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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

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A Novel parallel Jobs Scheduling Algorithm in the Cloud Computing

A Novel Parallel Jobs Scheduling Algorithm in The Cloud Comp...

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international econference on Computer and Knowledge Engineering (ICCKE)

作者： Zahra Mohtajollah Fazlollah Adibnia Department of Computer Engineering Yazd University Yazd Iran

Cloud Computing is a computational model that provides all computing services and its requirements over the Internet. So our computation is always available without burdens of carrying large-scale hardware and software. the utilization of resources has been decreasing due to the growth of parallel processing in most parallel applications. Accordingly, job scheduling, one of the fundamental issues in cloud computing, should manage more efficiently. the accuracy of parallel job scheduling is greatly important for cloud providers in order to guarantee the quality of their service. Given that optimal scheduling improves utilization of resources, reduces response time and satisfies user requirements. Most of the current parallel job scheduling algorithms do not use the consolidation of parallel workloads to improve scheduling performance. this paper introduces a scheduling algorithm enriches the powerful ACFCFS algorithm. To begin with, we employ tentative runs, workload consolidation and two-tier virtual machines architecture. Particularly, we consider deadline for jobs in order to prevent starvation of parallel jobs and improve performance. the simulation results indicate that our algorithm considerably reduces the makespan and the maximum waiting time. therefore it improves scheduling compare to the basic algorithm (ACFCFS). Overall, it can be employed as a strong and effective method for scheduling parallel jobs in the cloud.

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Fast and parallel Computation of the Discrete Periodic Radon Transform on GPUs, multi-core CPUs and FPGAs 25

Fast and Parallel Computation of the Discrete Periodic Radon...

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25th IEEE international conference on Image processing (ICIP)

作者： Carranza, Cesar Pattichis, Marios Llamocca, Daniel Pontificia Univ Catolica Peru Secc Elect & Elect Lima 32 Peru Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Oakland Univ Dept Elect & Comp Engn Rochester MI 48063 USA

ISBN: (纸本)9781479970612

the Discrete Periodic Radon Transform (DPRT) has many important applications in reconstructing images from their projections and has recently been used in fast and scalable architectures for computing 2D convolutions. Unfortunately, the direct computation of the DPRT involves O(N-3) additions and memory accesses that can be very costly in single-core architectures. the current paper presents new and efficient algorithms for computing the DPRT and its inverse on multi-core CPUs and GPUs. the results are compared against specialized hardware implementations (FPGAs/ASICs). the results provide significant evidence of the success of the new algorithms. On an 8-core CPU (Intel Xeon), with support for two threads per core, FastDirDPRT and FastDirInvDPRT achieve a speedup of approximately 10x (up to 12:83x) over the single-core CPU implementation. On a 2048-core GPU (GTX 980), FastRayDPRT and FastRayInvDPRT achieve speedups in the range of 526 (for 127 x 127) to 873 (for 1021 x 1021), which approximate ideal speedups of what can be achieved. the DPRT can be computed exactly and in real-time (30 frames per second) for 1471x1471 images using FastRayDPRT on the GPU. Furthermore, the GPU algorithms approximate the performance of an efficient FPGA implementation using 2N parallel cores at 100MHz.

关键词： Discrete Periodic Radon Transform parallel Architecture Multi-core CPU GPU FPGA

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Machine scheduling with stochastic release and processing times 9

Machine scheduling with stochastic release and processing ti...

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9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Liu, Ming Liu, Xin Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

Rapid development of technology provides the foundation for the fourth industrial revolution, namely Industry 4.0. the ability to cope with unpredictable and negative events is becoming increasingly important under Industry 4.0 settings. As scheduling plays an important role in the industry system, stochastic scheduling is therefore a rather hot and interesting research topic. Most existing works assume that the job processing times are stochastic, and rarely consider the release time uncertainty. However, in practice, job release times are usually stochastic as well, due to the various factors. this work considers a parallel machine scheduling problem, in which the processing times and release times are both uncertain. the objective is to minimize the expected total weighted earliness and tardiness in a Just-in-Time (JIT) mode. For the problem, a two-stage stochastic programming formulation is first proposed, and then a classic sample average approximation (SAA) and an improved SAA, combined with scenario reduction, are developed. A case study is conducted to illustrate the applicability of the proposed methods. (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： parallel machine scheduling Stochastic optimization Uncertain processing times Uncertain release times

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Designing Artificial Cognitive architectures: Brain Inspired or Biologically Inspired? 9

Designing Artificial Cognitive Architectures: Brain Inspired...

