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An efficient and fast polarity optimization approach for mixed polarity Reed-Muller logic circuits

Frontiers of computer science 2017年第4期11卷 728-742页

作者： Zhenxue HE Limin XIAO Fei GU Tongsheng XIA Shubin SU Zhisheng HUO Rong ZHANG Longbing ZHANG Li RUAN Xiang WANG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100191 China

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.

关键词： genetic algorithm polarity optimization mixed

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Parallelizing a machine translation decoder for multicore computer

Parallelizing a machine translation decoder for multicore co...

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2011 7th International Conference on Natural Computation, ICNC 2011

作者： Chen, Long Huo, Wei Mi, Haitao Zhang, Zhaoqing Feng, Xiaobing Li, Zhiyuan Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science Purdue University West Lafayette IN 47907 United States

ISBN: (纸本)9781424499533

Machine translation (MT), with its broad potential use, has gained increased attention from both researchers and software vendors. To generate high quality translations, however, MT decoders can be highly computation intensive. With significant raw computing power, multi-core microprocessors have the potential to speed up MT software on desktop machines. However, retrofitting existing MT decoders is a nontrivial issue. Race conditions and atomicity issues are among those complications making parallelization difficult. In this article, we show that, to parallelize a state-of-the-art MT decoder, it is much easier to overcome such difficulties by using a process-based parallelization method, called functional task parallelism, than using conventional thread-based methods. We achieve a 7.60 times speed up on an 8-core desktop machine while making significantly less changes to the original sequential code than required by using multiple threads. © 2011 IEEE.

关键词： Machine translation

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MAP-numa: Access patterns used to characterize the NUMA memory access optimization techniques and algorithms

MAP-numa: Access patterns used to characterize the NUMA memo...

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9th IFIP International Conference on Network and Parallel Computing, NPC 2012

作者： Luo, Qiuming Liu, Chenjian Kong, Chang Cai, Ye Shenzhen China College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642356056

Some typical memory access patterns are provided and programmed in C, which can be used as benchmark to characterize the various techniques and algorithms aim to improve the performance of NUMA memory access. These access patterns, called MAP-numa (Memory Access Patterns for NUMA), currently include three classes, whose working data sets are corresponding to 1-dimension array, 2-dimension matrix and 3-dimension cube. It is dedicated for NUMA memory access optimization other than measuring the memory bandwidth and latency. MAP-numa is an alternative to those exist benchmarks such as STREAM, pChase, etc. It is used to verify the optimizations' (made automatically/manually to source code/executive binary) capacities by investigating what locality leakage can be remedied. Some experiment results are shown, which give an example of using MAP-numa to evaluate some optimizations based on Oprofile sampling. © IFIP International Federation for Information Processing 2012.

关键词： Benchmarking

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Building Agile Workflow Microservice System for HPC Applications Based on Fast-start OSv 27

Building Agile Workflow Microservice System for HPC Applicat...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Hu, Qiang Ma, Lixian Shao, En Tan, Guangming Institute of Computing Technology CAS State Key Laboratory of Computer Architecture Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665408783

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker. © 2021 IEEE.

关键词： Containers

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INVESTIGATION ON STABILITY OF PZT/EPOXY PIEZOCOMPOSITE WITH PRESSURE AND TEMPERATURE

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Acta Mechanica Solida Sinica 2009年第3期22卷 226-231页

作者： Likun Wang Yuanyuan Wan Li Li Lei Qin Yi Lu Beijing Key Laboratory for Sensor Beijing Information Science and Technology University Beijing 100101 China Computer Science and Technology Department Shenyang Institute of Chemical Technology 11 Economic Development District Shenyang 110142 China The Department of Foreign Languages Teaching Yunnan University Kunming 650091 China

With the universal test machine and temperature test chamber, the effect of pressure and temperature on the behavior of 1-3 and 1-3-2 piezocomposites prepared with PZT/epoxy in our lab has been investigated through electrical impedance and dielectric analysis. The permittivity, resonant frequency and dielectric loss of the composites have been measured under vertical stress and temperature, and the variety of the material properties under pressure and temperature could also be obtained. The pressure and temperature stability of the composites have been assessed through experiments. The data of these two types of composites indicated that the stability of 1-342 composite with pressure was better than that of 1-3 composite, and two composites have almost the same dielectric stability with temperature. However, 1-3-2 composite showed slower frequency variation than 1-3 composite.

