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A Key Frame Selection Algorithm Based on Sliding Window and Image Features

A Key Frame Selection Algorithm Based on Sliding Window and ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Jigang Cao Linchen Yu Mianlong Chen Xinyue Cui School of Computer China University of Geosciences Wuhan

ISBN: (纸本)9781509053827

Network traffic associated with video increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications related with video, such as video search, video fast browsing, video index and storage are in great demand. Behind these technologies and applications, a core problem is how to quickly browse massive video data and obtain the main content of the video. To solve this problem, different key frame extraction algorithms have been proposed. Due to the diversity of video content, different video have different characteristics. So the design of general video key frame extraction algorithm to solve the problem is not the reality. The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself. In this article, we mainly focus on videos with edited boundaries and shot conversions. Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF. In this algorithm, we use Gist feature to construct the global scene information of frames, and the SURF keypoint detection algorithm to extract local keypoints as local feature for each frame. Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots. After that, we select the most representative frames in each video shot as key frames. Finally we evaluate the result of the algorithm from the subjective and objective perspective. Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video.

关键词： Feature extraction Algorithm design and analysis Signal processing algorithms Clustering algorithms Detection algorithms Image color analysis Visualization

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Joint Hybrid Frequent Value Cache and Multi-Coding for Data Bus Energy Saving

Joint Hybrid Frequent Value Cache and Multi-Coding for Data ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Mingquan Zhang Zhihua Gan Jizan Zhang Zhimin Gu Department of computer North China Electric Power University Baoding China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781509053827

In the deep submicron technology domain, the on-chip buses consume considerable amount of total energy of embedded multi-core chip. Lots of techniques have been produced to reduce the bus energy consumption. FVE (Frequent Value Encoding) and FV-MSB (Frequent Value-Most Significant Bit) which exploit abundant value locality on the data buses, are effective methods for reducing data bus energy consumption. In this paper, we propose a method that exploits more value locality that is overlooked by the FVE and FV-MSB. We found that a significant amount of non-frequent values and low-order bits of partial frequent values, not captured by the FVE and FV-MSB, produced large number of switching activity. Therefore, we produce an bus energy saving method based on frequent values and multi-coding which can be used to further reduce the on-chip data bus switching activity. The simulation results show that our method can reduce the ratio of switching activity by 18.7% on the data bus lines, and obtain the maximum ratio of energy saving by 17.76% and the average ratio about 16.91%, with 70nm technology when the coupling factor λ is 5. And the results also show that the method can still play a role when the technology size is further reduced in the future.

关键词： Energy consumption Encoding Couplings System-on-chip Multicore processing Capacitance Switches

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Stochastic logics for low cost and low power wearable applications

Stochastic logics for low cost and low power wearable applic...

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China Semiconductor Technology international conference (CSTIC)

作者： Yuan Ji Wendong Chen Feng Ran School of Mechatronic Engineering and Automation Shanghai University Shanghai China Microelectronic Research and Development Center Shanghai University Shanghai China

This paper gives a special insight standing on an unusual computing algorithm - stochastic computing - to discuss the strategy on designing low cost and low power smart wearable devices from the system architecture view. Some new stochastic logics are introduced to approximate complex mathematic functions like the Gaussian function with simple circuits, which are used to build the low cost and low power artificial neural network for data intensive applications for the smart wearable devices. The data process kernel is distributed close to the data source. An experiment on the facial recognition shows how the system complexity, speed, cost, power, and flexibility can be improved by the stochastic logic architecture.

关键词： Neurons Biological neural networks Program processors Face recognition Hardware Computer architecture parallel processing

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Comparative Studies of Sampling for Analytics on Massive Data

Comparative Studies of Sampling for Analytics on Massive Dat...

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international conference on Systems and Informatics

作者： Xuan Zhang Dongsheng Li National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781509055227

Groupwise analytics on big data have been widely used in statistics, computer science, parallel computing and many other fields in recent years. At The same time, Aggregation queries is one of the most important analytics techniques. In big data eras, the aggregation queries on the ever-increasing data volumes will consumes much time, the traditional methods of traversing the entire dataset is not acceptable to users. Data sampling is a technique that only process a part of data to get an approximate result, the technique can save a lot of time when dealing with a vast amount of data with the sacrifice of accuracy. This paper will introduce several data sampling algorithms for approximate aggregation queries for big data, and analyze the shortcomings and advantages of each methods. Including the technique apply to the sparse data which meaning data has a limited population but a wide range.