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9th Annual international conference of the Biologically-Inspired-Cognitive-architectures-Society (BICA) held as part of the Joint Multi-conference on Human-Level Artificial Intelligence (HLAI)

作者： Diamant, Emanuel VIDIA Mant POB 933 Kiriat Ono Israel

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence (AI) design implementations. However, it was soon became obvious that they are unable to fulfil their duties. the fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures. (C) 2018 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual international conference on Biologically Inspired Cognitive architectures.

关键词： Artificial Neural Networks Artificial Intelligence Translational Neurobiology cognitive modeling information duality cognitive information processing

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15th international Symposium on Applied Reconfigurable Computing, ARC 2019

15th International Symposium on Applied Reconfigurable Compu...

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15th international Symposium on Applied Reconfigurable Computing, ARC 2019

ISBN: (纸本)9783030172268

the proceedings contain 28 papers. the special focus in this conference is on Applied Reconfigurable Computing. the topics include: Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan;secure Local Configuration of Intellectual Property Without a Trusted third Party;HiFlipVX: An Open Source High-Level Synthesis FPGA Library for Image processing;Real-Time FPGA Implementation of Connected Component Labelling for a 4K Video Stream;A Scalable FPGA-Based Architecture for Depth Estimation in SLAM;Evaluating LULESH Kernels on OpenCL FPGA;the TaPaSCo Open-Source Toolflow for the Automated Composition of Task-Based parallel Reconfigurable Computing Systems;Graph-Based Code Restructuring Targeting HLS for FPGAs;UltraSynth: Integration of a CGRA into a Control Engineering Environment;exploiting Reconfigurable Vector processing for Energy-Efficient Computation in 3D-Stacked Memories;Optimizing CNN-Based Hyperspectral Image Classification on FPGAs;Automatic Toolflow for VCGRA Generation to Enable CGRA Evaluation for Arithmetic algorithms;reM: A Reconfigurable Multipotent Cell for New Distributed Reconfigurable architectures;Update or Invalidate: Influence of Coherence Protocols on Configurable HW Accelerators;hybrid Prototyping for Manycore Design and Validation;Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks;third Party CAD Tools for FPGA Design—A Survey of the Current Landscape;Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation;Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs;Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning;Supporting Columnar In-memory Formats on FPGA: the Hardware Design of Fletcher for Apache Arrow;A Novel Encoder for TDCs.

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How Language processing can Shape a Common Model of Cognition 9

How Language Processing can Shape a Common Model of Cognitio...

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9th Annual international conference of the Biologically-Inspired-Cognitive-architectures-Society (BICA) held as part of the Joint Multi-conference on Human-Level Artificial Intelligence (HLAI)

作者： Kelly, Mary Alexandria Reitter, David Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

What role does the study of natural language play in the task of developing a unified theory and common model of cognition? Language is perhaps the most complex behaviour that humans exhibit, and, as such, is one of the most difficult problems for understanding human cognition. Linguistic theory can both inform and be informed by unified models of cognition. We discuss (1) how computational models of human cognition can provide insight into how humans produce and comprehend language and (2) how the problem of modelling language processing raises questions and creates challenges for widely used computational models of cognition. Evidence from the literature suggests that behavioural phenomena, such as recency and priming effects, and cognitive constraints, such as working memory limits, affect how language is produced by humans in ways that can be predicted by computational cognitive models. But just as computational models can provide new insights into language, language can serve as a test for these models. For example, simulating language learning requires the use of more powerful machine learning techniques, such as deep learning and vector symbolic architectures, and language comprehension requires a capacity for on-the-fly situational model construction. In sum, language plays an important role in both shaping the development of a common model of the mind, and, in turn, the theoretical understanding of language stands to benefit greatly from the development of a common model. (C) 2018 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual international conference on Biologically Inspired Cognitive architectures.

关键词： natural language processing cognitive architectures cognitive models common model of cognition human memory

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parallel High Average-Utility Itemset Mining Using Better Search Space Division Approach 15th

Parallel High Average-Utility Itemset Mining Using Better Se...

