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FastDRC: Fast and Scalable Genome Compression Based on Distributed and parallel processing 19th

FastDRC: Fast and Scalable Genome Compression Based on Distr...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ji, Yimu Fang, Houzhi Yao, Haichang He, Jing Chen, Shuai Li, Kui Liu, Shangdong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Nanjing Inst Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst High Performance Comp & Big Data Nanjing 210003 Peoples R China Nanjing Ctr HPC China Nanjing 210003 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. this manuscript proposes a parallel and distributed referential genome compression algorithm-Fast Distributed Referential Compression (FastDRC). this algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. the source code for FastDRC can be obtained from this link: https://***/GhostCCCathenry/FastDRC.

关键词： Genome compression Distributed processing Apache Hadoop FASTA

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Machine Learning Migration for Efficient Near-Data processing

Machine Learning Migration for Efficient Near-Data Processin...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Aline S. Cordeiro Sairo R. dos Santos Francis B. Moreira Paulo C. Santos Luigi Carro Marco A. Z. Alves Federal University of Paraná Curitiba Brazil Federal Rural University of Semi-arid Angicos Brazil Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil

Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its high impact an time and energy consumption. In this context, the Near-Data processing (NDP) architectures emerged as a prominent solution to increasing data by drastically reducing the required amount of data movement For NDP, we see three main approaches, Application-Specific Integrated Circuits (ASICs), full Central processing Units (CPUs) and Graphics processing Units (GPIIs), or vector units integration. However, previous work considered only ASICs, CPUs and GPUs when executing ML algorithms inside the memory. In this paper, we present an approach to execute ML algorithms near-data, using a general-purpose vector architecture and applying near-data parallelism to kernels from KNN, MEP, and CNN algorithms. To facilitate this process, we also present an NDP intrinsics library to ease the evaluation and debugging tasks. Our results show speedups up to fox for KNN, 11× for MLP, and 3× for convolution when processing near-data compared to a high-performance ×86 baseline.

关键词： Integrated circuits Machine learning algorithms Graphics processing units Machine learning Computer architecture Tools parallel processing

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Factors on the Execution Times of Metropolis Resampling and its Variations 5

Factors on the Execution Times of Metropolis Resampling and ...

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5th international conference on Computer Science and Engineering (UBMK)

作者： Dulger, Ozcan Ogurtuzun, Halit Middle East Tech Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781728175652

Particle filter is a serial Monte-Carlo estimation method. It is suitable for the applications whose system or measurement model is highly non-linear and uncertainties are large. the standard particle filter encounters degeneracy problem. One of the important solution is resampling. the most common resampling method is Systematic resampling. It requires collective operations over the weights. this causes numerical instability problem for Systematic resampling. Furthermore, interaction between all weights causes less-readily parallelization of Systematic resampling on graphics processing unit (GPU). To overcome these problems, Metropolis resampling is proposed. Since it only uses ratio of two weights rather than collective operations, it does not suffer from numerical instability and it is more suitable for the parallel implementation of it on GPU. Although it is fast in theory, it suffers from non-coalesced global memory access patterns when implemented on CPU. Metropolis-C1 and Metropolis-C2 are proposed to overcome this problem. In this study, we investigate the factors on the execution times of Metropolis, Metropolis-C1 and Metropolis-C2. there are mainly three factors. these are physical resources and limitations of the GPU, non-coalesced global memory access patterns and the number of particles. We discuss how they affect the execution times of these resampling algorithms in detail.

关键词： particle filter Metropolis resampling Metropolis-C1 resampling Metropolis-C2 resampling non-coalesced global memory access pattern graphics processing unit

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Proceedings of the 34th ACM international conference on Supercomputing, ICS 2020

Proceedings of the 34th ACM International Conference on Supe...