关键词： Groupwise analytics Aggregation Approximate result Data sampling Sparse data

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Real time streaming data storage and processing using storm and analytics with Hive

Real time streaming data storage and processing using storm ...

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international conference on Advanced Communication, Control and Computing Technologies (ICACCCT)

作者： D. Surekha G. Swamy S Venkatramaphanikumar Department of CSE VFSTR University Vadlamudi BigData Practice Lead Anblicks Solutions Guntur Andhra Pradesh India

ISBN: (纸本)9781467395465

In big data world, Hadoop distributed File System (HDFS) is one of the famous file system to store huge data. HDFS will take care about managing and maintaining the data in distributed way. Based on research we did to discuss that how the real time streaming data can be processed and stored into Mongo DB and Hive. Big data analytics can be performed on data stored on Hadoop distributed file system using Apache Hive, Tez and Apache Presto. Hive is an ecosystem which is on top of Hadoop (MapReduce), and provides higher-level language to use Hadoop's core component MapReduce to process the data. The key benefits of this approach are it can able to store and process the large amount of data. It can also handle the millions of user requests concurrently. It can provide the scalability for the system is enhanced by adding new nodes. Integrating the Visualization tools with Big Data applications will give the big picture to the users to view the insights of the Big data. It can provide the analytic reports for giving the big picture about the system.

关键词： Storms Fasteners Real-time systems parallel processing Big data Topology File systems

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Selectively GPU Cache Bypassing for Un-Coalesced Loads

Selectively GPU Cache Bypassing for Un-Coalesced Loads

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international conference on parallel and distributed Systems (ICPADS)

作者： Chen Zhao Fei Wang Zhen Lin Huiyang Zhou Nanning Zheng Institute of Artificial Intelligence and Robotics Xi'an JiaoTong University Xi'an China

ISBN: (纸本)9781509053827

GPUs are widely used to accelerate general purpose applications, and could hide memory latency through massive multithreading. But multithreading can increase contention for the L1 data caches (L1D). This problem is exacerbated when an application contains irregular memory references which would lead to un-coalesced memory accesses. In this paper, we propose a simple yet effective GPU cache Bypassing scheme for Un-Coalesced Loads (BUCL). BUCL makes bypassing decisions at two granularities. At the instruction-level, when the number of memory accesses generated by a non-coalesced load instruction is bigger than a threshold, referred as the threshold of un-coalescing degree (TUCD), all the accesses generated from this load will bypass L1D. The reason is that the cache data filled by un-coalesced loads typically have low probabilities to be reused. At the level of each individual memory access, when the L1D is stalled, the accessed data is likely with low locality, and the utilization of the target memory sub-partition is not high, this memory access may also bypass L1D. Our experiments show that BUCL achieves 36% and 5% performance improvement over the baseline GPU for memory un-coalesced and memory coherent benchmarks, respectively, and also significantly outperforms prior GPU cache bypassing and warp throttling schemes.

关键词： Graphics processing units Instruction sets Benchmark testing Multithreading Computer architecture Hardware Kernel

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Cruncher: distributed In-Memory processing for Location-Based Services

Cruncher: Distributed In-Memory Processing for Location-Base...

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IEEE international conference on Data Engineering

作者： Ahmed S. Abdelhamid Mingjie Tang Ahmed M. Aly Ahmed R. Mahmood Thamir Qadah Walid G. Aref Saleh Basalamah Purdue University Umm Al-Qura University

ISBN: (纸本)9781509020218

Advances in location-based services (LBS) demand high-throughput processing of both static and streaming data. Recently, many systems have been introduced to support distributed main-memory processing to maximize the query throughput. However, these systems are not optimized for spatial data processing. In this demonstration, we showcase Cruncher, a distributed main-memory spatial data warehouse and streaming system. Cruncher extends Spark with adaptive query processing techniques for spatial data. Cruncher uses dynamic batch processing to distribute the queries and the data streams over commodity hardware according to an adaptive partitioning scheme. The batching technique also groups and orders the overlapping spatial queries to enable inter-query optimization. Both the data streams and the offline data share the same partitioning strategy that allows for data co-locality optimization. Furthermore, Cruncher uses an adaptive caching strategy to maintain the frequently-used location data in main memory. Cruncher maintains operational statistics to optimize query processing, data partitioning, and caching at runtime. We demonstrate two LBS applications over Cruncher using real datasets from OpenStreetMap and two synthetic data streams. We demonstrate that Cruncher achieves order(s) of magnitude throughput improvement over Spark when processing spatial data.

