Guide Future Application and Middleware Technology on e-Science

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TOP 5 Future Technology in computer Science

These standard components allow the construction of high speed parallel systems in the petascale range at a reasonable cost. The number of processors incorporated in such systems are of the order of 10 4 to 10 6. Due to the flexibility offered by parallel systems constructed with commodity components, these can easily be linked through wide area networks, for example the Internet, to realise Grids or Clouds.

Such networks can be accessed by a wide community of users from many different disciplines to solve compute intensive and data intensive problems requiring high speed computing resources. The problems associated with the efficient and effective use of such large systems were the theme of the biennial High Performance Computing workshop held in June in Cetraro, Italy.

A selection of papers presented at the workshop are collected in this book. They cover a range of topics, from algorithms and architectures to Grid and Cloud technologies to applications and infrastructures for e-science. The editors wish to cordially thank all the authors for preparing their contributions as well as the reviewers who supported this effort with their constructive recommendations.

With the widespread availability of high-speed networks, it becomes feasible to outsource computing to remote providers and to federate resources from many locations.

Towards tractable toolkits for the Grid: a plea for lightweight, usable middleware.

Such observations motivated the development, from the mids onwards, of a range of innovative Grid technologies, applications, and infrastructures. We review the history, current status, and future prospects for Grid computing. Recent developments in the field of supercomputing in Germany and Europe are presented.

With these systems, the JSC has finally realized its concept of a dual system complex, which is adapted to meet the requirements of the NIC application portfolio, both flexibility and scalability, as effective as possible. It is followed by the first implementation phase project, again coordinated by the JSC, which started mid of Significant advancements in hardware and software will be required to reach Exascale computing in this decade.

The confluence of changes needed in both architectures and applications has created an opportunity for co-design. This paper offers a co-design methodology for high-performance computing and describes several tools that are being developed to enable co-design. One of them is the maturity of parallel computing after many years of struggle and experimentation with different parallel computer architectures. The second is the relatively low price of processors and our ability to put many of them on a single chip. The third equally important factor is the structure of very many numerical mathematics algorithms containing highly parallelizable operations whose processing can be accelerated by using massively parallel GPU and multicore CPU.

In this paper we provide an overview of the field and simple but realistic examples. The paper is targeted for beginner CUDA users. We have decided to show a simple source code of vector addition on GPU. This example does not cover advanced CUDA usage, such as shared memory accesses, divergent branches, optimization coalescing or loop unrolling. To illustrate performance we demonstrate results of matrix-matrix multiplication where some of the optimization techniques were used to gain impressive speedup.

Although there are many production level service and desktop grids they are usually not able to interoperate. The growing number of commercial and scientific clouds strongly suggests that in the near future users will be able to combine cloud services to build new services, a plausible scenario being the case when users need to aggregate capabilities provided by different clouds. In such scenarios, it will be essential to provide virtual networking technologies that enable providers to support crosscloud communication and users to deploy crosscloud applications.

This chapter describes one such technology, its salient features and remaining challenges. It also makes the case for crosscloud computing, discussing its requirements, challenges possible technologies and applications. Using cloud technologies, it is possible to provision HPC services on-demand. Customers of the service are able to provision virtual HPC systems in a self-service portal and deploy and execute their specific application without operator intervention.

The business model foresees to only charge the amount of resources actually used. There remain open questions in the area of performance optimization, advanced resource management, and fault tolerance. The Open Cirrus cloud computing testbed offers an environment in which we can treat these problems. Nowadays cloud computing is a popular paradigm to provide software, platform and infrastructure as a service to the consumers. It has been observed that seldom the capacity of personal computers PC is fully utilized. Desktop cloud is a novel approach for resource harvesting in a heterogeneous non-dedicated desktop environment.

This paper discusses a virtual infrastructure manager and a scheduling framework to leverage idle PCs, with the permission of PC owners. Prima facie, VirtualBox is the best suited hypervisor as a backbone of the private desktop cloud architecture. A consumer is able to submit a lease to be deployed on idle resources, to launch a computation abstracted as a virtual machine VM or a virtual cluster using virtualization.