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34th ACM international conference on Supercomputing, ICS 2020

ISBN: (纸本)9781450379830

the proceedings contain 40 papers. the topics discussed include: a scalable framework for solving fractional diffusion equations;fast distributed bandits for online recommendation systems;wavefront parallelization of recurrent neural networks on multi-core architectures;NV-group: link-efficient reduction for distributed deep learning on modern dense GPU systems;a coordinate-oblivious index for high-dimensional distance similarity searches on the GPU;V-combiner: speeding-up iterative graph processing on a shared-memory platform with vertex merging;efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs;parallelizing pruned landmark labeling: dealing with dependencies in graph algorithms;Graptor: efficient pull and push style vectorized graph processing;and SB-fetch: synchronization aware hardware prefetching for chip multiprocessors.

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A novel parallel hybrid model for forecasting the stock price 22

A novel parallel hybrid model for forecasting the stock pric...

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Proceedings of the 2022 5th international conference on Machine Learning and Natural Language processing

作者： Chengdong Zhu Zhenyang Xu Lianfeng Han China University of mining and technology China

ISBN: (纸本)9781450399067

In recent years, in addition to the growth in the number of investors in the stock market, there has been a growing interest in predicting stock prices. Accurate stock prices can effectively improve investment returns on the premise of reducing investment risks for stock investors. therefore, this study presents a hybrid Bo-LSTM-SVR model to predict the next day's stock closing price. Firstly, the hyper-parameters of LSTM and SVR as well as the length of their respective sliding windows are optimized by the Bayesian optimization method, so as to obtain more accurate predicted values of the single models. the genetic algorithm is then adopted every day to decide the weight of the two single models, and finally, the combined predicted values are obtained. In order to ensure that the prediction of the proposed model is more accurate, this model and the other six models are applied to predict the closing prices of the Shanghai Composite Index on the next trading day. the results reveal that the model proposed in this study is the most accurate, with the smallest MAE and RMSE as well as the largest R^2. Compared with other models, the proposed model is more suitable for stock price prediction, which provides a dependable tool for investors to make stock investment decisions.

关键词： hybrid model SVR LSTM Bayesian optimization stock price prediction

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OpenCLTM Implementation of Fast Frequency Domain Image Denoisification Kernels for parallel Environments 5th

OpenCLTM Implementation of Fast Frequency Domain Image Denoi...

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5th international conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2019

作者： Satapathy, Ashutosh Jenila Livingston, L.M. School of Computing Science and Engineering Vellore Institute of Technology Vellore Chennai600127 India

ISBN: (纸本)9789811574856

From the last couple of decades, image denoisification is one of the challenging areas in the image processing and computer vision domains that adds clarity to images by removing noise and makes them suitable for further processing. Images or videos captured by various visual sensors are degenerated during various circumstances such as sensor temperature, light intensity, target environment, erroneous electric instruments, and interference in the communication channels and required to be enhanced by various filtering techniques. Most of the real-world applications like student monitoring, vigilance system, traffic analysis, target detection, and classification use image smoothing to reduce artifacts present in the captured images and additionally demand its quicker execution. In this paper, we have explained different types of noise models normally degrade the quality of an image and the three most popular frequency domain smoothing filters (ideal, Gaussian, and Butterworth) are available to remove those noises. In addition to that, OpenCL kernels of those filters have been implemented for rapid denoisification and can be easily portable on wide verities of parallel devices. At last, performance evaluation has been carried out for both the CPU and GPU implementations to seek out their productiveness on those noise models in terms of time and precision. Various performance assessment metrics like entropy, root-mean-squared error, peak signal-to-noise ratio, and mean absolute error are applied to determine the rightness of the above filters. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Low pass filters

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Multi-scaled Non-local Means parallel Filters for Medical Image Denoising 20th

Multi-scaled Non-local Means Parallel Filters for Medical Im...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Rabbouch, Hana Ben Messaoud, Othman Saadaoui, Foued Fac Sci Lab LR18ES15 Algebre Theorie Nombres & Anal Nonli Monastir 5019 Tunisia Compagnie Phosphates Gafsa CPG Cite Bayech 2100 Gafsa Tunisia King Abdulaziz Univ POB 80203 Jeddah 21589 Saudi Arabia

