scale HPC system. This measured data are communicated to a base station via Ethernet. This base station collects all such measurements which can be used for power profiling of IT load of the HPC system. This helps to provide better insight into the power utilization pattern. Wireless Sensor Network (WSN) is used to collect exhaust and inlet air temperature of the server nodes and this information is used for directing the cold air effectively. A vent control system is designed and fabricated for intelligently directing the air flow to the server node inlet. It takes the node power and the temperature data as its inputs. This enables supplying/ redirecting more cold air towards under-cooled nodes without creating an extra load on the cooling system, thereby bringing in effective cooling at reduced power consumption. As an added advantage this could help in hot-spots mitigation.

At first thought, one may not realize the need for a special issue of Parallel and Distributed Computing Practices focused on Cluster Computing when our community has already invested years of research and spent a wealth of resources on traditional Big Iron supercomputers, for instance, the SGI/Cray Origin and IBM SP. Academia, industry, and national laboratories are still using, and for the foreseeable future, will continue to use, supercomputers to solve both grand-challenge scale and high-throughput applications. Then why the interest and new popularity of research into clusters for high-performance parallel and distributed applications? Clusters incorporate the tremendous successes that are attributable to our parallel and distributed processing community. Over the next decade, clusters will span the entire range of high-performance computing platforms. Instead of being restricted solely to supercomputers for achieving high-performance computing, the spectrum of available platforms will spread out to Beowulf-class clusters, to superclusters, and on to supercomputers. Moving along this continuum represents increasing computational resources, which in turn allows the capability for solving larger classes of applications or instances of problems. Compute-bound applications on workstations often may take advantage of the extra horsepower provided by symmetric multiprocessors. Applications with even larger computational requirements and a high-degree of concurrency may use Beowulf clusters, that is, clusters that are home-built from mass-market, commercial off-the-shelf workstations and networks, for instance, PC's interconnected by Fast Ethernet. A supercluster may contain commodity SMP nodes interconnected with scalable, low-latency, high-bandwidth networks, and is integrated by a system vendor rather than the computational scientist. Applications with greater complexity, for example, grand challenges from the sciences, demand scalable systems with high-performance, interconnects, perhaps coupled with terascale I/O subsystems, and thus, are appropriate for these superclusters. Finally, supercomputers, the largest of the clusters, may provide the ultimate platform for the highest-fidelity models and simulations and data-intensive applications. At each step up this ladder of cluster architectures, the system cost increases approximately by an order of magnitude, but having the high-performance capability to solve the problem of interest may require an entry point at any of these levels, the Beowulf, or supercluster, or even supercomputer platform. Cluster computing with Beowulf- or superclusters, thus, may be one or two orders of magnitude less expensive than a supercomputer, and provide a cost-effective solution and capability that was not available on a workstation. But this cost comparison measures just the basic system price. What about the applications? As recently as a few years ago, the lack of a canonical high-performance architectural paradigm meant that migrating to a new computer system, even from the same vendor, typically required redesigning applications to efficiently use the high-performance system. Thus, the application must be redesigned with parallel algorithms and libraries that are optimized for each high-performance system. This process of porting, redesigning, optimizing, debugging, and analyzing, the application may in fact be prohibitively expensive, and certainly frustrating when the process is completed just in time to greet the next generation system! That was then, this is now. With the successful standardization of system-level interfaces, many of these issues no longer hinder our expedited use of these new, high-performance clusters. The message-passing interface (MPI) is a clear example of a standard that has contributed significantly to the success of cluster computing. Supercomputer vendors working together with the high-performance computing community embarked on a process to standardize the user-level interface and behavior for passing messages between concurrent tasks. Now, using ordinary sequential compilers and vendor-supplied or freely-available implementations of this message-passing interface, applications can be implemented efficiently on one supercomputer and ported with little or no effort to other parallel machines. The message-passing paradigm, thus, provides a reasonably good canonical high-performance model and methodology for implementing high-performance applications. An algorithm that exhibits efficient execution and scaling on one machine then is likely to perform well on another with little or no additional programming effort. Using this cluster computing paradigm, algorithms typically are optimized by minimizing the number of remote memory accesses (reducing the amount of messages passed), and carefully organizing the data placement to increase memory locality. Thus, high-performance algorithms and applications that have been optimized for supercomputers, using the same cluster paradigm, may be appropriate for Beowulf and superclusters. Cluster computing, realizing the importance of a standard high-performance programming environment that applies equally well to both ends of the architectural spectrum, highly leverages these and other successes from parallel and distributed processing research and development. This is in an exciting, new era for high-performance computing where the canonical architectural paradigm stretches from home-built Beowulf clusters, to superclusters, and on to supercomputers. But cluster computing faces some challenges ahead of it. Continued research is required for designing 1) scalable, high-performance algorithms, applications, and methodologies, that can tolerate the communication overheads, and adapt to hybrid, SMP, and heterogeneous, nodes; 2) high-performance computer architectures, scalable interconnection networks, and I/O subsystems; and 3) scalable systems software, compilers, and monitoring tools. Cluster computing has come of age, and it is high time for the parallel and distributed processing community to lay the groundwork for our own legacy of success stories. David A. Bader University of New Mexico

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.