We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of funda...
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We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of fundamental problems in turbulence through computational materials science research to biomedical applications at the forefront of HIV/AIDS research and cerebrovascular haemodynamics. This work was mainly performed on the US TeraGrid 'petascale' resource, Ranger, at Texas Advanced computing Center, in the first half of 2008 when it was the largest computing system in the world available for open scientific research. We have sought to use this petascale supercomputer optimally across application domains and scales, exploiting the excellent parallel scaling performance found on up to at least 32 768 cores for certain of our codes in the so-called 'capability computing' category as well as high-throughput intermediate-scale jobs for ensemble simulations in the 32-512 core range. Furthermore, this activity provides evidence that conventional parallel programming with MPI should be successful at the petascale in the short to medium term. We also report on the parallel performance of some of our codes on up to 65 636 cores on the IBM Blue Gene/P system at the Argonne Leadership computing Facility, which has recently been named the fastest supercomputer in the world for open science.
During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high comple...
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During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high complexity of these applications hampers the testing process. Moreover, generating adequate test suites to properly check these applications is a challenging task due to the elevated number of potential test cases. Mutation testing is a valuable technique to measure the quality of the selected test suite that can be used to overcome this difficulty. However, one of the main drawbacks of mutation testing lies on the high computational cost associated to this process. In this paper we propose a dynamic distributed algorithm focused on HPC systems, called EMINENT, which has been designed to face the performance problems in mutation testing techniques. EMINENT alleviates the computational cost associated with this technique since it exploits parallelism in cluster systems to reduce the final execution time. In addition, several experiments have been carried out on three applications in order to analyse the scalability and performance of EMINENT. The results show that EMINENT provides an increase in the speed-up in most scenarios.
We present a parallel algorithm that solves a time-domain non-linear mathematical model of the cochlea. The previously known serial solution of the cochlear model is recursive in the longitudinal dimension and iterati...
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We present a parallel algorithm that solves a time-domain non-linear mathematical model of the cochlea. The previously known serial solution of the cochlear model is recursive in the longitudinal dimension and iterative in the time dimension. These two characteristics of the serial solution limit parallelism and prevent efficient computations on a massively parallel processor. We introduce a novel parallel algorithm that successfully overcomes the challenges posed by the cochlear model. We present performance results of a parallel implementation of the algorithm that shortens the computation time by a typical factor of 160 – 180, which makes the proposed algorithm of practical value for applications such as clinical audiological diagnosis.
This paper proposes a method for reducing the number of search nodes involved in the solution of queries arriving to a Web search engine. The method is applied by the query receptionist machine during situations of su...
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This paper proposes a method for reducing the number of search nodes involved in the solution of queries arriving to a Web search engine. The method is applied by the query receptionist machine during situations of sudden peaks in query trafic to reduce the load on the search nodes. The experimental evaluation based on actual traces from users of a major search engine, shows that the proposed method outperforms alternative strategies. This is more evident for systems composed of a large number of search nodes which indicates that the method is also more scalable than the alternative strategies.
The saturation strategy for symbolic state-space generation is particularly effective for globallyasynchronous locally-synchronous systems. A distributed version of saturation, Saturation NOW, uses the overall memory ...
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The saturation strategy for symbolic state-space generation is particularly effective for globallyasynchronous locally-synchronous systems. A distributed version of saturation, Saturation NOW, uses the overall memory available on a network of workstations to effectively spread the memory load, but its execution is essentially sequential. To achieve true parallelism, we explore a speculative firing prediction, where idle workstations work on predicted future event firing requests. A naive approach where all possible firings may be explored a priori, given enough idle time, can result in excessive memory requirements. Thus, we introduce a history-based approach for firing prediction that recognizes firing patterns and explores only firings conforming to these patterns. Experiments show that our heuristic improves the runtime and has a small memory overhead. The saturation strategy for symbolic state-space generation is particularly effective for globallyasynchronous locally-synchronous systems. A distributed version of saturation, SaturationNOW, uses the overall memory available on a network of workstations to effectively spread the memory load, but its execution is essentially sequential. To achieve true parallelism, we explore a speculative firing prediction, where idle workstations work on predicted future event firing requests. A naive approach where all possible firings may be explored a priori, given enough idle time, can result in excessive memory requirements. Thus, we introduce a history-based approach for firing prediction that recognizes firing patterns and explores only firings conforming to these patterns. Experiments show that our heuristic improves the runtime and has a small memory overhead.
A new class of interconnection networks, the hypernetworks, have been proposed recently. Hypernetworks are characterized by hypergraphs. Compared with point-to-point networks, they allow for increased resource-sharing...
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A new class of interconnection networks, the hypernetworks, have been proposed recently. Hypernetworks are characterized by hypergraphs. Compared with point-to-point networks, they allow for increased resource-sharing and communication bandwidth utilization, and they are especially suitable for optical interconnects. One way to derive a hypernetwork is by finding the dual of a point-to-point network. Hypercube Qn, where n is the dimension, is a very popular point-to-point network. In this article, we consider using the dual Q*n of hypercube of Qn as an interconnection network. We investigate the properties of Q*n, and present a set of fundamental data communication algorithms for Q*n. Our results indicate that hypernetwork Q*n is a useful and promising interconnection structure for high-performance parallel and distributed computing systems.
The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be repre...
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The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be represented as dynamic,self-evolving and distributed social *** approach allows us to find important solutions on a holistic level by spatial navigation and parallel pattern matching of social networks with active self-propagating scenarios represented in a special recursive *** approach effectively hides inside the distributed and networked language implementation traditional system management routines,often providing hundreds of times shorter and simpler high-level solution *** paper highlights the demands to efficient simulation of social systems,briefs the technology used,and provides some programming examples for solutions of practical problems.
In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an indu...
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In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones.
Dynamic task assignment and migration are the key technique to load balancing which plays an important role in the achievement of high performance in distributedcomputing system. In this paper, we describe the design...
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Dynamic task assignment and migration are the key technique to load balancing which plays an important role in the achievement of high performance in distributedcomputing system. In this paper, we describe the design and implementation of an online thread scheduling and migration system (S&M) based on a previous work of LWP -MPI. Experimental results show that performance is enhanced.
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