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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是251-260 订阅

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A Framework for Efficient and Convenient Evaluation of Trajectory compression algorithms

A Framework for Efficient and Convenient Evaluation of Traje...

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International Conference on Computing for Geospatial Research and Application (COM.Geo)

作者： Jonathan Muckell Paul W. Olsen Jeong-Hyon Hwang S.S. Ravi Catherine T. Lawson Department of Informatics University at Albany The State University of New York Albany NY USA Department of Computer Science University at Albany The State University of New York Albany NY USA Department of Geography and Planning University at Albany The State University of New York Albany NY USA

Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been proposed in the literature, no common benchmarking platform for evaluating their effectiveness exists. This paper presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. This framework supports various compression algorithms and metrics defined in the literature, as well as three synthetic trajectory generators that have different trade-offs. It also has a highly extensible architecture that facilitates the incorporation of new compression algorithms, evaluation metrics, and trajectory data generators. This paper provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this paper describes challenges that arise in the design and implementation of the above framework and our approaches to tackling these challenges. Finally, this paper presents evaluation results that demonstrate the utility of the benchmarking framework.

关键词： Trajectory Measurement compression algorithms Generators Benchmark testing Accuracy Dead reckoning

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Huffman and Lempel-Ziv based data compression algorithms for wireless sensor networks

Huffman and Lempel-Ziv based data compression algorithms for...

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International Conference on Pattern Recognition, Informatics and Medical Engineering

作者： S. Renugadevi P. S. Nithya Darisini School of Electronics Engineering VIT University Vellore India School of Computing Sciences and Engineering VIT University Chennai India

In the recent years, wireless sensor networks have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery powered devices, it is essential to minimize the energy consumption of the nodes so that the network lifetime can be extended. Most of the energy is consumed in the processing and transmission of data. Applying compression algorithms on the wireless sensor data prior to transmission is one of the efficient ways to save energy. In this paper we propose data compression algorithms based on Huffman and Lempel-Ziv techniques and compare the efficiency of different algorithms in a wireless sensor network scenario.

关键词： Huffman coding Wireless sensor networks Algorithm design and analysis Dictionaries compression algorithms

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Instant Quantization of Neural Networks using Monte Carlo Methods 5

Instant Quantization of Neural Networks using Monte Carlo Me...

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5th Workshop on Energy Efficient Machine Learning and Cognitive Computing ((EMC2) / Conference on Neural Information Processing Systems (NIPS)

作者： Mordido, Goncalo Van Keirsbilck, Matthijs Keller, Alexander Hasso Plattner Inst Potsdam Germany NVIDIA Berlin Germany

ISBN: (纸本)9781665424189

Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quantize the weights and activations of pre-trained neural networks without any re-training. By performing importance sampling, we obtain quantized low bit-width integer values from full-precision weights and activations. The precision, sparsity, and complexity are easily configurable by the amount of sampling performed. Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, with the resulting quantized, sparse networks showing minimal accuracy loss when compared to the original full-precision networks. Our method either outperforms or achieves competitive results on CIFAR-10 and ImageNet when compared to previous methods that do require additional training.

关键词： compression algorithms Deep learning Neural networks

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BDD-Based algorithms for Packet Classification 19

BDD-Based Algorithms for Packet Classification

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19th International Conference on Formal Methods in Computer Aided Design (FMCAD)

作者： Narodytska, Nina Ryzhyk, Leonid Ganichev, Igor Sevinc, Soner VMware Res Palo Alto CA 94304 USA Google Mountain View CA 94043 USA Facebook Menlo Pk CA USA

ISBN: (纸本)9780983567899

Packet classifiers are the building blocks of modern networking. A classifier determines the action to take on a packet by matching its header against a set of rules. Efficient classification is achieved by using associative memory to perform the match operation in one clock cycle. This requires compressing large rule sets to fit in the small associative memory space available in modern network switches. We propose two symbolic rule set compression algorithms based on binary decision diagrams. Following McGeer and Yalagandula, we formalize the problem as that of obtaining a sequential cover of the rule set. We develop a simple BDD-based algorithm for computing sequential covers, which significantly outperforms state of the art algorithms in terms of compression ratio-a surprising result that highlights the unexplored potential of symbolic techniques in packet classification. Despite this improvement, very large industrial classifiers are still beyond reach. We decompose such classifiers into a pipeline of smaller classifiers over subsets of packet header fields. We then compress each classifier using the sequential cover technique. Our algorithm is able to compress industrial rule sets with hundreds of thousands rules to readily fit in the memory of network switches.

关键词： compression algorithms Associative memory Clocks Pipelines IP networks Boolean functions Encoding

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Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations

Analyzing the Impact of Lossy Compressor Variability on Chec...

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IEEE International Conference on Cluster Computing (IEEE CLUSTER)

作者： Triantafyllides, Pavlo Reza, Tasmia Calhoun, Jon C. Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (纸本)9781728147345

Lossy compression algorithms are effective tools to reduce the size of high-performance computing data sets. As established lossy compressors such as SZ and ZFP evolve, they seek to improve the compression/decompression bandwidth and the compression ratio. Algorithm improvements may alter the spatial distribution of errors in the compressed data even when using the same error bound and error bound type. If HPC applications are to compute on lossy compressed data, application users require an understanding of how the performance and spatial distribution of error changes. We explore how spatial distributions of error, compression/decompression bandwidth, and compression ratio change for HPC data sets from the applications PlasComCM and Nek5000 between various versions of SZ and ZFP. In addition, we explore how the spatial distribution of error impacts application correctness when restarting from lossy compressed checkpoints. We verify that known approaches to selecting error tolerances for lossy compressed checkpointing are robust to compressor selection and in the face of changes in the distribution of error.