ISBN: (纸本)9783030602451;9783030602444

In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. the extraordinary development of the denoising area is no doubt due to the ever expanding and successful computing technology, but also to the emergence of the multi-resolution analysis (MRA) on both mathematical and algorithmic levels. However, many denoising techniques still remain ineffective in dealing with certain types of noise. Other methods can be too expensive, given their nested and complicated structure. therefore, in this paper, A new multi-scale parallel denoising paradigm is defined and tested. A comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). the comparison is carried out in this framework of multi-scaled parallel denoising, where a Non-Local Means (NLM) filter is implemented and adjusted scale-by-scale to a sample of X-ray benchmark images. Some state-of-the-art denoising methods were also used in the evaluation. the numerical results proved the effectiveness of the multi-scaled parallel denoising in terms of accuracy and speed of convergence, especially when the NLM filtering is coupled with the EMD. this shows a bright future for their medical use in the next few years.

关键词： Medical imaging Non-local means Multi-scaled denoising Empirical mode decomposition Wavelets

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Research on parallel System for Motion States Monitoring of the Planetary Rover 5

Research on Parallel System for Motion States Monitoring of ...

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5th international conference on Communication, Image and Signal processing, CCISP 2020

作者： Shuai, Sun Lei, Wang Zhiping, Li Peng, Gu Feifei, Chen Yuting, Feng Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781728185897

In the course of patrol and exploration on extraterrestrial celestial bodies, it is hard to monitor the state of the planetary rover in real time due to the large communication delay and tight link between the target planet and the earth. In order to solve this problem, a parallel system is developed which simulates the state of the environment variable and the planetary rover simultaneously to realize the real-Time monitoring of the rover. the test and analysis of the system is also introduced in this paper. Based on this system, the level of telemetry data interpretation of planetary rover can be effectively improved to ensure the safety of planetary rover in the process of surface detection of extraterrestrial bodies. © 2020 IEEE.

关键词： Earth (planet)

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CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous parallel Variants 20th

CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochas...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Peng, Yaqiong Fei, Haiqiang Li, Lun Ding, Zhenquan Hao, Zhiyu Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. the unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. the experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

关键词： Machine learning Stochastic gradient descent Streaming data parallel algorithm Multicore

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parallel Fast DOA Estimation Algorithm Based on SML and Membrane Computing 17th

Parallel Fast DOA Estimation Algorithm Based on SML and Memb...

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17th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2020

作者： Bai, Xiaofeng Song, Huajun Xiang, Changbo College of Oceanography and Space Informatics China University of Petroleum Qingdao China The 41st Research Institute of China Electronic Science and Technology Group Corporation Qingdao China

ISBN: (纸本)9783030794774

Direction of arrival (DOA) is widely used in communication, biomedicine, and other fields. Stochastic maximum likelihood (SML) algorithm is an excellent direction of arrival (DOA) estimation algorithm. However, the extremely heavy computational complexity in the process of SML analysis restricts its application in practical systems. Aiming at this problem of SML, this paper proposes a parallel accelerating algorithm of membrane computing (MC), particle swarm optimization (PSO), and artificial bee colony (ABC). Firstly, the solution space of SML algorithm is divided into basic membrane and surface membrane by using membrane computing;then particle swarm optimization algorithm is used for local parallel optimization in each basic membrane, and the locally optimal solution is transferred to the surface membrane;finally, the artificial bee colony algorithm is used to find the global optimum in the surface membrane. the results of the experiment show that the proposed algorithm greatly reduces the analytical complexity of SML, and the calculation time is decreased by more than 5 times compared with the commonly used optimization algorithms such as GA, AM, and PSO. © 2021, IFIP international Federation for Information processing.

关键词： Direction of arrival

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