In this approach, the role of the Scheduler is to balance both requirements of resource provider and resource consumer of the cloud in a non-dedicated heterogeneous environment. In addition, the permission of PC owners is taken into account, and consumers expect the best possible performance during the whole session. From the consumer's point of view, a prototype implementation of desktop clouds is useful for submitting lease requirement to the scheduler e.

This work discusses the scheduling technique for Virtual Infrastructure Management VIM and virtual cluster launching in private desktop clouds; besides, Virtual Disk Preservation Mode DPM and its relation with virtual cluster deployment time is explained.

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In all, it is quite challenging to yield the power of the idle resources in such a non-dedicated heterogeneous environment. The complexity of high-end computing has been increasing rapidly following the exponential increase in processing speed of the novel electronic digital technologies. Subsequently, the software development productivity has been attracting higher attention by the professional community because of its increasing importance for the development of complex software systems and applications. At the same time, component-based technologies have emerged as a modern and promising alternative with a clear potential to improve significantly the productivity of software development, particularly for extreme-scale computing.

However, the lack of longer-term experience and the increasing complexity of the target systems demand much more research results in the field. In particular, the search for the most appropriate component model and corresponding programming environments is of high interest and importance. The higher level of complexity involves a wider range of requirements and resources which demand dedicated support for dynamic properties and flexibility that could be provided in an elegant way by adopting the component-based methodology for software development.

Middleware for Distributed Data Management - SAGE Research Methods

General purpose many-core architectures of the future need new scalable operating system designs and abstractions in order to be managed efficiently. As both memory and processing resources will be ubiquitous, concurrency will be the norm.

We present a strategy for operating systems for such architectures, presenting the approach we take for our SVP Self-adaptive Virtual Processor based Microgrid many-core architecture. Even though, they are getting more complex to develop. However, the continual growth of computing and storage capabilities is achieved with an increase complexity of infrastructures. Thus, there is an important challenge to define programming abstractions able to deal with software and hardware complexity.

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An interesting approach is represented by software component models. This chapter first analyzes how high performance interactions are only partially supported by specialized component models.

Future Generation Computer Systems

Then, it introduces HLCM, a component model that aims at efficiently supporting all kinds of static compositions. Current desktop computers are heterogeneous systems that integrate different types of processors. For example, general-purpose processors and GPUs do not only have different characteristics but also adopt diverse programming models. Despite these differences, data parallelism is exploited for both types of processors, by using application processing interfaces such as OpenMP and CUDA, respectively. In this work we propose to collaboratively use all these types of processors, thus increasing the amount of data parallelism exploited.

In this setup, each processor executes its own optimized implementation of a target application.

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To achieve this goal, a platform has been developed composed of a task scheduler and an algorithm for runtime dynamic load balancing using online performance models of the different devices. These models are built without relying on any prior assumptions on the target application or system characteristics. The modeling time is negligible when several instances of a class of applications are executed in sequence or for iterative applications. As a case study, a database application is chosen to illustrate the usage of the proposed algorithm for building the performance models and to achieve dynamic load balancing.

Experimental results clearly show the advantage of collaboratively using a quad-core processor along with a GPU. This special issue aims to bring together researchers, developers and industry experts in order to foster the investigations on cutting-edge research and allowing one to contribute in advancing the blockchain innovation. Increasingly heterogenous and inter-networked environments allow such threats to become more difficult to combat, e.

Researchers aim to address these new threats with the development of novel methods countermeasures for defending networked systems. This is challenging and important at the same time. One of the most important advancements proposed by the community of security experts both from industry and academia deals with new forms of traffic normalization or active wardens, which allow to mitigate attacks, but do not offer a comprehensive protection.

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  • Moreover, novel attacks target highly specific features of the system to be exploited, for instance, vulnerabilities of the hardware and its energy consumption and network side channels.