Cluster Computing Review

In the past decade there has been a dramatic shift from mainframe or ‘host−centric’ computing to a distributed ‘client−server’ approach. In the next few years this trend is likely to continue with further shifts towards ‘network−centric’ computing becoming apparent. All these trends were set in motion by the invention of the mass−reproducible microprocessor by Ted Hoff of Intel some twenty−odd years ago. The present generation of RISC microprocessors are now more than a match for mainframes in terms of cost and performance. The long−foreseen day when collections of RISC microprocessors assembled together as a parallel computer could out perform the vector supercomputers has finally arrived. Such high−performance parallel computers incorporate proprietary interconnection networks allowing low−latency, high bandwidth inter−processor communications. However, for certain types of applications such interconnect optimization is unnecessary and conventional LAN technology is sufficient. This has led to the realization that clusters of high−performance workstations can be realistically used for a variety of applications either to replace mainframes, vector supercomputers and parallel computers or to better manage already installed collections of workstations. Whilst it is clear that ‘cluster computers’ have limitations, many institutions and companies are exploring this option. Software to manage such clusters is at an early stage of development and this report reviews the current state−of−the−art. Cluster computing is a rapidly maturing technology that seems certain to play an important part in the ‘network−centric’ computing future

A Review of Commercial and Research Cluster Management Software

In the past decade there has been a dramatic shift from mainframe or ‘host-centric’ computing to a distributed ‘client-server’ approach. In the next few years this trend is likely to continue with further shifts towards ‘network-centric’ computing becoming apparent. All these trends were set in motion by the invention of the mass-reproducible microprocessor by Ted Hoff of Intel some twenty-odd years ago. The present generation of RISC microprocessors are now more than a match for mainframes in terms of cost and performance. The long-foreseen day when collections of RISC microprocessors assembled together as a parallel computer could outperform the vector supercomputers has finally arrived. Such high-performance parallel computers incorporate proprietary interconnection networks allowing low-latency, high bandwidth inter-processor communications. However, for certain types of applications such interconnect optimization is unnecessary and conventional LAN technology is sufficient. This has led to the realization that clusters of high-performance workstations can be realistically used for a variety of applications either to replace mainframes, vector supercomputers and parallel computers or to better manage already installed collections of workstations. Whilst it is clear that ‘cluster computers’ have limitations, many institutions and companies are exploring this option. Software to manage such clusters is at an early stage of development and this report reviews the current state-of-the-art. Cluster computing is a rapidly maturing technology that seems certain to play an important part in the ‘network-centric’ computing future

Using Java for distributed computing in the Gaia satellite data processing

In recent years Java has matured to a stable easy-to-use language with the flexibility of an interpreter (for reflection etc.) but the performance and type checking of a compiled language. When we started using Java for astronomical applications around 1999 they were the first of their kind in astronomy. Now a great deal of astronomy software is written in Java as are many business applications. We discuss the current environment and trends concerning the language and present an actual example of scientific use of Java for high-performance distributed computing: ESA's mission Gaia. The Gaia scanning satellite will perform a galactic census of about 1000 million objects in our galaxy. The Gaia community has chosen to write its processing software in Java. We explore the manifold reasons for choosing Java for this large science collaboration. Gaia processing is numerically complex but highly distributable, some parts being embarrassingly parallel. We describe the Gaia processing architecture and its realisation in Java. We delve into the astrometric solution which is the most advanced and most complex part of the processing. The Gaia simulator is also written in Java and is the most mature code in the system. This has been successfully running since about 2005 on the supercomputer "Marenostrum" in Barcelona. We relate experiences of using Java on a large shared machine. Finally we discuss Java, including some of its problems, for scientific computing.Comment: Experimental Astronomy, August 201

ILS-ESP: An efficient, simple, and parameter-free algorithm for solving the permutation flow-shop problem

From a managerial point of view, the more e cient, simple, and parameter-free (ESP) an algorithm is, the more likely it will be used in practice for solving real-life problems. Following this principle, an ESP algorithm for solving the Permutation Flowshop Sequencing Problem (PFSP) is proposed in this article. Using an Iterated Local Search (ILS) framework, the so-called ILS-ESP algorithm is able to compete in performance with other well-known ILS-based approaches, which are considered among the most e cient algorithms for the PFSP. However, while other similar approaches still employ several parameters that can a ect their performance if not properly chosen, our algorithm does not require any particular fine-tuning process since it uses basic ‘common sense’ rules for the local search, perturbation, and acceptance criterion stages of the ILS metaheuristic. Our approach defines a new operator for the ILS perturbation process, a new acceptance criterion based on extremely simple and transparent rules, and a biased randomization process of the initial solution to randomly generate di erent alternative initial solutions of similar quality -which is attained by applying a biased randomization to a classical PFSP heuristic. This diversification of the initial solution aims at avoiding poorly designed starting points and, thus, allows the methodology to take advantage of current trends in parallel and distributed computing. A set of extensive tests, based on literature benchmarks, has been carried out in order to validate our algorithm and compare it against other approaches. These tests show that our parameter-free algorithm is able to compete with state-of-the-art metaheuristics for the PFSP. Also, the experiments show that, when using parallel computing, it is possible to improve the top ILS-based metaheuristic by just incorporating to it our biased randomization process with a high-quality pseudo-random number generator.Preprin

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Inner product computation for sparse iterative solvers on\ud distributed supercomputer

Recent years have witnessed that iterative Krylov methods without re-designing are not suitable for distribute supercomputers because of intensive global communications. It is well accepted that re-engineering Krylov methods for prescribed computer architecture is necessary and important to achieve higher performance and scalability. The paper focuses on simple and practical ways to re-organize Krylov methods and improve their performance for current heterogeneous distributed supercomputers. In construct with most of current software development of Krylov methods which usually focuses on efficient matrix vector multiplications, the paper focuses on the way to compute inner products on supercomputers and explains why inner product computation on current heterogeneous distributed supercomputers is crucial for scalable Krylov methods. Communication complexity analysis shows that how the inner product computation can be the bottleneck of performance of (inner) product-type iterative solvers on distributed supercomputers due to global communications. Principles of reducing such global communications are discussed. The importance of minimizing communications is demonstrated by experiments using up to 900 processors. The experiments were carried on a Dawning 5000A, one of the fastest and earliest heterogeneous supercomputers in the world. Both the analysis and experiments indicates that inner product computation is very likely to be the most challenging kernel for inner product-based iterative solvers to achieve exascale

A review of High Performance Computing foundations for scientists

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analysed [3-6]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document we attempt to discuss the fundamentals of High Performance Computing (HPC) [7] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.Comment: 33 page

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR