In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in a...
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ISBN:
(纸本)9781450340212
In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in areas such as disaster detection, business analytics or health-care. parallel Complex Event processing has evolved as the paradigm of choice to analyze data streams in a timely manner, where the incoming data streams are split and processed independently by parallel operator instances. However, the degree of parallelism is limited by the feasibility of splitting the data streams into independent parts such that correctness of event processing is still ensured. In this paper, we overcome this limitation for graph-structured data by further parallelizing individual operator instances using modern graph processing systems. these systems partition the graph data and execute graph algorithms in a highly parallel fashion, for instance using cloud resources. To this end, we propose a novel graph-based Complex Event processing system GraphCEP and evaluate its performance in the setting of two case studies from the DEBS Grand Challenge 2016.
Magnetic Resonance Imaging (MRI) system in recent times demands a high rate of acceleration in data acquisition to reduce the scanning time. the data acquisition rate can be accelerated to a significant order through ...
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Withthe rapid advances in Internet technology, publishing real-time statistics data, in a privacy-preserving way, has led to a large body of research. the current state-of-the-art paradigm for privacy preserving with...
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ISBN:
(数字)9783319491783
ISBN:
(纸本)9783319491783;9783319491776
Withthe rapid advances in Internet technology, publishing real-time statistics data, in a privacy-preserving way, has led to a large body of research. the current state-of-the-art paradigm for privacy preserving with differential privacy on data stream is w-event privacy. But it neglects if only a few part of the elements of dataset change over time and others are substantially stabilize, then processing all the user data in specified timestamps will bring additional noise and reduce the utility of data. In this paper, a novel privacy preserving approach called G-event which follow the conventional use of w-event differential privacy is proposed. We group the statistics result at each timestamp based on difference calculation. then the high difference group will publish more often than the similar group. We guarantee that all result with greater change will publish by adding noise, and the result with smaller change will be approximate withthe corresponding lastly published statistics. Experiment using real-life dataset show that our approach improves the utility of data.
It is a trend now that computing power through parallelism is provided by multi-core systems or heterogeneous architectures for High Performance Computing (HPC) and scientific computing. Although many algorithms have ...
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ISBN:
(纸本)9781509052530
It is a trend now that computing power through parallelism is provided by multi-core systems or heterogeneous architectures for High Performance Computing (HPC) and scientific computing. Although many algorithms have been proposed and implemented using sequential computing, alternative parallel solutions provide more suitable and high performance solutions to the same problems. In this paper, three parallelization strategies are proposed and implemented for a dynamic programming based cloud smoothing application, using both shared memory and non-shared memory approaches. the experiments are performed on NVIDIA GeForce GT750m and Tesla K20m, two GPU accelerators of Kepler architecture. Detailed performance analysis is presented on partition granularity at block and thread levels, memory access efficiency and computational complexity. the evaluations described show high approximation of results with high efficiency in the parallel implementations, and these strategies can be adopted in similar data analysis and processing applications.
Computer hardware is currently moving towards heavily parallelized architectures with multiprocessors, multicore and chip multithreaded designs. Cache memory, the fastest component of the memory hierarchy, adapts to t...
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ISBN:
(纸本)9781509039005
Computer hardware is currently moving towards heavily parallelized architectures with multiprocessors, multicore and chip multithreaded designs. Cache memory, the fastest component of the memory hierarchy, adapts to this new kind of parallel systems in order to provide the promised performance increase. Current cache designs have limitations that can be transformed into optimization opportunities both in hardware and software. this paper provides a detailed research of cache performance in multicore processors, considering critical hardware aspects. A new solution is proposed to improve the current performance: an optimized replacement policy for the shared cache level. From experiments run on four and eight core setups in a multicore simulator, the proposed enhancements achieve up to 30% execution speed increase over the default setup.
In this article, an efficient parallel algorithm for a hybrid CPU-GPU platform is proposed to enable large-scale molecular dynamics (MD) simulations of the metal solidification process. the results, implemented the pa...