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15th international conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Sethi, Krishan Kumar Ramesh, Dharavath Sreenu, M. Indian Inst Technol ISM Dhanbad Dhanbad 826004 Jharkhand India Ashoka Inst Engn & Technol Hyderabad 500082 Telangana India

ISBN: (纸本)9783030053666;9783030053659

Since the last decade, High Utility Itemset (HUI) mining has emerged as a popular pattern mining approach. HUI mining discovers a set of itemset with their profit more than a user defined profit threshold. High Average-Utility Itemset (HAUI) mining is an improvement over HUI mining that involves the length of items to refine the patterns and keep a fair mining process. In the era of big data, traditional HAUI mining algorithms are not suitable to process large transaction dataset on standalone system due to limitation of processing resources. therefore, several distributed frameworks have been developed to process big data on cluster of commodity hardwares. this paper presents a parallel version of the traditional HAUI-Miner algorithm and names it as parallel High-Average Utility Itemset Miner (PHAUIM). PHAUIM is a Spark-based distributed algorithm which splits the dataset into multiple chunks and distributes on cluster nodes to process each data chunk in parallel. In addition, an improved approach for search space division is developed. Proposed search space division technique fairly assigns the workload to each node and upgrades the performance. Comprehensive experiments have been performed to measure the performance of PHAUIM in terms of speedup and data scalability. PHAUIM is also compared with traditional HAUIM.

关键词： High average-utility itemset mining Big data Apache-Spark Search space

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Accelerating FM-index Search for Genomic Data processing 18

Accelerating FM-index Search for Genomic Data Processing

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47th international conference on parallel processing (ICPP)

作者： Wang, Yuanrong Li, Xueqi Zang, Dawei Tan, Guangming Sun, Ninghui Univ Chinese Acad Sci CAS ICT SKL Comp Architecture Beijing Peoples R China Chinese Acad Sci SKL Comp Architecture ICT Beijing Peoples R China

ISBN: (纸本)9781450365109

the deluge of genomics data is incurring prohibitively high computational costs. As an important building block for genomic data processing algorithms, FM-index search occupies most of execution time in sequence alignment. Due to massive random streaming memory references relative to only small amount of computations, FM-index search algorithm exhibits extremely low efficiency on conventional architectures. this paper proposes Niubility, an accelerator for FM-index search in genomic sequence alignment. Based on our algorithm-architecture co-design analysis, we found that conventional architectures exploit low memory-level parallelism so that the available memory bandwidth cannot be fully utilized. Niubility accelerator customizes bit-wise operations and exploit data-level parallelism, that produces maximal concurrent memory accesses to saturate memory bandwidth. We implement an accelerator ASIC in a ST 28nm process that achieves up to 990x speedup over the state-of-the-art software.

关键词： Energy efficiency Hardware-acceleration Genome alignment

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WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture 9

WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architectu...

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9th Annual international conference of the Biologically-Inspired-Cognitive-architectures-Society (BICA) held as part of the Joint Multi-conference on Human-Level Artificial Intelligence (HLAI)

作者： Mishra, Amit Kumar Univ Cape Town Cape Town South Africa

Neuroethology has been an active field of study for more than a century now. Out of some of the most interesting species that has been studied so far, weakly electric fish is a fascinating one. It performs communication, echo-location and inter-species detection efficiently with an interesting configuration of sensors, neurons and a simple brain. In this paper we propose a cognitive architecture inspired by the way these fishes handle and process information. We believe that it is easier to understand and mimic the neural architectures of a simpler species than that of human. Hence, the proposed architecture is expected to both help research in cognitive robotics and also help understand more complicated brains like that of human beings. (C) 2018 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual international conference on Biologically Inspired Cognitive architectures.

关键词： Cognitive architecture weakly electric fish information processing Neuroethology

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A Malleable Vectorized Auction Algorithm for Modern Multicore architectures 20

A Malleable Vectorized Auction Algorithm for Modern Multicor...

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20th IEEE international conference on High Performance Computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Sena, A. C. Marzulo, L. A. J. Nascimento, A. P. Vasconcelos, C. N. Univ Estado Rio De Janeiro Inst Matemat & Estat Rio De Janeiro Brazil Univ Fed Fluminense Inst Comp Niteroi RJ Brazil

ISBN: (纸本)9781538666142

the auction algorithm has been widely used to solve the bipartite graph matching problem and its parallel implementation is employed to find solutions in a reasonable computational time. Moreover, the new multicore architectures, besides its various cores, have a SIMD instruction set that can increase application performance when exactly the same operations are to be performed on multiple data objects. the aim of this paper is to efficiently execute the auction algorithm on these architectures. To achieve that, a vectorized version was implemented and evaluated. these versions were then run in parallel using the OpenMP library. Finally, to optimize the number of threads used during the execution, a malleable strategy is proposed and evaluated. Results show that the vectorized version outperforms the sequential one by a factor of 10, while the malleable vectorized version was able to adapt its execution to exploit the full potential of multicore architectures.

关键词： Multicore processing Heuristic algorithms Program processors Indexes Bipartite graph conferences

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