关键词： Bandwidth compression algorithms Checkpointing Standards Graphical models Distribution functions Numerical models

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RePair in Compressed Space and Time

RePair in Compressed Space and Time

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Data compression Conference (DCC)

作者： Sakai, Kensuke Ohno, Tatsuya Goto, Keisuke Takabatake, Yoshimasa Tomohiro, I Sakamoto, Hiroshi Kyushu Inst Technol Kitakyushu Fukuoka Japan Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781728106571

Given a string T of length N, the goal of grammar compression is to construct a small context-free grammar generating only T. Among existing grammar compression methods, RePair (recursive paring) [Larsson and Moffat, 1999] is notable for achieving good compression ratios in practice. In this paper, we propose the first RePair algorithm working in compressed space, i.e., potentially o(N) space for highly compressible texts. The key idea is to give a new way to restructure an arbitrary (context-free) grammar S for T into RePair(T) in compressed space and time. We propose an algorithm for RePair(T) running in O(min(N, nm log N)) space and expected O(min(N, nm log N) m) time or O(min(N, nm log N) log log N) time, where n is the size of S and m is the number of variables in RePair(T). We implemented our O(min(N, nm log N) m)-time algorithm and show it can actually run in compressed space. We also present a new approach to reduce the peak memory usage of existing RePair algorithms combining with our algorithms, and show that the new approach outperforms, both in computation time and space, the most space efficient linear-time RePair implementation to date.

关键词： Maintenance engineering Grammar compression algorithms Production Time complexity Frequency measurement

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Parallel I/O on Compressed Data Files: Semantics, algorithms, and Performance Evaluation

Parallel I/O on Compressed Data Files: Semantics, Algorithms...

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Siddhesh Pratap Singh Edgar Gabriel Parallel Software Technologies Laboratory University of Houston Houston TX USA

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Many scientific applications operate on data sets that span hundreds of Gigabytes or even Terabytes in size. Large data sets often use compression to reduce the size of the files. Yet as of today, parallel I/O libraries do not support reading and writing compressed files, necessitating either expensive sequential compression/decompression operations before/after the simulation, or omitting advanced features of parallel I/O libraries, such as collective I/O operations. This paper introduces parallel I/O on compressed data files, discusses the key challenges, requirements, and solutions for supporting compressed data files in MPI I/O, as well as limitations on some MPI I/O operations when using compressed data files. The paper details handling of individual read and write operations of compressed data files, and presents an extension to the two-phase collective I/O algorithm to support data compression. The paper further presents and evaluates an implementation based on the Snappy compression library and the OMPIO parallel I/O framework. The performance evaluation using multiple data sets demonstrate significant performance benefits when using data compression on a parallel BeeGFS file system.

关键词： Libraries Writing compression algorithms Layout Data compression Matrix decomposition Tools

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Zero Block Caching for CNN Applications Running on a Vision DSP

Zero Block Caching for CNN Applications Running on a Vision ...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Sangheon Lee Wontae Kim Hyuk-Jae Lee Kyujoong Lee Department of Electrical Engineering and Computer Science Inter-university Semiconductor Research Center (ISRC) Seoul National University Seoul Korea Department of Electronics Engineering Sunmoon University Asan Korea

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Digital Signal Processors (DSPs) nowadays are widely used for edge devices due to their power-efficiency in inference operations of Convolutional neural networks (CNNs). In most DSP systems, the ratio of zero-valued feature map data in a CNN is very high due to fixed-point quantization and activation functions like ReLu. This paper presents a new cache for a DSP system, zero block skip (ZBS) cache, to reduce off-chip memory access by caching the addresses of zero blocks in which all data are zero. In addition, a data remapping method is proposed for more zero block accesses to off-chip memory, which successfully increases the portion of zero blocks among all feature data blocks from 21.66% to 3S.7l% in Tiny YOLO network. However, it is observed that a ZBS cache using typical cache structure suffers from frequent capacity misses in some cases. To relieve the frequent capacity misses, zero block skip vector (ZBSV) for a group of blocks, each bit of which indicates whether a block is zero or not, is proposed. A ZBSV cache keeps the ZBSV entry for the currently accessed group. With DDR3 as an off-chip memory model, experiments show that four 2 KB ZBSV caches operate together effectively, with relatively lower SRAM power consumption. As a result, the proposed ZBSV cache reduces the off-chip memory access by 19.15% and 15.87% for Tiny YOLO and VDSR networks, respectively.

关键词： Random access memory Quantization (signal) Indexes compression algorithms Registers Power demand Neural networks

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Pattern Matching Based algorithms for Graph compression

Pattern Matching Based Algorithms for Graph Compression

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IEEE International Conference on Research in Computational Intelligence and Communication Networks

作者： Amlan Chatterjee Rushabh Jitendrakumar Shah Soumya Sen Department of Computer Science California State University Dominguez Hills A.K. Choudhury School of Information Technology University of Calcutta

ISBN: (纸本)9781538676394;9781538676387

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of Big Data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, we study different graph compression techniques and propose novel algorithms to do the same in this paper. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and |V| = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to the adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.

关键词： Graph compression Patterns Quadtree Big data compression compression algorithms

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Parallelization of Variable Rate Decompression through Metadata

Parallelization of Variable Rate Decompression through Metad...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Lennart Noordsij Steven van der Vlugt Mohamed A. Bamakhrama Zaid Al-Ars Peter Lindstrom ASML Netherlands Synopsys Corporation Netherlands TU Delft Netherlands Lawrence Livermore National Laboratory United States of America

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. This allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.

关键词： Decoding Metadata compression algorithms Encoding Data transfer Acceleration Multicore processing

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