关键词： piezoelectric composite directional pressure temperature stability

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DUAL-STEP OPTIMIZATION FOR BINARY SEQUENCES WITH HIGH MERIT FACTORS

arXiv

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arXiv 2024年

作者： Pšeničnik, Blaž Mlinarič, Rene Brest, Janez Bošković, Borko Computer Architecture and Languages Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

MSC Codes 68T20The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a novel dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified new best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. © 2024, CC BY.

关键词： Stochastic systems

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Deceptive answer prediction with user preference graph

Deceptive answer prediction with user preference graph

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Li, Fangtao Gao, Yang Zhou, Shuchang Si, Xiance Dai, Decheng Google Research Mountain View Institute of Computing Technology CAS China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781937284503

In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning methods. Two kinds of features, including textual and contextual features, are investigated for this task. We further propose to exploit the user relationships to identify the deceptive answers, based on the hypothesis that similar users will have similar behaviors to post deceptive or authentic answers. To measure the user similarity, we propose a new user preference graph based on the answer preference expressed by users, such as "helpful" voting and "best answer" selection. The user preference graph is incorporated into traditional supervised learning framework with the graph regularization technique. The experiment results demonstrate that the user preference graph can indeed help improve the performance of deceptive answer prediction. © 2013 Association for Computational Linguistics.

关键词： Supervised learning

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Extendable pattern-oriented optimization directives

Extendable pattern-oriented optimization directives

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作者： Cui, Huimin Xue, Jingling Wang, Lei Yang, Yang Feng, Xiaobing Fan, Dongrui Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Institute of Computing Technology Beijing China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

Algorithm-specific, that is, semantic-specific optimizations have been observed to bring significant performance gains, especially for a diverse set of multi/many-core architectures. However, current programming models and compiler technologies for the state-of-the-art architectures do not exploit well these performance opportunities. In this article, we propose a pattern-making methodology that enables algorithm-specific optimizations to be encapsulated into "optimization patterns". Such optimization patterns are expressed in terms of preprocessor directives so that simple annotations can result in significant performance improvements. To validate this new methodology, a framework, named EPOD, is developed to map these directives into the underlying optimization schemes for a particular architecture. It is difficult to create an exact performance model to determine an optimal or near-optimal optimization scheme (including which optimizations to apply and in which order) for a specific application, due to the complexity of applications and architectures. However, it is trackable to build individual optimization components and let compiler developers synthesize an optimization scheme from these components. Therefore, our EPOD framework provides an Optimization Programming Interface (OPI) for compiler developers to define new optimization schemes. Thus, new patterns can be integrated into EPOD in a flexible manner. We have identified and implemented a number of optimization patterns for three representative computer platforms. Our experimental results show that a pattern-guided compiler can outperform the state-of-theart compilers and even achieve performance as competitive as hand-tuned code. Therefore, such a patternmaking methodology represents an encouraging direction for domain experts' experience and knowledge to be integrated into general-purpose compilers. © 2012 ACM.

关键词： Semantics

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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice-based system

DRS: A deep reinforcement learning enhanced Kubernetes sched...

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作者： Jian, Zhaolong Xie, Xueshuo Fang, Yaozheng Jiang, Yibing Lu, Ye Dash, Ankan Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Department of Computer Science New Jersey Institute of Technology NewarkNJ United States

Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud-native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube-scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube-scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube-scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average. © 2023 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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NDANN: Efficient SSD-Based Approximate Nearest Neighbor Search through Navigation

NDANN: Efficient SSD-Based Approximate Nearest Neighbor Sear...

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2022 International Conference on Mechanisms and Robotics, ICMAR 2022

作者： Yang, Kaixiang Wang, Hongya Tan, Zongyuan Zhang, Jie College of Computer Science and Technology Donghua University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Institute of Artificial Intelligence Donghua University Shanghai China

ISBN: (纸本)9781510657328

Graph-based Approximate Nearest Neighbor Search (ANNS) algorithms have attracted more attention due to their better performance. Numerous ANNS optimization methods have been proposed by researchers. However, Current graph-based ANNS algorithms still cannot index billion-scale datasets on a single server with 256GB RAM. Although several researchers have investigated on this problem, we believe there is still an improvement in reducing disk accesses. In this paper, we provide a fast navigation layer to decrease the number of disk accesses by assisting query points in swiftly reaching the range of strongly connected components. Compared with the state-of-the-art ANNS algorithms, NDANN reduces the mean latency by about 20% under the same recall. © 2022 SPIE.

关键词： Graphic methods

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