The influence of on-chip metal interconnections, power grids, heat sink together with packaging, and metal dummy fills on the transmission characteristics of a 2mm-long integrated dipole antenna pair has been investig...
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Nowadays, the demand for software resources on different granularity is becoming prominent in software engineering field. However, a large quantity of heterogeneous software resources have not been organized in a reas...
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Current high performance computing systems all rely on parallelprocessing techniques to achieve high performance. With the parallelcomputer systems scaling up, the new generation of high performance computers puts m...
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ISBN:
(纸本)9781605580777
Current high performance computing systems all rely on parallelprocessing techniques to achieve high performance. With the parallelcomputer systems scaling up, the new generation of high performance computers puts more emphasis on "high productivity" [1], rather than "high performance" as in the past. These new systems will not only meet the traditional requirements of computing performance, but also address the ongoing technical challenges in the current high-end computing domain regarding energy consumption, reliability, etc. For energy consumption, with the computer system scaling up, it increases dramatically [2]. High energy consumption means high maintenance cost and low system stability. For example, the peak energy consumption of the Earth Simulator and BlueGene/L is 18MW and 1.6MW respectively. For reliability, with the complexity of a computer system increasing, its meantimebetween-failure (MTBF) is becoming significantly shorter than what is required by many current high performance computing applications [3], such as BlueGene/L. Therefore, energy optimization techniques and fault tolerance techniques should be introduced to computer systems to achieve low energy consumption and high reliability. To improve the productivity of high performance computing systems, we need to find a proper way to measure it. Unfortunately, traditional measurement models can not evaluate the system productivity comprehensively and effectively [4]. To address this issue, this paper proposes an effective scalability metric for high performance computing systems based on Gustafson speedup law. The metric makes a good balance among runtime productivity factors including computing performance, energy consumption and reliability. The contribution of our work lies in the following three aspects. First, in order to measure the scalability of an energyconsumption optimized parallel program, we should consider not only whether the program computing performance is scalable, but also w
Single-electronic transistors (SETs) are considered as the attractive candidates for post-COMS VLSI due to their ultra-small size and low power consumption. Because SETs with single island can not work at room tempera...
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ISBN:
(纸本)9781424435432
Single-electronic transistors (SETs) are considered as the attractive candidates for post-COMS VLSI due to their ultra-small size and low power consumption. Because SETs with single island can not work at room temperature normally, more and more researchers begin to make research on the SETs with 1-dimension multi-islands. A new simulation method-nSET, is introduced in this paper Compared with other methods, nSET can simulate the SET device with 1-Dimension multiple islands with high speed and accuracy. Through the comparison, it can be get that nSET is accurate and fast compared with the classical Monte Carlo(MC) simulator, and is very useful for the ASIC design of SET devices.
Stragglers can temporize jobs and reduce cluster efficiency seriously. Many researches have been contributed to the solution, such as Blacklist[8], speculative execution[1, 6], Dolly[8]. In this paper, we put forward ...
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Wireless sensor networks (WSN) is a critical technology for information gathering covering many areas, including health-care, transportation, air traffic control and environment monitoring. Despite wide use, the fast ...
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Code review is an important process to reduce code defects and improve software quality. However, in social coding communities using the pull-based model, everyone can submit code changes, which increases the required...
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In this paper, we propose an example-based decoder for a statistical machine translation (SMT) system, which is used for spoken language machine translation. In this way, it will help to solve the re-ordering problem ...
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To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today's production environments. Much effort has been devoted to the problem of meeting deadlines...
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To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today's production environments. Much effort has been devoted to the problem of meeting deadlines, and typically there exist two kinds of solutions. The first is to allocate appropriate resources to complete the entire job before the specified time limit, where missed deadlines result because of tight deadline constraints or lack of resources; the second is to run a pre-constructed sample based on deadline constraints, which can satisfy the time requirement but fail to maximize the volumes of processed data. In this paper, we propose a deadline-oriented task scheduling approach, named 'Dart', to address the above problem. Given a specified deadline and restricted resources, Dart uses an iterative estimation method, which is based on both historical data and job running status to precisely estimate the real-time job completion time. Based on the estimated time, Dart uses an approach-revise algorithm to make dynamic scheduling decisions for meeting deadlines while maximizing the amount of processed data and mitigating stragglers. Dart also efficiently handles task failures and data skew, protecting its performance from being harmed. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 64 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximum volumes of data even with tight deadlines and limited resources.
As the rapid growth of open source software, how to choose software from many alternatives becomes a great challenge. Traditional ranking approaches mainly focus on the characteristics of the software themselves, such...
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