The computing power provided by high performance low-cost PC-based Cluster and Grid platforms are attractive, and they are equal or superior to supercomputers and mainframes widely available. In this research paper, w...
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The authors study the parallel implementation of a traditional frame-based knowledge representation system for a general-purpose massively parallel hypercube architecture (such as the Connection Machine). It is shown ...
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The authors study the parallel implementation of a traditional frame-based knowledge representation system for a general-purpose massively parallel hypercube architecture (such as the Connection Machine). It is shown that, using a widely available parallel system (instead of a special-purpose architecture), it is possible to provide multiple users with efficient shared access to a large-scale knowledge-base. parallel algorithms are presented for answering multiple inference assert, and retract queries on both single and multiple inheritance hierarchies. In addition to theoretical time complexity analysis, empirical results obtained from extensive testing of a prototype implementation are presented.< >
Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations'...
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ISBN:
(纸本)9781479915194
Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations' throughput will bound the system performance to storage and retrieval operations and remarkably reduce the overall performance of high performance computing clusters. File replication is a way to improve the performance of I/O operations and increase network utilization by storing several copies of every file. Furthermore, this will lead to a more reliable and fault-tolerant storage cluster. In order to improve the response time of I/O operations, we have proposed a mechanism that estimates the required number of replicas for each file based on its popularity. Besides that, the remaining space of storage cluster is considered in the evaluation of replication factors and the number of replicas is adapted to the storage state. We have implemented the proposed mechanism using HDFS and evaluated it using MapReduce framework. Evaluation results prove its capability to improve the response time of read operations and increase network utilization. Consequently, this mechanism reduces the overall response time of read operations by considering files' popularity in replication process and adapts the replication factor to the cluster state.
In deep sub-micron fabrication technology, clock skew is one of the dominant factors which determine system performance. Previous works in zero skew clock tree routing assume that the wires have uniform size, and prev...
ISBN:
(纸本)9781581130218
In deep sub-micron fabrication technology, clock skew is one of the dominant factors which determine system performance. Previous works in zero skew clock tree routing assume that the wires have uniform size, and previous wire-sizing algorithms for general signal nets do not produce the exact zero skew. In this paper, we first propose an algorithm to get the exact zero skew wire-sizing by using an iterative method to make the wire size improvement. Our experiments on benchmark clock trees show that the algorithm reduces the source sink delay more than 3 times that of the clock trees with uniform wire sizes and keeps the clock skew zero. Motivated by the computation intensive nature of the zero skew clock tree construction and wire-sizing, we propose a parallel algorithm using a cluster-based clock tree construction algorithm and our zero skew wire-sizing algorithm. Without sacrificing the quality of the solution, on the average we obtain speedups of 7.8 from the parallel clustering based clock tree construction algorithm on an 8 processor SUN SPARC Server 1000E shared memory multi-processor.
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candida...
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High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be usefu...
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High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too.
Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing ...
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ISBN:
(纸本)9781479954711
Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing model and unknown underlying configuration of the system, are contrary to the basic assumptions of available benchmarks such as TPC-W or RUBiS. In this paper, we introduce a systematic performance benchmark approach for OLTP applications on public clouds that use virtual machines(VMs). We propose WPress benchmark, which is based on the widespread blogging software, WordPress, as a representative OLTP application and implement an open source workload generator. Furthermore, we utilize a CPU micro-benchmark to investigate CPU performance of cloud-based VMs in greater detail. Average response time and total VM cost are the performance metrics measured by WPress. We evaluate small and large instance types of three real-life cloud providers, Amazon EC2, Microsoft Azure and Rackspace cloud. Results imply that Rackspace cloud has better average response times and total VM cost on small instances. However, Microsoft Azure is preferable for large instance type.
Finitely inductive (F1) sequences are a class of sequences, finite or infinite, which are amenable to a certain mathematical representation which has direct significance to pattern recognition and string matching. Pat...
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With the increasing number of scientific applications manipulating huge amounts of data, effective data management is an increasingly important problem. Unfortunately, so far the solutions to this data management prob...
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With the increasing number of scientific applications manipulating huge amounts of data, effective data management is an increasingly important problem. Unfortunately, so far the solutions to this data management problem either require deep understanding of specific storage architectures and file layouts (as in high-performance file systems) or produce unsatisfactory I/O performance in exchange for ease-of-use and portability (as in relational DBMSs). In this paper we present a new environment which is built around an active meta-data management system (MDMS). The key components of our three-tiered architecture are user application, the MDMS, and a hierarchical storage system (HSS). Our environment overcomes the performance problems of pure database-oriented solutions, while maintaining their advantages in terms of ease-of-use and portability. The high levels of performance are achieved by the MDMS, with the aid of user-specified directives. Our environment supports a simple, easy-to-use yet powerful user interface, leaving the task of choosing appropriate I/O techniques to the MDMS. We discuss the importance of an active MDMS and show how the three components, namely application, the MDMS, and the HSS, fit together. We also report performance numbers from our initial implementation and illustrate that significant improvements are made possible without undue programming effort.
Evidence and protocol based medicine decreases the complexity and in the same time also standardizes the healing process. Intervention descriptions moderately open for the public, and they differ more or less at every...
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Evidence and protocol based medicine decreases the complexity and in the same time also standardizes the healing process. Intervention descriptions moderately open for the public, and they differ more or less at every medical service provider. Normally patients are not much familiar about the steps of the intervention process. There is a certain need expressed by patients to view the whole healing process through intervention plans, thus they can prepare themselves in advance to the coming medical interventions. Intervention plan tracking is a game changer for practitioners too, so they can follow the clinical pathway of the patients, and can receive objective feedbacks from various sources about the impact of the services. Resource planning (with time, cost and other important parameters) and resource pre-allocation became feasible tasks in the healthcare sector. The evolution of consensus protocols developed by medical professionals and practitioners requires accurate measurement of the difference between plans and real world scenarios. To support these comparisons we have developed the Intervention Process Analyzer and Explorer software solution. This software solution enables practitioners and healthcare managers to review in an objective way the effectiveness of interventions targeted at health care professionals and aimed at improving the process of care and patient outcomes.
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