关键词： spatial data Location based services data flow query analysis Cache memory Distribute Data partitioning Batch processing rivers streams

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A Memory Access Pattern-Based Program Profiling System for Dynamic parallelism Prediction

A Memory Access Pattern-Based Program Profiling System for D...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications

作者： Zijun Han Guangzhi Qu Dona Burkard Kelvin Dobbins Comput. Sci. & Eng. Dept. Oakland Univ. Rochester MI USA Res. & Innovation Center Ford Motor Co. Dearbon MI USA

ISBN: (纸本)9781509032068

parallel computing is the simultaneous use of multiple computer resources to solve a computational problem. In order to automatically execute a sequential program so that inherent parallelism could utilize the underlying multi-core platforms, program profiling is necessary to learn the control and dataflow dependency which is critical for predicting the parallelization feasibility and finding parallelism solutions. In literature, algorithms and tools have been designed for either dynamic or static program analysis in order to increase the accuracy and performance in discovering all aspects of the target program. However, memory and runtime overhead remain two challenge problems for program profiling. In this work, based on the PIN framework, we have designed and implemented a memory access pattern-based program profiling system for sequential program parallelization prediction. Validation results on seven SPEC 2006 benchmark programs show the effectiveness of proposed system on both memory usage and cpu time for target program dynamic profiling.

关键词： parallel processing History Memory management Engines Runtime Pins Heuristic algorithms parallel processing (COMPUTERS) Runtime PIN Store management Heuristic algorithms Engines Bone Nails analysis programme sequential program Synclitism parallelism object programs Memory Memory CPU time

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Massed Refresh: An Energy-Efficient Technique to Reduce Refresh Overhead in Hybrid Memory Cube Architectures

Massed Refresh: An Energy-Efficient Technique to Reduce Refr...

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international conference on VLSI Design

作者： Ishan G. Thakkar Sudeep Pasricha Department of Electrical and Computer Engineering Colorado State University Fort Collins CO U.S.A

ISBN: (纸本)9781467387019

This paper presents a novel, energy-efficient DRAM refresh technique called massed refresh that simultaneously leverages bank-level and sub array-level concurrency to reduce the overhead of distributed refresh operations in the Hybrid Memory Cube (HMC). In massed refresh, a bundle of DRAM rows in a refresh operation is composed of two subgroups mapped to two different banks, with the rows of each subgroup mapped to different sub arrays within the corresponding bank. Both subgroups of DRAM rows are refreshed concurrently during a refresh command, which greatly reduces the refresh cycle time and improves bandwidth and energy efficiency of the HMC. Our experimental analysis shows that the proposed massed refresh technique achieves up to 6.3% and 5.8% improvements in throughput and energy-delay product on average over JEDEC standardized distributed per-bank refresh and state-of-the-art scattered refresh techniques.

关键词： Random access memory Memory management Energy efficiency parallel processing Density measurement Power system measurements Noise level

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Performance Measurement on Scale-Up and Scale-Out Hadoop with Remote and Local File Systems

Performance Measurement on Scale-Up and Scale-Out Hadoop wit...

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IEEE international conference on Cloud Computing, CLOUD

作者： Zhuozhao Li Haiying Shen Department of Electrical and Computer Engineering Clemson University Clemson SC

MapReduce is a popular computing model for parallel data processing on large-scale datasets, which can vary from gigabytes to terabytes and petabytes. Though Hadoop MapReduce normally uses Hadoop distributed File System (HDFS) local file system, it can be configured to use a remote file system. Then, an interesting question is raised: for a given application, which is the best running platform among the different combinations of scale-up and scale-out Hadoop with remote and local file systems. However, there has been no previous research on how different types of applications (e.g., CPU-intensive, data-intensive) with different characteristics (e.g., input data size) can benefit from the different platforms. Thus, in this paper, we conduct a comprehensive performance measurement of different applications on scale-up and scaleout clusters configured with HDFS and a remote file system (i.e., OFS), respectively. We identify and study how different job characteristics (e.g., input data size, the number of file reads/writes, and the amount of computations) affect the performance of different applications on the different platforms. This study is expected to provide a guidance for users to choose the best platform to run different applications with different characteristics in the environment that provides both remote and local storage, such as HPC cluster.

关键词： Random access memory Measurement File systems Program processors distributed databases Servers Computer architecture

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