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ISBN:
(纸本)9781509040940
In this article, an efficient parallel algorithm for a hybrid CPU-GPU platform is proposed to enable large-scale molecular dynamics (MD) simulations of the metal solidification process. the results, implemented the parallel algorithm program on the hybrid CPU-GPU platform shows better performance than the program based on previous algorithms running on the CPU cluster platform. By contrast, the total execution time of the new program has been obviously decreased. Particularly, because of the use of the modified load balancing method, the neighbor list update time is approximately zero. the parallel program based on the CUDA+OpenMP model shows a factor of 6 16-core calculation speedups compared to the parallel program based on the MPI+OpenMP model, and the optimal computational efficiency is achieved in the simulation system including 10,000,000 aluminum atoms. Finally, the good consistency between them verifies the correctness of the algorithm efficiently, by comparison of the theoretical results and experimental results.
Stereo matching techniques aim at reconstructing disparity maps from a pair of images. the use of stereo matching techniques in embedded systems is very challenging due to the complexity of the state-of-the-art algori...
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Stereo matching techniques aim at reconstructing disparity maps from a pair of images. the use of stereo matching techniques in embedded systems is very challenging due to the complexity of the state-of-the-art algorithms. Local stereo matching algorithms are efficiently implemented on GPU and DSP. this paper presents the optimization of the One Dimension Belief Propagation (BP-1D) algorithm. BP-1D is faster than previous algorithms on monocore DSP and its implementation onto multicore DSPs is straightforward. BP-1D implemented on multicore embedded platforms out-performs previous stereo matching implementations reaching real-time performances for resolutions up to 1080p with a 10 Watts power consumption.
Health care is the hardest real time constrained domain. Queuing system, Delay in treatment, Difficult to treat rural people, Disability to do remote treatment are the major issues in current health care system. Inter...
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ISBN:
(纸本)9781467378086
Health care is the hardest real time constrained domain. Queuing system, Delay in treatment, Difficult to treat rural people, Disability to do remote treatment are the major issues in current health care system. Internet of things (IoT) allows caring of people from remote locations withthe help of integration of wireless sensor network with internet. Different sensors are used to measure different health parameters. processing of smart health care data in an efficient manner is necessary. Slight time variation causes severe effect such as loss of life. In IoT, delay occur in processing large volume of sensor data in real time. Energy spent by the sensors also affected by processing delay. Because sensors spent energy in idle state. To reduce delay in processing smart health care data, Multi core technology is included with IoT. SixLoWPAN is the technique used to connect low configured devices with internet. In this paper, Task Level parallelism (TLP) is applied to process different health parameters in parallel. TLP utilizes the available resources in optimal way. It makes our system as more efficient. the proposed system improves performance upto 65.5%. Efficient system also reduces power consumption of the devices. this will increase the life time of the sensor network.
Withthe social networks getting increasingly larger, fast community detection algorithms like the label propagation algorithm, are attracting more attention. But the label propagation algorithm deals vertices with no...
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Programming FPGAs has been an arduous task that requires extensive knowledge of hardware design languages (HDLs), such as Verilog or VHDL, and low-level hardware details. With OpenCL support for FPGAs, the design, pro...
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ISBN:
(纸本)9781509015047
Programming FPGAs has been an arduous task that requires extensive knowledge of hardware design languages (HDLs), such as Verilog or VHDL, and low-level hardware details. With OpenCL support for FPGAs, the design, prototyping and implementation of an FPGA is increasingly moving towards a much higher level of abstraction, when compared to the intrinsically low-level nature of HDLs. On the other hand, in the context of traditional (i.e., CPU) software development, OpenCL is still considered to be low-level and complex because the programmer needs to manually expose parallelism in the code. In this work, we present our approach to enhancing FPGA programmability via GLAF, a visual programming framework, to automatically generate synthesizable OpenCL code with an array of FPGA-specific optimizations. We find that our tool facilitates the development process and produces functionally correct and well-performing code on the FPGA for our molecular modeling, gene sequence search, and filtering algorithms.
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